Alison Callahan is an Instructor and Clinical Data Scientist in the Center for Biomedical Informatics. In collaboration with Nigam Shah's group, her work involves research and development of informatics methods for the analysis of biomedical and clinical data to derive insights and inform medical decision making. Her current research focuses on using informatics to expand and improve the data available about pregnancy and birth, including developing an obstetric database from Stanford Health Care EHRs.
Alison completed her PhD in the Department of Biology at Carleton University in Ottawa, Canada. Her doctoral research focused on developing HyQue, a framework for representing and evaluating scientific hypotheses, and applying this framework to discover genes related to aging. She was also a developer for Bio2RDF, an open-source project to build and provide the largest network of Linked Data for the life sciences. Her postdoctoral work at Stanford applied methodologies developed during her PhD to study spinal cord injury in model organisms and humans in a collaboration with scientists at the University of Miami.
Instructor, Medicine - Biomedical Informatics Research
Boards, Advisory Committees, Professional Organizations
Member, American Medical Informatics Association (2014 - Present)
Doctor of Philosophy, Biology, Carleton University, Ottawa, Canada (2014)
Master of Information Studies, University of Toronto, Toronto, Canada (2009)
Bachelor of Science, Carleton University, Ottawa, Canada (2007)
Nigam H. Shah, Vladimir Polony, Juan Manuel Banda, Alison Callahan. "United States Patent 17/654,569 Systems and Methods for Cohort Analysis Using Compressed Data Objects Enabling Fast Memory Lookups", The Board of Trustees of the Leland Stanford Junior University, Dec 22, 2021
Using aggregate patient data at the bedside via an on-demand consultation service
NEJM Catalyst Innovations in Care Delivery
2022; 2 (10)
View details for DOI 10.1056/CAT.21.0224
Ontology-driven weak supervision for clinical entity classification in electronic health records.
2021; 12 (1): 2017
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.
View details for DOI 10.1038/s41467-021-22328-4
View details for PubMedID 33795682
ACE: the Advanced Cohort Engine for searching longitudinal patient records.
Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.MATERIALS AND METHODS: The Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE's temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI.RESULTS: ACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases.DISCUSSION: ACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden.CONCLUSION: ACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.
View details for DOI 10.1093/jamia/ocab027
View details for PubMedID 33712854
Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries.
JAMA network open
2021; 4 (3): e211728
Importance: Implant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry.Objectives: To quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States.Design, Setting, and Participants: Retrospective cohort study of a large EHR of veterans who had 45 351 total hip arthroplasty procedures in Veterans Health Administration hospitals from 2000 to 2017. Data analysis was performed from January 1, 2000, to December 31, 2017.Exposures: Total hip arthroplasty.Main Outcomes and Measures: Number of total hip arthroplasty procedures extracted from the EHR, trends in implant use, and relative survivorship of implants.Results: A total of 45 351 total hip arthroplasty procedures were identified from 2000 to 2017 with 192 805 implant parts. Data completeness improved over the time. After 2014, 85% of prosthetic heads, 91% of shells, 81% of stems, and 85% of liners used in the Veterans Health Administration health care system were identified by part number. Revision burden and trends in metal vs ceramic prosthetic femoral head use were found to reflect data from the American Joint Replacement Registry. Recalled implants were obvious negative outliers in implant survivorship using Kaplan-Meier curves.Conclusions and Relevance: Although loss to follow-up remains a challenge that requires additional attention to improve the quantitative nature of calculated implant survivorship, we conclude that data collected during routine clinical care and stored in the EHR of a large health system over 18 years were sufficient to provide clinically meaningful data on trends in implant use and to identify poor implants that were subsequently recalled. This automated approach was low cost and had no reporting burden. This low-cost, low-overhead method to assess implant use and performance within a large health care setting may be useful to internal quality assurance programs and, on a larger scale, to postmarket surveillance of implant performance.
