Grants and Funding: Extramural Programs (EP)
2013 NLM Informatics Lecture Series – Speaker Profiles
Dr. Gonzalez is an assistant professor at the Department of Biomedical Informatics at Arizona State University, and data core director of one of the National Institute on Aging supported Alzheimer’s Disease Centers. She is a member of the NLM’s chartered scientific review committee. She leads the discovery through integration and extraction of genomic knowledge lab, in the area of knowledge discovery, focusing her research on translational applications of information extraction using natural language processing techniques. Her research has contributed to the advancement of knowledge discovery methods across the biomedical spectrum.
Can social media provide reliable signals of adverse drug reactions?
Pre-market testing of drugs produces reasonably high quality information about the efficacy of the drug as a treatment for the condition for which it was approved, but gives a very incomplete picture of the drug’s safety. It is only after a drug is marketed and used on a more widespread basis over longer periods of time that it is possible to identify other effects, such as rare but serious adverse effects, or those that are more common in the special subgroups excluded from the trial, among others. Post-marketing surveillance currently relies on voluntary reporting to the FDA by health care professionals (and recently, patients themselves). Self-reported patient information captures a valuable perspective not captured by other means, and has been found to be of similar quality to that provided by health professionals. However, the value of numerous, informal self-reports such as those found in social network postings has not been evaluated. Through recently awarded NIH/NLM funding, Dr. Gonzalez is deploying the infrastructure needed to explore the value of such postings as a source of “signals” of potential adverse drug reactions soon after the drugs hit the market. Despite the significant challenge of processing colloquial text, her studies showed promising results. Additional evaluation on un-annotated comments revealed encouraging correlations between adverse drug reactions found by her system and the documented reactions for those drugs. An overview of the methods and ongoing findings of this project will be discussed in this presentation, particularly as Dr. Gonzalez seek to answer the question: can social media provide reliable signals of adverse drug reactions?
Mining Social Network Postings for Mentions of Potential Adverse Drug Reactions
Arizona State University
Dr. Chunhua Weng is the Florence Irving Assistant Professor of Biomedical Informatics at Columbia University, where she has been a faculty member since 2007. Before arriving at Columbia, she obtained an undergraduate degree in computer science with a focus on software engineering from Nankai University, P. R. China, a master’s degree in Information and Computer Science from University of California at Irvine, and a Ph.D. in Biomedical and Health Informatics from University of Washington at Seattle. Her research theme is developing human-computer collaborative approaches to help clinical researchers make the best use of health information technology. Her current research is focused on problems that include interactive query formulation to assist clinical researchers in interrogating large clinical databases.
Bridging the Semantic Gap between Research Eligibility Criteria and Clinical Data: Methods and Issues
Central to clinical and translational research activities, clinical research eligibility criteria are perceived, defined, interpreted, and implemented by various stakeholders in a series of translations. They are initially authored by investigators to define target research populations. Then they are interpreted by research volunteers seeking experimental therapies for self-screening, translated and implemented by clinical database query analysts as database queries for electronic screening, referenced by research coordinators for manual patient screening, summarized in meta-analyses by scientists for developing clinical practice guidelines, and eventually interpreted by physicians to screen patients for evidence-based care. As a result, their intended meanings often get distorted, as in the game of “telephone”, and lead to misinterpretation of clinical research results.
With the burgeoning adoption of electronic health records (EHRs), vast amounts of clinical data are increasingly available for computational reuse. It is imperative that the scientific community leverage these data to accelerate clinical and translational science at low cost and large scale. A critical step toward this goal is cohort identification by matching clinical data to research eligibility criteria. However, this task is complicated by the semantic gap between the raw clinical data and free-text human-provided eligibility criteria: each criterion has many ways to describe it and a myriad of clinical data points that represent it.
Dr. Weng will describe the evolving understanding of the semantic gap and approaches to overcoming it in the context of EHR-based phenotyping and clinical trial prescreening. She will present considerations for augmenting domain experts in interrogating large clinical databases as part of their current efforts and the need for data-driven phenotype modeling and summarization as their potential future directions.
Bridging the Semantic Gap between Research Eligibility Criteria and Clinical Data
2012 NLM Informatics Lecture Series – Speaker Profiles
Dr. Gregory Cooper is a Professor of Biomedical Informatics and of Intelligent Systems at the University of Pittsburgh, where he has been a faculty member since 1990. Prior to arriving at the University of Pittsburgh, he obtained an undergraduate degree in computer science from MIT, a Ph.D. in Medical Information Sciences from Stanford University, and an M.D. from Stanford. His research theme is the application of probability theory, decision theory, Bayesian statistics, and artificial intelligence to biomedical informatics problems. His current research is focused on problems that include clinical alerting based on machine learning, causal modeling and discovery from clinical and biological data, computer-aided medical diagnosis and prediction, and the detection and characterization of disease outbreaks using clinical data. He is best known for his research on Bayesian networks, especially work on learning Bayesian networks from data. Dr. Cooper was elected as a Fellow into the American College of Medical Informatics in 1991. In 2006 he was elected as a Fellow into the Association for the Advancement of Artificial Intelligence.
