Madeleine Udell

All Publications

• Disciplined Multi-Convex Programming Shen, X., Diamond, S., Udell, M., Gu, Y., Boyd, S., IEEE IEEE. 2017: 895–900

  View details for Web of Science ID 000427082203091

• Bounding duality gap for separable problems with linear constraints COMPUTATIONAL OPTIMIZATION AND APPLICATIONS Udell, M., Boyd, S. 2016; 64 (2): 355-378

  View details for DOI 10.1007/s10589-015-9819-4

  View details for Web of Science ID 000376872900002

• DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Schuler, A., Liu, V., Wan, J., Callahan, A., Udell, M., Stark, D. E., Shah, N. H. 2016; 21: 144-155

  Abstract

  The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.

  View details for PubMedID 26776181

• Introduction FOUNDATIONS AND TRENDS IN MACHINE LEARNING Udell, M., Horn, C., Zadeh, R., Boyd, S. 2016; 9 (1): 2-+

  View details for DOI 10.1561/2200000055

  View details for Web of Science ID 000383972700001

• Revenue Maximization for Broadband Service Providers Using Revenue Capacity Mehmood, H., Udell, M., Cioffi, J., IEEE IEEE. 2015

  View details for DOI 10.1109/GLOCOM.2015.7417861

  View details for Web of Science ID 000382389305113

• FACTORIZATION FOR ANALOG-TO-DIGITAL MATRIX MULTIPLICATION Lee, E. H., Udell, M., Wong, S., IEEE IEEE. 2015: 1061–65

  View details for Web of Science ID 000427402901036

• Incorporation of flexible objectives and time-linked simulation with flux balance analysis. Journal of theoretical biology Birch, E. W., Udell, M., Covert, M. W. 2014; 345: 12-21

  Abstract

  We present two modifications of the flux balance analysis (FBA) metabolic modeling framework which relax implicit assumptions of the biomass reaction. Our flexible flux balance analysis (flexFBA) objective removes the fixed proportion between reactants, and can therefore produce a subset of biomass reactants. Our time-linked flux balance analysis (tFBA) simulation removes the fixed proportion between reactants and byproducts, and can therefore describe transitions between metabolic steady states. Used together, flexFBA and tFBA model a time scale shorter than the regulatory and growth steady state encoded by the biomass reaction. This combined short-time FBA method is intended for integrated modeling applications to enable detailed and dynamic depictions of microbial physiology such as whole-cell modeling. For example, when modeling Escherichia coli, it avoids artifacts caused by low-copy-number enzymes in single-cell models with kinetic bounds. Even outside integrated modeling contexts, the detailed predictions of flexFBA and tFBA complement existing FBA techniques. We show detailed metabolite production of in silico knockouts used to identify when correct essentiality predictions are made for the wrong reason.

  View details for DOI 10.1016/j.jtbi.2013.12.009

  View details for PubMedID 24361328

  View details for PubMedCentralID PMC3933926

• Analyzing patterns of drug use in clinical notes for patient safety. AMIA Summits on Translational Science proceedings AMIA Summit on Translational Science LePendu, P., Liu, Y., Iyer, S., Udell, M. R., Shah, N. H. 2012; 2012: 63-70

  Abstract

  Doctors prescribe drugs for indications that are not FDA approved. Research indicates that 21% of prescriptions filled are for off-label indications. Of those, more than 73% lack supporting scientific evidence. Traditional drug safety alerts may not cover usages that are not FDA approved. Therefore, analyzing patterns of off-label drug usage in the clinical setting is an important step toward reducing the incidence of adverse events and for improving patient safety. We applied term extraction tools on the clinical notes of a million patients to compile a database of statistically significant patterns of drug use. We validated some of the usage patterns learned from the data against sources of known on-label and off-label use. Given our ability to quantify adverse event risks using the clinical notes, this will enable us to address patient safety because we can now rank-order off-label drug use and prioritize the search for their adverse event profiles.

  View details for PubMedID 22779054