View details for DOI 10.1001/jamanetworkopen.2021.1728
View details for PubMedID 33720372
Association of Systemic Diseases With Surgical Treatment for Obstructive Sleep Apnea Compared With Continuous Positive Airway Pressure.
JAMA otolaryngology-- head & neck surgery
Importance: The efficacy of surgical treatments for obstructive sleep apnea (OSA) is variable when considering only the Apnea Hypopnea Index as the treatment end point. However, only a few studies have shown an association between these procedures and improved clinically relevant outcomes, such as cardiovascular, endocrine, and neurological sequelae of OSA.Objective: To evaluate the association of surgery for OSA with clinically relevant outcomes.Design, Setting, and Participants: This retrospective cohort study used the Truven MarketScan Database from January 1, 2007, to December 31, 2015, to identify all patients diagnosed with OSA who received a prescription of continuous positive airway pressure (CPAP), were 40 to 89 years of age, and had at least 3 years of data on file. Data were analyzed September 19, 2019.Interventions: Soft tissue and skeletal surgical procedures for the treatment of OSA.Main Outcomes and Measures: The occurrence of cardiovascular, neurological, and endocrine complications was compared in patients who received CPAP alone and those who received surgery. High-dimensionality propensity score matching was used to adjust the models for confounders. Kaplan-Meier survival analysis with a log-rank test was used to compare differences in survival curves.Findings: A total of 54 224 patients were identified (33 405 men [61.6%]; mean [SD] age, 55.1 [9.2] years), including a cohort of 49 823 patients who received CPAP prescription alone (mean [SD] age, 55.5 [9.4] years) and 4269 patients who underwent soft tissue surgery (mean [SD] age, 50.3 [7.0] years). The median follow-up time was 4.47 (interquartile range, 3-8) years after the index CPAP prescription. In the unadjusted model, soft tissue surgery was associated with decreased cardiovascular (hazard ratio [HR], 0.92; 95% CI, 0.86-0.98), neurological (HR, 0.49; 95% CI, 0.39-0.61), and endocrine (HR, 0.80; 95% CI, 0.74-0.86) events. This finding was maintained in the adjusted model (HR for cardiovascular events, 0.91 [95% CI, 0.83-1.00]; HR for neurological events, 0.67 [95% CI, 0.51-0.89]; HR for endocrine events, 0.82 [95% CI, 0.74-0.91]). Skeletal surgery (n=114) and concomitant skeletal and soft tissue surgery (n=18) did not demonstrate significant differences in rates of development of systemic complications.Conclusions and Relevance: In this cohort study, soft tissue surgery for OSA was associated with lower rates of development of cardiovascular, neurological, and endocrine systemic complications compared with CPAP prescription in a large convenience sample of the working insured US adult population. These findings suggest that surgery should be part of the early treatment algorithm in patients at high risk of CPAP failure or nonadherence.
View details for DOI 10.1001/jamaoto.2020.5179
View details for PubMedID 33475682
Treatment and Monitoring Variability in US Metastatic Breast Cancer Care.
JCO clinical cancer informatics
2021; 5: 600-614
Treatment and monitoring options for patients with metastatic breast cancer (MBC) are increasing, but little is known about variability in care. We sought to improve understanding of MBC care and its correlates by analyzing real-world claims data using a search engine with a novel query language to enable temporal electronic phenotyping.Using the Advanced Cohort Engine, we identified 6,180 women who met criteria for having estrogen receptor-positive, human epidermal growth factor receptor 2-negative MBC from IBM MarketScan US insurance claims (2007-2014). We characterized treatment, monitoring, and hospice usage, along with clinical and nonclinical factors affecting care.We observed wide variability in treatment modality and monitoring across patients and geography. Most women received first-recorded therapy with endocrine (67%) versus chemotherapy, underwent more computed tomography (CT) (76%) than positron emission tomography-CT, and were monitored using tumor markers (58%). Nearly half (46%) met criteria for aggressive disease, which were associated with receiving chemotherapy first, monitoring primarily with CT, and more frequent imaging. Older age was associated with endocrine therapy first, less frequent imaging, and less use of tumor markers. After controlling for clinical factors, care strategies varied significantly by nonclinical factors (median regional income with first-recorded therapy and imaging type, geographic region with these and with imaging frequency and use of tumor markers; P < .0001).Variability in US MBC care is explained by patient and disease factors and by nonclinical factors such as geographic region, suggesting that treatment decisions are influenced by local practice patterns and/or resources. A search engine designed to express complex electronic phenotypes from longitudinal patient records enables the identification of variability in patient care, helping to define disparities and areas for improvement.
