Suzanne Tamang is based at the Center for Population Health Sciences She received her Ph.D. in Computer Science from the City University of New York and completed her postdoctoral training at the Stanford's Center for Biomedical Bioinformatics.
At Stanford, Suzanne's collaborations span the Alcoa Research Consortium, the Clinical Excellence Research Center and the Stanford Cancer Institute. She is also affiliated with the Department of Rheumatology at UCSF.
Instructor, Biomedical Data Science
Postdoctoral Training, Stanford School of Medicine, Biomedical Informatics (2015)
Doctor of Philosophy, Graduate Center, City University of New York (CUNY), Computer Science (2013)
Master of Science, Brooklyn College, CUNY, Computer Science and Health Science (2006)
Bachelor of Science, Brooklyn College, CUNY, Biology
The incidence of hematologic cancers after breast cancer. A 35-year population-based cohort study in Denmark
WILEY. 2019: 75
View details for Web of Science ID 000481785600147
Risk of primary urological and genital cancers following incident breast cancer: A Danish population-based cohort study
WILEY. 2019: 79
View details for Web of Science ID 000481785600155
Risk of primary gastrointestinal cancers following incident breast cancer: A Danish population-based cohort study
WILEY. 2019: 81
View details for Web of Science ID 000481785600159
- Stress Disorders and Dementia in the Danish Population AMERICAN JOURNAL OF EPIDEMIOLOGY 2019; 188 (3): 493–99
Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.
2019; 2 (4): 528–37
Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data.We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients.There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870.To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.
View details for DOI 10.1093/jamiaopen/ooz040
View details for PubMedID 32025650
View details for PubMedCentralID PMC6994019
Stress Disorders and Dementia in the Danish Population.
American journal of epidemiology
There is an association between stress and dementia. However, less is known about dementia among persons with varied stress responses and sex differences in these associations. This population-based cohort study examined dementia among persons with a range of clinician-diagnosed stress disorders, and the interaction between stress disorders and sex in predicting dementia, in Denmark from 1995 to 2011. This study included Danes 40 years or older with a stress disorder diagnosis (n=47,047) and a matched comparison cohort (n=232,141) without a stress disorder diagnosis from 1995 through 2011. Diagnoses were culled from national registries. We used Cox proportional-hazards regression to estimate associations between stress disorders and dementia. Risk of dementia was higher for persons with stress disorders than for persons without such diagnosis; adjusted hazard ratios ranged from 1.6 to 2.8. There was evidence of an interaction between sex and stress disorders in predicting dementia, with a greater rate of dementia among men with stress disorders except posttraumatic stress disorder, for which women had a greater rate. Results support existing evidence of an association between stress and dementia. This study contributes novel information regarding dementia risk across a range of stress responses, and interactions between stress disorders and sex.
View details for PubMedID 30576420
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
JAMA INTERNAL MEDICINE
2018; 178 (11): 1544–47
A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning-based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.
View details for PubMedID 30128552
Scalable Electronic Phenotyping For Studying Patient Comorbidities.
AMIA ... Annual Symposium proceedings. AMIA Symposium
2018; 2018: 740–49
Over 75 million Americans have multiple concurrent chronic conditions and medical decision making for these patients is mostly based on retrospective cohort studies. Current methods to generate cohorts of patients with comorbidities are neither scalable nor generalizable. We propose a supervised machine learning algorithm for learning comorbidity phenotypes without requiring manually created training sets. First, we generated myocardial infarction (MI) and type-2 diabetes (T2DM) patient cohorts using ICD9-based imperfectly labeled samples upon which LASSO logistic regression models were trained. Second, we assessed the effects of training sample size, inclusion of physician input, and inclusion of clinical text features on model performance. Using ICD9 codes as our labeling heuristic, we achieved comparable performance to models created using keywords as labeling heuristic. We found that expert input and higher training sample sizes could compensate for the lack of clinical text derived features. However, our best performing model included clinical text as features with a large training sample size.
View details for PubMedID 30815116
SynthNotes: A Generator Framework for High-volume, High-fidelity Synthetic Mental Health Notes
IEEE. 2018: 951–58
View details for Web of Science ID 000468499301003
Performance of Machine Learning Methods Using Electronic Medical Records to Predict Varicella Zoster Virus Infection
View details for Web of Science ID 000411824106394
Predicting patient 'cost blooms' in Denmark: a longitudinal population-based study.
2017; 7 (1)
To compare the ability of standard versus enhanced models to predict future high-cost patients, especially those who move from a lower to the upper decile of per capita healthcare expenditures within 1 year-that is, 'cost bloomers'.We developed alternative models to predict being in the upper decile of healthcare expenditures in year 2 of a sample, based on data from year 1. Our 6 alternative models ranged from a standard cost-prediction model with 4 variables (ie, traditional model features), to our largest enhanced model with 1053 non-traditional model features. To quantify any increases in predictive power that enhanced models achieved over standard tools, we compared the prospective predictive performance of each model.We used the population of Western Denmark between 2004 and 2011 (2 146 801 individuals) to predict future high-cost patients and characterise high-cost patient subgroups. Using the most recent 2-year period (2010-2011) for model evaluation, our whole-population model used a cohort of 1 557 950 individuals with a full year of active residency in year 1 (2010). Our cost-bloom model excluded the 155 795 individuals who were already high cost at the population level in year 1, resulting in 1 402 155 individuals for prediction of cost bloomers in year 2 (2011).Using unseen data from a future year, we evaluated each model's prospective predictive performance by calculating the ratio of predicted high-cost patient expenditures to the actual high-cost patient expenditures in Year 2-that is, cost capture.Our best enhanced model achieved a 21% and 30% improvement in cost capture over a standard diagnosis-based model for predicting population-level high-cost patients and cost bloomers, respectively.In combination with modern statistical learning methods for analysing large data sets, models enhanced with a large and diverse set of features led to better performance-especially for predicting future cost bloomers.
