Elizabeth K. Rasch, PT, PhD
Dr. Rasch is a Staff Scientist and Chief of the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. A physical therapist for over 40 years, Dr. Rasch was one of the first clinical specialists in neurology to be board certified by the American Board of Physical Therapy Specialties. From 2001 to 2007 she was a service fellow in the Office of Analysis and Epidemiology at the National Center for Health Statistics, CDC. During this time, she was actively involved in the Washington Group on Disability Statistics, an international group formed under the auspices of the UN Statistical Commission to develop comparable measures of disability suitable for censuses and surveys worldwide. As Chief of the Epidemiology and Biostatistics Section, Dr. Rasch has administrative responsibility for budget management, identifying and hiring scientific and administrative staff, planning and procurement of contracts, development and execution of institutional agreements, ensuring adherence to Federal regulatory requirements, supervision of staff, promoting staff development, as well as assigning, monitoring and coordinating work of the Section. She has been instrumental in conceptualizing and implementing an inter-agency agreement with the Social Security Administration to improve their disability determination process. As a result, NIH has received access to an unprecedented volume of data from SSA to develop novel, systematic, data-driven analytic tools to augment SSA’s determination processes. In addition, NIH is working collaboratively with researchers from several academic institutions to develop, refine, and evaluate computer adaptive tests which could potentially inform SSA’s disability determination processes. Since SSA is responsible for administering the largest Federal programs serving people with disabilities, NIH has a unique opportunity to develop methods that may meaningfully improve the lives of millions of individuals with disabilities who apply for SSA disability benefits. Dr. Rasch has co-authored over 50 articles. She is a member of the Editorial Board for the Disability and Health Journal.
Chunxiao Zhou, PhD
Dr. Chunxiao Zhou, a Computer Scientist with the Epidemiology and Biostatistics Section, has over 20 years of experience participating in, supporting, and leading projects involving research. With expertise spanning data mining, machine learning, natural language processing, biostatistics, computer vision, image processing, and applied mathematics, Dr. Zhou’s work has provided him with a wide range of experiences and knowledge of research from design to implementation. Since 2011, Dr. Zhou is collaborating with the SSA to develop advanced analytical research methods using machine learning, data mining, and natural language processing (NLP) techniques. Dr. Zhou received his Ph.D. in Electrical Engineering and MS. in Statistics from the University of Illinois, Urbana-Champaign.
Pei-Shu Ho, PhD
Dr. Ho is a health services researcher and biostatistician in the Epidemiology and Biostatistics Section. She has been under contract with the Epidemiology and Biostatistics Section to contribute to the RMD inter-agency agreement with SSA since 2009. In this capacity, she collaborates with her colleagues at NIH to develop analytic approaches to enhance the SSA Disability Determination Process. Currently, she supports research in natural language processing through the development of annotation guidelines and the annotation of functional information documented in clinical notes. She also provides analytic and scientific research support to staff and trainees in RMD. Her research interests include access to care, quality of care, and treatment outcomes among vulnerable populations including those with disabilities. She holds a bachelor’s degree in Chinese Literature from Soochow University, a master’s degree in Health Services Administration from the University of Arkansas at Little Rock, and a Ph.D. in Health Services Organization and Research from the Virginia Commonwealth University. Her work has been published in peer-reviewed articles and presented at national scientific conferences.
Jonathan Camacho, MD
Dr. Camacho is a medical annotator in the Epidemiology and Biostatistics Section. He received his MD degree from Universidad Mayor de San Simon in Bolivia and obtained his accreditation as an international medical graduate from the Educational Commission for Foreign Medical Graduates. He currently works on natural language processing research through the annotation of functional terminology in free text electronic medical records using the International Classification of Functioning Disability and Health. Dr. Camacho is also pursuing a Master of Public Health degree with a concentration in Public Health Practice and Policy at the University of Maryland.
Julia Porcino, MS
Julia Porcino is a mathematical statistician with the Epidemiology and Biostatistics Section. She joined the section in 2014 after completing her M.S. in Mathematics at the University of Oregon as a computer programmer to support data analytics and coding work around the Work Disability Functional Assessment Battery (WD-FAB). Julia currently performs both administrative and analytic work in support of the NIH-SSA inter-agency agreement and to promote health informatics targeted to whole-person function.
Alex Marr, BS
Alex Marr is a Computer Programmer in the Epidemiology and Biostatistics Section. He serves as the Systems Administrator for the section, managing several high-powered machine learning servers. Versed in programming and scripting languages like Python, Java, and Bash, he also employs data engineering techniques towards data pipelines and data storage. He has a Bachelor of Science in Computational and Data Sciences from George Mason University.
Maryanne Sacco, MA, OTR/L
Maryanne Sacco is a clinical annotator with the Epidemiology and Biostatistics Section. She is an occupational therapist by training and as a clinical annotator she supports the SSA Disability Determination Study through research designed to capture the language that disability applicants and their providers use to describe functional abilities and disabilities in every day and work life. She holds a bachelor’s degree in Fine Arts from the United States International University and a master’s of arts degree in Occupational Therapy from the University of Southern California. She has over 35 years of clinical experience and over 15 years’ experience as a professor of occupational therapy at national and international universities. Her research areas have focused on health care outcomes of vulnerable populations and her work has been published in peer-reviewed journals and presented at national and international scientific conferences.