View details for DOI 10.1200/CCI.21.00031
View details for PubMedID 34043432
Trove: Ontology-driven weak supervision for medical entity classification.
Recognizing named entities (NER) and their associated attributes like negation are core tasks in natural language processing. However, manually labeling data for entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications. Weakly supervised learning, which automatically builds imperfect training sets from low cost, less accurate labeling rules, offers a potential solution. Medical ontologies are compelling sources for generating labels, however combining multiple ontologies without ground truth data creates challenges due to label noise introduced by conflicting entity definitions. Key questions remain on the extent to which weakly supervised entity classification can be automated using ontologies, or how much additional task-specific rule engineering is required for state-of-the-art performance. Also unclear is how pre-trained language models, such as BioBERT, improve the ability to generalize from imperfectly labeled data.We present Trove, a framework for weakly supervised entity classification using medical ontologies. We report state-of-the-art, weakly supervised performance on two NER benchmark datasets and establish new baselines for two entity classification tasks in clinical text. We perform within an average of 3.5 F1 points (4.2%) of NER classifiers trained with hand-labeled data. Automatically learning label source accuracies to correct for label noise provided an average improvement of 3.9 F1 points. BioBERT provided an average improvement of 0.9 F1 points. We measure the impact of combining large numbers of ontologies and present a case study on rapidly building classifiers for COVID-19 clinical tasks. Our framework demonstrates how a wide range of medical entity classifiers can be quickly constructed using weak supervision and without requiring manually-labeled training data.
View details for PubMedID 32793768
View details for PubMedCentralID PMC7418750
Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.
Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order.MATERIALS AND METHODS: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed positive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over the 40 days between March 2nd and April 11th 2020.RESULTS: We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems.CONCLUSION: Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.
View details for DOI 10.1093/jamia/ocaa076
View details for PubMedID 32548636
Research and Reporting Considerations for Observational Studies Using Electronic Health Record Data.
Annals of internal medicine
2020; 172 (11_Supplement): S79–S84
Electronic health records (EHRs) are an increasingly important source of real-world health care data for observational research. Analyses of data collected for purposes other than research require careful consideration of data quality as well as the general research and reporting principles relevant to observational studies. The core principles for observational research in general also apply to observational research using EHR data, and these are well addressed in prior literature and guidelines. This article provides additional recommendations for EHR-based research. Considerations unique to EHR-based studies include assessment of the accuracy of computer-executable cohort definitions that can incorporate unstructured data from clinical notes and management of data challenges, such as irregular sampling, missingness, and variation across time and place. Principled application of existing research and reporting guidelines alongside these additional considerations will improve the quality of EHR-based observational studies.
View details for DOI 10.7326/M19-0873
View details for PubMedID 32479175
Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.
BMC medical research methodology
2020; 20 (1): 102
BACKGROUND: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.METHODS: Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites.RESULTS: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.CONCLUSION: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.
View details for DOI 10.1186/s12874-020-00991-3
View details for PubMedID 32375693
FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research.
Journal of neurotrauma
2020; 37 (6): 831-838
Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data.
View details for DOI 10.1089/neu.2019.6674
View details for PubMedID 31608767
View details for PubMedCentralID PMC7071068
Estimating the efficacy of symptom-based screening for COVID-19.