Machine Learning of Patient-Specific Predictive Models from Clinical Data
A patient-specific predictive model is a model that is constructed in a way that tailors it to the particular history, symptoms, signs, laboratory results, and other features of the patient case at hand. Such a model can be applied to perform risk assessment, diagnosis, prognosis, and the prediction of response to therapy. In contrast, traditional population-wide models are constructed to perform predictions well on average for all future patient cases. By taking advantage of the known features of a given patient case, the patient-specific method may learn a model that predicts better than a population-wide method. In particular, a patient-specific approach focuses the search for predictive models to those that are closely related to the current patient case, and it specializes model evaluation (scoring) to be sensitive to the features of the current case.
This talk will describe the implementation and evaluation of a particular approach to patient-specific predictive modeling. The evaluation considers two domains. One involves predicting whether a patient with community acquired pneumonia will develop severe sepsis. The other involves predicting whether a patient with heart failure will develop serious medical complications. The results of these studies provide support that patient-specific modeling can improve the prediction of clinical outcomes.
This talk will also discuss how patient-specific methods might be applied in personalized medicine, where the predictive model for a patient is individualized, based on the use of both traditional clinical data as well as high-throughput molecular measurements, such as whole genome data.
Predicting Patient Outcomes from Clinical and Genome-Wide Data
1 R01 LM010020-01
University of Pittsburgh at Pittsburgh
Dr. Hurdle earned his MD from the University of Colorado and his MS in Computer Science from Columbia University in 1981. After working in healthcare informatics, including a stint as CIO for The Graduate Hospital in Philadelphia, he returned to research, completing his PhD in Computer Science from the University of Utah in 1994. He has completed two informatics fellowships, a postdoctoral fellowship in the Utah/VA postdoctoral program (1996-97) and, in 2007 he served as a Senior Fellow at the National Library of Medicine. Dr. Hurdle has a broad interest in the areas of clinical research and public health informatics. His current research interests include: building tools to unlock the content of clinical narratives using natural language processing; finding high-performance computing solutions to clinical research informatics challenges; and exploring novel ways to use informatics to address regulatory and bioethical concerns. His research also includes an historical interest in health-services research and a developing interest in nutritional data-mining to improve individual and population diet-related outcomes. Dr. Hurdle is an appointed member of NLM Biomedical Library and Informatics Review Committee. He has also served as chair of the American Medical Informatics Association's Ethics Committee when it created AMIA's first code of professional conduct.
Nutritional Informatics: Integrating real-time dietary patterns into the Electronic Health Record
Improving the dietary health of the nation has been a long-standing goal of healthcare researchers and practitioners, as well as of the federal government. Efforts such as the National Health and Nutrition Examination Survey (NHANES) are important epidemiological tools in the battle against weight-related healthcare morbidity and mortality. We propose here to bring informatics technology to bear as a personalized medicine intervention in the effort against weight-related healthcare morbidity and mortality. We have preliminary data that indicates we can, using data mining, extract a variety of dietary patterns from family food item sales data. In collaboration with researchers at the USDA, we are exploring ways to map these dietary patterns to standard dietary metrics, such as the Healthy Eating Index (HEI). The goal of the work he will discuss is a new research direction: to find ways to integrate these real-time dietary data into the EHR in a clinically meaningful way. Such metrics, because they are collected automatically at the point of purchase from grocery sales transactions, are virtually free of reporting bias and impose no respondent burden on patients. We see this very much as personalized medicine. By linking dietary pattern metrics to the EHR, dietary trends could become as amenable to monitoring and counseling in the clinical setting as other common biomarker measures such as lipid panels.
Hurdle, John F.
POET-2: High-performance Computing for Advanced Clinical Narrative Preprocessing
1 R01 LM010981-01A1
University of Utah
Dr. Wagner is an Associate Professor of Biomedical Informatics and Intelligent Systems at the University of Pittsburgh. He directs the Real-time Outbreak and Disease Surveillance (RODS) laboratory.
Dr. Wagner’s research focuses on real-time methods for detecting and characterizing disease outbreaks, including the development and testing of operational biosurveillance systems. In his role as director of the RODS Laboratory, Dr. Wagner led the development and implementation of two widely used biosurveillance systems: the RODS system and the National Retail Data Monitor (NRDM). Currently, Dr. Wagner is developing a third system called BioEcon, a decision analytic tool for use by analysts working in health departments.
After completing his education (BS in biology, SUNY at Stony Brook; MD, NYU School of Medicine), Dr. Wagner practiced internal medicine from 1979 to 1988 at Baltimore City Hospital, Bellevue Hospital, and with the Hawaii Permanente Medical Group. He then moved to Pittsburgh where he received additional formal training in artificial intelligence (PhD, Intelligent Systems, University of Pittsburgh) and joined the Pitt faculty in 1991. He also practiced geriatric medicine until 2002.
Decision-theoretic Model of Disease Surveillance and Control and a Prototype Implementation for the Disease Influenza
This talk will first describe a decision-theoretic model of disease surveillance and control, followed by a description of a prototype system for influenza monitoring based on the model. The decision-theoretic model connects disparate work in epidemiological modeling and disease control under a uniform mathematical formulation. The last part of the talk will focus on an ontology for population disease models and an infrastructure called the Apollo Web Service that allows end-user applications and epidemic models to interoperate. The expectation is that the theoretical model, the prototype, and the interoperability infrastructure will stimulate new avenues of research in disease surveillance/control and epidemic modeling.
Wagner, Michael M.
Decision Making in Biosurveillance
5 R01 LM009132-04
University of Pittsburgh at Pittsburgh
The slides for this presentation are available upon request by contacting Ms. Ebony Hughes at Ebony.Hughes@nih.gov.