Guy Divita, MS
Mr. Divita is a Computer Scientist in the Epidemiology and Biostatistics Section. He has a long history working in clinical and bio-medical NLP and terminology tasks. He has an interest in NLP tasks that involve entity recognition that are less codified in lexemes from terminologies. His current areas of exploration include document decomposition to elucidate relevant and not relevant sections from noisy OCR’d clinical text. He is also involved with efforts to build out a mental functioning ontology to be used to find mental functioning mentions in clinical notes. He has created entity extraction tools around Body Function to test improvements made to document decomposition tools. He is perennially interested in transforming unstructured text into structured knowledge, both from the side of extraction tools and techniques to developing terminologies and ontologies. He helped develop pipelines and terminologies to identify psycho-social risk factors and general symptoms while working for the VA and the University of Utah. He worked for the National Library of Medicine and had a helping hand in the creation and maintenance of the UMLS, the SPECIALIST Lexicon, MetaMap, MMTx, and the creation of the consumer health vocabulary.
Rafael Jimenez Silva, MPH
Rafael Jiménez Silva is a Data Analyst in Epidemiology & Biostatistics Section. He earned a Bachelor’s degree in Psychology from the University of Puerto Rico, Río Piedras Campus and a Master’s degree in Public Health with a concentration in Biostatistics from the University of Puerto Rico, Medical Sciences Campus. In addition, he has completed a considerate amount of coursework in the field of Computer Science. At NIH, he supports research in natural language processing through the development of annotation/model support tools as well as annotating clinical documents for functional information. He also provides statistical support for the ongoing research in RMD.
Bart Desmet, PhD
Dr. Desmet is a Computer Scientist in the Epidemiology and Biostatistics Section. He is a research scientist with a background in computational linguistics. He obtained his PhD from Ghent University, Belgium, on the automatic detection of suicidal language in online forums. He was a postdoctoral fellow at the Research Foundation - Flanders and an affiliated researcher of the Information Retrieval Lab at Georgetown University. His main research interest is in modelling the linguistic aspects of mental health conditions, such as suicidality, depression and bipolar disorder. At NIH, he focuses on extracting mental and physical functioning information to help in the disability determination process at SSA.
Luke Breitfeller, MLT
Luke Breitfeller is a Computer Scientist in the Epidemiology and Biostatistics Section working under contract on machine learning tasks. He is currently completing a doctorate in language technologies at Carnegie Mellon University, and has completed a masters in language technologies (MLT) as well as an undergraduate degree in creative writing (both also from Carnegie Mellon). His focus is on how information from the rhetorical and semantic layers of language can strengthen machine learning and make it more applicable to solving real-world challenges. Luke's work currently focuses on the construction of narrative in electronic health records, with a specific eye to how the natural structure of narrative can be translated into machine-readable analysis of patient functioning over time. He has worked on projects to identify the underlying structure of medical freeform text for better information extraction, and is heading works to extract detailed, filterable timelines across multiple source documents. He is developing tools for better visualization and framing of time in the medical domain, and formulating a comprehensive grammar of "time rules" to more efficiently extract clear time cues from any format of medical text.
Josh Chang, PhD
Dr. Chang is a Biostatistician in the Epidemiology and Biostatistics Section. He is an applied mathematician with a PhD in Biomathematics (UCLA 2012). He has mainly worked on developing quantitative methods for understanding phenomena in the biomedical and health sciences. He currently works on Bayesian statistical methodology in support of the WD-FAB, including fundamental aspects of item response theory and computer adaptive testing. In the past he has worked on optimizing the case adjudication workflow using queueing methods.
Kathleen (Cricket) Coale, PT, DPT
Cricket is a clinical annotator in the Epidemiology and Biostatistics Section. She joined the Section in August 2020 after practicing clinical physical therapy for 34 years in various settings managing populations with vestibular, gait, and complex medical disorders. She received her B.S. in PT at Russell Sage College and her DPT at Massachusetts General Institute of Health Professions. Currently she serves as a content expert for clinical annotation using the ICF model to support the NIH-SSA interagency agreement to facilitate machine learning and natural language processing in Disability Determination.
Leslee Grubbs-King, BS
Leslee Grubbs-King is a Management Analyst with the Epidemiology and Biostatistics section. Her professional career has been in the area of educating and training young people to enter into the world of work. She has been engaged in this enterprise for 15 years. Many of the individuals she assisted were individuals with intellectual disabilities, and mental and behavioral health challenges. She began her career in New York City as a high school educator. She relocated to the Philadelphia metro area and worked for the State of Delaware at the Department of Labor as a Supervisor in the Employment Training Division. She wanted to work closely with individuals with disabilities in their endeavors to become more independent and worked to change employment opportunities for individuals 16-24 years old in the State of Delaware. That landed her at the Division of Developmental Disability Services where she was the Supervisor for a team of nurses, behavioral support specialists, psychologists, social workers, and family support workers. Eventually, she was appointed as the Regional Program Director for the Division. She moved into the Federal System after attempting to move individuals into training opportunities from sheltered workshops. She headed back to the United States Department of Labor and into the Employment and Training Administration Division of Job Corps. She arrived at NIH with experience and information to help forge ahead in endeavors to recreate policies that strengthen a young person’s ability to go to work and be successful in the workplace. Her most recent educational experience is at Drexel University pursuing a doctoral degree in public health.