NPJ digital medicine
2020; 3: 95
There is substantial interest in using presenting symptoms to prioritize testing for COVID-19 and establish symptom-based surveillance. However, little is currently known about the specificity of COVID-19 symptoms. To assess the feasibility of symptom-based screening for COVID-19, we used data from tests for common respiratory viruses and SARS-CoV-2 in our health system to measure the ability to correctly classify virus test results based on presenting symptoms. Based on these results, symptom-based screening may not be an effective strategy to identify individuals who should be tested for SARS-CoV-2 infection or to obtain a leading indicator of new COVID-19 cases.
View details for DOI 10.1038/s41746-020-0300-0
View details for PubMedID 32695885
Profiling off-label prescriptions in cancer treatment using social health networks.
2019; 2 (3): 301–5
Objectives: To investigate using patient posts in social media as a resource to profile off-label prescriptions of cancer drugs.Methods: We analyzed patient posts from the Inspire health forums (www.inspire.com) and extracted mentions of cancer drugs from the 14 most active cancer-type specific support groups. To quantify drug-disease associations, we calculated information component scores from the frequency of posts in each cancer-specific group with mentions of a given drug. We evaluated the results against three sources: manual review, Wolters-Kluwer Medi-span, and Truven MarketScan insurance claims.Results: We identified 279 frequently discussed and therefore highly associated drug-disease pairs from Inspire posts. Of these, 96 are FDA approved, 9 are known off-label uses, and 174 do not have records of known usage (potentially novel off-label uses). We achieved a mean average precision of 74.9% in identifying drug-disease pairs with a true indication association from patient posts and found consistent evidence in medical claims records. We achieved a recall of 69.2% in identifying known off-label drug uses (based on Wolters-Kluwer Medi-span) from patient posts.
View details for DOI 10.1093/jamiaopen/ooz025
View details for PubMedID 31709388
It is time to learn from patients like mine.
NPJ digital medicine
2019; 2: 16
Clinicians are often faced with situations where published treatment guidelines do not provide a clear recommendation. In such situations, evidence generated from similar patients' data captured in electronic health records (EHRs) can aid decision making. However, challenges in generating and making such evidence available have prevented its on-demand use to inform patient care. We propose that a specialty consultation service staffed by a team of medical and informatics experts can rapidly summarize 'what happened to patients like mine' using data from the EHR and other health data sources. By emulating a familiar physician workflow, and keeping experts in the loop, such a service can translate physician inquiries about situations with evidence gaps into actionable reports. The demand for and benefits gained from such a consult service will naturally vary by practice type and data robustness. However, we cannot afford to miss the opportunity to use the patient data captured every day via EHR systems to close the evidence gap between available clinical guidelines and realities of clinical practice. We have begun offering such a service to physicians at our academic medical center and believe that such a service should be core offering by clinical informatics professional throughout the country. Only if we launch such efforts broadly can we systematically study the utility of learning from the record of routine clinical practice.
View details for DOI 10.1038/s41746-019-0091-3
View details for PubMedID 31304364
View details for PubMedCentralID PMC6550176
Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection.
JMIR public health and surveillance
2019; 5 (2): e11264
Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs.The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature.We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses.Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal.Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance.
View details for DOI 10.2196/11264
View details for PubMedID 31162134
Medical device surveillance with electronic health records.
NPJ digital medicine
2019; 2: 94
Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.
View details for DOI 10.1038/s41746-019-0168-z
View details for PubMedID 31583282
View details for PubMedCentralID PMC6761113
A Second Opinion From Observational Data on Second-line Diabetes Drugs.
JAMA network open
2018; 1 (8): e186119
View details for PubMedID 30646309
Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.