View details for DOI 10.1136/bmjopen-2016-011580
View details for PubMedID 28077408
View details for PubMedCentralID PMC5253526
Enhanced Quality Measurement Event Detection: An Application to Physician Reporting.
EGEMS (Washington, DC)
2017; 5 (1): 5
The wide-scale adoption of electronic health records (EHR)s has increased the availability of routinely collected clinical data in electronic form that can be used to improve the reporting of quality of care. However, the bulk of information in the EHR is in unstructured form (e.g., free-text clinical notes) and not amenable to automated reporting. Traditional methods are based on structured diagnostic and billing data that provide efficient, but inaccurate or incomplete summaries of actual or relevant care processes and patient outcomes. To assess the feasibility and benefit of implementing enhanced EHR- based physician quality measurement and reporting, which includes the analysis of unstructured free- text clinical notes, we conducted a retrospective study to compare traditional and enhanced approaches for reporting ten physician quality measures from multiple National Quality Strategy domains. We found that our enhanced approach enabled the calculation of five Physician Quality and Performance System measures not measureable in billing or diagnostic codes and resulted in over a five-fold increase in event at an average precision of 88 percent (95 percent CI: 83-93 percent). Our work suggests that enhanced EHR-based quality measurement can increase event detection for establishing value-based payment arrangements and can expedite quality reporting for physician practices, which are increasingly burdened by the process of manual chart review for quality reporting.
View details for PubMedID 29881731
New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An Example of Detecting Urinary Incontinence Following Prostatectomy.
EGEMS (Washington, DC)
2016; 4 (3): 1231-?
National initiatives to develop quality metrics emphasize the need to include patient-centered outcomes. Patient-centered outcomes are complex, require documentation of patient communications, and have not been routinely collected by healthcare providers. The widespread implementation of electronic medical records (EHR) offers opportunities to assess patient-centered outcomes within the routine healthcare delivery system. The objective of this study was to test the feasibility and accuracy of identifying patient centered outcomes within the EHR.Data from patients with localized prostate cancer undergoing prostatectomy were used to develop and test algorithms to accurately identify patient-centered outcomes in post-operative EHRs - we used urinary incontinence as the use case. Standard data mining techniques were used to extract and annotate free text and structured data to assess urinary incontinence recorded within the EHRs.A total 5,349 prostate cancer patients were identified in our EHR-system between 1998-2013. Among these EHRs, 30.3% had a text mention of urinary incontinence within 90 days post-operative compared to less than 1.0% with a structured data field for urinary incontinence (i.e. ICD-9 code). Our workflow had good precision and recall for urinary incontinence (positive predictive value: 0.73 and sensitivity: 0.84).Our data indicate that important patient-centered outcomes, such as urinary incontinence, are being captured in EHRs as free text and highlight the long-standing importance of accurate clinician documentation. Standard data mining algorithms can accurately and efficiently identify these outcomes in existing EHRs; the complete assessment of these outcomes is essential to move practice into the patient-centered realm of healthcare.
View details for DOI 10.13063/2327-9214.1231
View details for PubMedID 27347492
Detecting unplanned care from clinician notes in electronic health records.
Journal of oncology practice / American Society of Clinical Oncology
2015; 11 (3): e313-9
Reduction in unplanned episodes of care, such as emergency department visits and unplanned hospitalizations, are important quality outcome measures. However, many events are only documented in free-text clinician notes and are labor intensive to detect by manual medical record review.We studied 308,096 free-text machine-readable documents linked to individual entries in our electronic health records, representing care for patients with breast, GI, or thoracic cancer, whose treatment was initiated at one academic medical center, Stanford Health Care (SHC). Using a clinical text-mining tool, we detected unplanned episodes documented in clinician notes (for non-SHC visits) or in coded encounter data for SHC-delivered care and the most frequent symptoms documented in emergency department (ED) notes.Combined reporting increased the identification of patients with one or more unplanned care visits by 32% (15% using coded data; 20% using all the data) among patients with 3 months of follow-up and by 21% (23% using coded data; 28% using all the data) among those with 1 year of follow-up. Based on the textual analysis of SHC ED notes, pain (75%), followed by nausea (54%), vomiting (47%), infection (36%), fever (28%), and anemia (27%), were the most frequent symptoms mentioned. Pain, nausea, and vomiting co-occur in 35% of all ED encounter notes.The text-mining methods we describe can be applied to automatically review free-text clinician notes to detect unplanned episodes of care mentioned in these notes. These methods have broad application for quality improvement efforts in which events of interest occur outside of a network that allows for patient data sharing.
View details for DOI 10.1200/JOP.2014.002741
View details for PubMedID 25980019
View details for PubMedCentralID PMC4438112
- Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art DRUG SAFETY 2014; 37 (10): 777-790