Statistics in medicine
2018; 37 (23): 3309-3324
There is growing interest in using routinely collected data from health care databases to study the safety and effectiveness of therapies in "real-world" conditions, as it can provide complementary evidence to that of randomized controlled trials. Causal inference from health care databases is challenging because the data are typically noisy, high dimensional, and most importantly, observational. It requires methods that can estimate heterogeneous treatment effects while controlling for confounding in high dimensions. Bayesian additive regression trees, causal forests, causal boosting, and causal multivariate adaptive regression splines are off-the-shelf methods that have shown good performance for estimation of heterogeneous treatment effects in observational studies of continuous outcomes. However, it is not clear how these methods would perform in health care database studies where outcomes are often binary and rare and data structures are complex. In this study, we evaluate these methods in simulation studies that recapitulate key characteristics of comparative effectiveness studies. We focus on the conditional average effect of a binary treatment on a binary outcome using the conditional risk difference as an estimand. To emulate health care database studies, we propose a simulation design where real covariate and treatment assignment data are used and only outcomes are simulated based on nonparametric models of the real outcomes. We apply this design to 4 published observational studies that used records from 2 major health care databases in the United States. Our results suggest that Bayesian additive regression trees and causal boosting consistently provide low bias in conditional risk difference estimates in the context of health care database studies.
View details for DOI 10.1002/sim.7820
View details for PubMedID 29862536
Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative.
JAMA network open
2018; 1 (4): e181755
Consensus around an efficient second-line treatment option for type 2 diabetes (T2D) remains ambiguous. The availability of electronic medical records and insurance claims data, which capture routine medical practice, accessed via the Observational Health Data Sciences and Informatics network presents an opportunity to generate evidence for the effectiveness of second-line treatments.To identify which drug classes among sulfonylureas, dipeptidyl peptidase 4 (DPP-4) inhibitors, and thiazolidinediones are associated with reduced hemoglobin A1c (HbA1c) levels and lower risk of myocardial infarction, kidney disorders, and eye disorders in patients with T2D treated with metformin as a first-line therapy.Three retrospective, propensity-matched, new-user cohort studies with replication across 8 sites were performed from 1975 to 2017. Medical data of 246 558 805 patients from multiple countries from the Observational Health Data Sciences and Informatics (OHDSI) initiative were included and medical data sets were transformed into a unified common data model, with analysis done using open-source analytical tools. Participants included patients with T2D receiving metformin with at least 1 prior HbA1c laboratory test who were then prescribed either sulfonylureas, DPP-4 inhibitors, or thiazolidinediones. Data analysis was conducted from 2015 to 2018.Treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones starting at least 90 days after the initial prescription of metformin.The primary outcome is the first observation of the reduction of HbA1c level to 7% of total hemoglobin or less after prescription of a second-line drug. Secondary outcomes are myocardial infarction, kidney disorder, and eye disorder after prescription of a second-line drug.A total of 246 558 805 patients (126 977 785 women [51.5%]) were analyzed. Effectiveness of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones prescribed after metformin to lower HbA1c level to 7% or less of total hemoglobin remained indistinguishable in patients with T2D. Patients treated with sulfonylureas compared with DPP-4 inhibitors had a small increased consensus hazard ratio of myocardial infarction (1.12; 95% CI, 1.02-1.24) and eye disorders (1.15; 95% CI, 1.11-1.19) in the meta-analysis. Hazard of observing kidney disorders after treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones was equally likely.The examined drug classes did not differ in lowering HbA1c and in hazards of kidney disorders in patients with T2D treated with metformin as a first-line therapy. Sulfonylureas had a small, higher observed hazard of myocardial infarction and eye disorders compared with DPP-4 inhibitors in the meta-analysis. The OHDSI collaborative network can be used to conduct a large international study examining the effectiveness of second-line treatment choices made in clinical management of T2D.
View details for DOI 10.1001/jamanetworkopen.2018.1755
View details for PubMedID 30646124
View details for PubMedCentralID PMC6324274
U-Index, a dataset and an impact metric for informatics tools and databases
2018; 5: 180043
Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources' impact over time. Our dataset differentiates the context in which citations occur to distinguish between 'awareness' and 'usage', and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.
View details for PubMedID 29557976
Performing an Informatics Consult: Methods and Challenges
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
2018; 15 (3): 563–68
Our health care system is plagued by missed opportunities, waste, and harm. Data generated in the course of care are often underutilized, scientific insight goes untranslated, and evidence is overlooked. To address these problems, we envisioned a system where aggregate patient data can be used at the bedside to provide practice-based evidence. To create that system, we directly connect practicing physicians to clinical researchers and data scientists through an informatics consult. Our team processes and classifies questions posed by clinicians, identifies the appropriate patient data to use, runs the appropriate analyses, and returns an answer, ideally in a 48-hour time window. Here, we discuss the methods that are used for data extraction, processing, and analysis in our consult. We continue to refine our informatics consult service, moving closer to a learning health care system.
View details for PubMedID 29396125
Developing a data sharing community for spinal cord injury research.
The rapid growth in data sharing presents new opportunities across the spectrum of biomedical research. Global efforts are underway to develop practical guidance for implementation of data sharing and open data resources. These include the recent recommendation of 'FAIR Data Principles', which assert that if data is to have broad scientific value, then digital representations of that data should be Findable, Accessible, Interoperable and Reusable (FAIR). The spinal cord injury (SCI) research field has a long history of collaborative initiatives that include sharing of preclinical research models and outcome measures. In addition, new tools and resources are being developed by the SCI research community to enhance opportunities for data sharing and access. With this in mind, the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH) hosted a workshop on October 5-6, 2016 in Bethesda, MD, in collaboration with the Open Data Commons for Spinal Cord Injury (ODC-SCI) titled "Preclinical SCI Data: Creating a FAIR Share Community". Workshop invitees were nominated by the workshop steering committee (co-chairs: ARF and VPL; members: AC, KDA, MSB, KF, LBJ, PGP, JMS), to bring together junior and senior level experts including preclinical and basic SCI researchers from academia and industry, data science and bioinformatics experts, investigators with expertise in other neurological disease fields, clinical researchers, members of the SCI community, and program staff representing federal and private funding agencies. The workshop and ODC-SCI efforts were sponsored by the International Spinal Research Trust (ISRT), the Rick Hansen Institute, Wings for Life, the Craig H. Neilsen Foundation and NINDS. The number of attendees was limited to ensure active participation and feedback in small groups. The goals were to examine the current landscape for data sharing in SCI research and provide a path to its future. Below are highlights from the workshop, including perspectives on the value of data sharing in SCI research, workshop participant perspectives and concerns, descriptions of existing resources and actionable directions for further engaging the SCI research community in a model that may be applicable to many other areas of neuroscience. This manuscript is intended to share these initial findings with the broader research community, and to provide talking points for continued feedback from the SCI field, as it continues to move forward in the age of data sharing.
View details for DOI 10.1016/j.expneurol.2017.05.012
View details for PubMedID 28576567
- Machine Learning in Healthcare Key Advances in Clinical Informatics: Transforming Health Care through Health Information Technology Elsevier. 2017: 279–291
RegenBase: a knowledge base of spinal cord injury biology for translational research
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org.
View details for DOI 10.1093/database/baw040
View details for Web of Science ID 000374094100001
View details for PubMedID 27055827
View details for PubMedCentralID PMC4823819
Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records.
2016; 39 (1): 45-57
Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs).We prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.We collected information for 5983 putative EHR-derived drug-drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug-drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.Our proof-of-concept method for scoring putative drug-drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug-drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.
View details for DOI 10.1007/s40264-015-0352-2
View details for PubMedID 26446143
The health care and life sciences community profile for dataset descriptions.
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.
View details for DOI 10.7717/peerj.2331
View details for PubMedID 27602295
View details for PubMedCentralID PMC4991880
An evidence-based approach to identify aging-related genes in Caenorhabditis elegans
View details for DOI 10.1186/s12859-015-0469-4
View details for Web of Science ID 000349409100001
Analyzing search behavior of healthcare professionals for drug safety surveillance.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2015; 20: 306-317
Post-market drug safety surveillance is hugely important and is a significant challenge despite the existence of adverse event (AE) reporting systems. Here we describe a preliminary analysis of search logs from healthcare professionals as a source for detecting adverse drug events. We annotate search log query terms with biomedical terminologies for drugs and events, and then perform a statistical analysis to identify associations among drugs and events within search sessions. We evaluate our approach using two different types of reference standards consisting of known adverse drug events (ADEs) and negative controls. Our approach achieves a discrimination accuracy of 0.85 in terms of the area under the receiver operator curve (AUC) for the reference set of well-established ADEs and an AUC of 0.68 for the reference set of recently labeled ADEs. We also find that the majority of associations in the reference sets have support in the search log data. Despite these promising results additional research is required to better understand users' search behavior, biasing factors, and the overall utility of analyzing healthcare professional search logs for drug safety surveillance.
View details for PubMedID 25592591
Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance.
Journal of medical Internet research
2015; 17 (8)
Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance.To analyze health care professionals' information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource.Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert.Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert.Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate.
View details for DOI 10.2196/jmir.4427
View details for PubMedID 26293444
Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art
2014; 37 (10): 777-790
Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.
View details for DOI 10.1007/s40264-014-0218-z
View details for Web of Science ID 000344615300005
View details for PubMedCentralID PMC4217510
Minimum Information about a Spinal Cord Injury Experiment: A Proposed Reporting Standard for Spinal Cord Injury Experiments
JOURNAL OF NEUROTRAUMA
2014; 31 (15): 1354-1361
The lack of reproducibility in many areas of experimental science has a number of causes, including a lack of transparency and precision in the description of experimental approaches. This has far-reaching consequences, including wasted resources and slowing of progress. Additionally, the large number of laboratories around the world publishing articles on a given topic make it difficult, if not impossible, for individual researchers to read all of the relevant literature. Consequently, centralized databases are needed to facilitate the generation of new hypotheses for testing. One strategy to improve transparency in experimental description, and to allow the development of frameworks for computer-readable knowledge repositories, is the adoption of uniform reporting standards, such as common data elements (data elements used in multiple clinical studies) and minimum information standards. This article describes a minimum information standard for spinal cord injury (SCI) experiments, its major elements, and the approaches used to develop it. Transparent reporting standards for experiments using animal models of human SCI aim to reduce inherent bias and increase experimental value.
View details for DOI 10.1089/neu.2014.3400
View details for Web of Science ID 000340535500004
View details for PubMedID 24870067
Automatically exposing OpenLifeData via SADI semantic Web Services
Journal of Biomedical Semantics
View details for DOI 10.1186/2041-1480-5-46
The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery.
Journal of biomedical semantics
2014; 5 (1): 14-?
The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org.
View details for DOI 10.1186/2041-1480-5-14
View details for PubMedID 24602174
Ontology-Based Querying with Bio2RDF's Linked Open Data.
Journal of biomedical semantics
2013; 4: S1-?
A key activity for life scientists in this post "-omics" age involves searching for and integrating biological data from a multitude of independent databases. However, our ability to find relevant data is hampered by non-standard web and database interfaces backed by an enormous variety of data formats. This heterogeneity presents an overwhelming barrier to the discovery and reuse of resources which have been developed at great public expense.To address this issue, the open-source Bio2RDF project promotes a simple convention to integrate diverse biological data using Semantic Web technologies. However, querying Bio2RDF remains difficult due to the lack of uniformity in the representation of Bio2RDF datasets.We describe an update to Bio2RDF that includes tighter integration across 19 new and updated RDF datasets. All available open-source scripts were first consolidated to a single GitHub repository and then redeveloped using a common API that generates normalized IRIs using a centralized dataset registry. We then mapped dataset specific types and relations to the Semanticscience Integrated Ontology (SIO) and demonstrate simplified federated queries across multiple Bio2RDF endpoints.This coordinated release marks an important milestone for the Bio2RDF open source linked data framework. Principally, it improves the quality of linked data in the Bio2RDF network and makes it easier to access or recreate the linked data locally. We hope to continue improving the Bio2RDF network of linked data by identifying priority databases and increasing the vocabulary coverage to additional dataset vocabularies beyond SIO.
View details for DOI 10.1186/2041-1480-4-S1-S1
View details for PubMedID 23735196
Evaluating Scientific Hypotheses Using the SPARQL Inferencing Notation
The Semantic Web: Research and Applications
Springer Berlin Heidelberg. 2012: 647–658
View details for DOI 10.1007/978-3-642-30284-8_50
HyQue: evaluating hypotheses using Semantic Web technologies.
Journal of biomedical semantics
2011; 2: S3-?
Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks.We present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF.HyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque.
View details for DOI 10.1186/2041-1480-2-S2-S3
View details for PubMedID 21624158
Contextual Cocitation: Augmenting Cocitation Analysis and its Applications
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
2010; 61 (6): 1130-1143
View details for DOI 10.1002/asi.21313
View details for Web of Science ID 000277892600005
Behaviourally mediated crypsis in two nocturnal moths with contrasting appearance
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
2009; 364 (1516): 503-510
The natural resting orientations of several species of nocturnal moth on tree trunks were recorded over a three-month period in eastern Ontario, Canada. Moths from certain genera exhibited resting orientation distributions that differed significantly from random, whereas others did not. In particular, Catocala spp. collectively tended to orient vertically, whereas subfamily Larentiinae representatives showed a variety of orientations that did not differ significantly from random. To understand why different moth species adopted different orientations, we presented human subjects with a computer-based detection task of finding and 'attacking' Catocala cerogama and Euphyia intermediata target images at different orientations when superimposed on images of sugar maple (Acer saccharum) trees. For both C. cerogama and E. intermediata, orientation had a significant effect on survivorship, although the effect was more pronounced in C. cerogama. When the tree background images were flipped horizontally the optimal orientation changed accordingly, indicating that the detection rates were dependent on the interaction between certain directional appearance features of the moth and its background. Collectively, our results suggest that the contrasting wing patterns of the moths are involved in background matching, and that the moths are able to improve their crypsis through appropriate behavioural orientation.
View details for DOI 10.1098/rstb.2008.0215
View details for Web of Science ID 000262353500010
View details for PubMedID 19000977
Empirical tests of the role of disruptive coloration in reducing detectability
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
2007; 274 (1615): 1325-1331
Disruptive patterning is a potentially universal camouflage technique that is thought to enhance concealment by rendering the detection of body shapes more difficult. In a recent series of field experiments, artificial moths with markings that extended to the edges of their 'wings' survived at higher rates than moths with the same edge patterns inwardly displaced. While this result seemingly indicates a benefit to obscuring edges, it is possible that the higher density markings of the inwardly displaced patterns concomitantly reduced their extent of background matching. Likewise, it has been suggested that the mealworm baits placed on the artificial moths could have created differential contrasts with different moth patterns. To address these concerns, we conducted controlled trials in which human subjects searched for computer-generated moth images presented against images of oak trees. Moths with edge-extended disruptive markings survived at higher rates, and took longer to find, than all other moth types, whether presented sequentially or simultaneously. However, moths with no edge markings and reduced interior pattern density survived better than their high-density counterparts, indicating that background matching may have played a so-far unrecognized role in the earlier experiments. Our disruptively patterned non-background-matching moths also had the lowest overall survivorship, indicating that disruptive coloration alone may not provide significant protection from predators. Collectively, our results provide independent support for the survival value of disruptive markings and demonstrate that there are common features in human and avian perception of camouflage.
View details for DOI 10.1098/rspb.2007.0153
View details for Web of Science ID 000245301900012
View details for PubMedID 17360282