Kaushik Gedela, MS
Kaushik Gedela is a computer programmer with the Epidemiology and Biostatistics Section with a Masters in Computer Science and a concentration in Machine Learning from George Mason University. While he finished his Bachelors in Engineering in Computer Engineering he worked as an NLP and AI engineer at a production level in the CTO’s office at Syntel and he also worked as a Full Stack Engineer at anyEMI a loan management service. He is versed in both software engineering and data science techniques at the academic level as well as the production level.
Aakanksha Naik, MLT
Aakanksha Naik is a Computer Scientist in the Epidemiology and Biostatistics Section. She is a PhD candidate at the Language Technologies Institute in the School of Computer Science, Carnegie Mellon University. Her thesis research focuses on building better models and evaluation frameworks for the long tail in language understanding, i.e. domains and phenomena that are underrepresented in traditional language understanding benchmarks. In particular, her recent work focuses on developing better event extraction models for clinical records and doctor-patient conversations, with little to no annotated data, by adapting general-domain extractors to these domains. Her work can be leveraged to improve extraction of key medical and other life events which affect a patient's level of functioning from their free text clinical records, which may serve as helpful evidence for disability adjudication. Prior to starting her PhD, she received her Bachelor’s degree in Computer Science from Birla Institute of Technology and Science, Pilani and her Master’s degree in Language Technologies from Carnegie Mellon University.
Hao-Ren Yao, PhD
Dr. Yao is a Computer Scientist and post-doctoral fellow in the Epidemiology and Biostatistics Section. He holds a Ph.D. in Computer Science from Georgetown University and a B.A degree from the National University of Kaohsiung, Taiwan. During his Ph.D. training, Dr. Yao contributed to research and published several papers for health informatics top conferences with a focus on disease and treatment outcome prediction. Dr. Yao develops interpretable predictive AI models in medicine, specifically, graph modeling in large-scale Electronic Health Records (EHR). Additionally, Dr. Yao has developed a system that predicts the outcome of the drug prescription for selected diseases spanning from short-term to chronic treatments. Physicians have evaluated his system to demonstrate the ability of the system to perform interpretation and potential deployment in the real world. Dr. Yao’s work is licensed with Maxeler Technologies, UK, a leading company in computation. Dr. Yao’s system work with drug prescription has resulted in the transfer and patent-ready prototype system for a real-world product. Dr. Yao recently joined NIH and will work on projects intended to improve the SSA disability determination process using methods in data science.
External Collaborators
Larry Tang, PhD
Dr. Tang is an associate professor and associate chair in the Department of Statistics and Data Science and National Center for Forensic Science at University of Central Florida. He is a statistician specializing in statistical methodology and collaborative research. His current methodological research areas include statistical methods in forensics, diagnostic medicine, group sequential designs and substance abuse research and criminology. He received his Ph.D. in Statistics from Southern Methodist University in 2005. He did postdoctoral training in the Department of Biostatistics at University of Washington. Through the Intergovernmental Personnel Act agreement with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center, he has conducted research on SSA-NIH related topics including accuracy assessment without gold standard, efficiency analysis of decision making units, and classification in high dimensional sparse data. His work in accuracy assessment has generated peer-reviewed publications with potential applications to the accuracy evaluation of the Work Disability Functional Assessment Battery (WD-FAB) system. His work in efficiency analysis has implemented novel applications of data envelopment analysis to identify best practice within SSA ODAR hearing offices via a user-friendly Python package. His work in high dimensional data has generated peer-reviewed publications with potential applications to improve accuracy in SSA disability evaluation.
Aya Zirikly, PhD
Dr. Zirikly is an assistant research scientist in the Center for Language and Speech Processing at Johns Hopkins University and works with the Epidemiology and Biostatistics section on a variety of biomedical Natural Language Processing (NLP) projects. One of the most important NIH projects she works on aims to improve the disability eligibility process at the Social Security Administration. She and her team focus on extracting information relevant to the disability determination, such as the mobility of the applicant, his/her ability for self-care, and mental health factors that affect the applicant’s ability to function using deep learning models. Additionally, she has been heavily involved in suicide risk assessment using social media data, where she and collaborators at the University of Maryland (UMD) released one of the few annotated suicide risk assessment data sets (UMD Reddit Suicidality Dataset). Dr. Zirikly holds a PhD in computer science with a focus on NLP from the George Washington University under the supervision of Dr. Mona Diab. Her work focused on Named Entity Recognition (NER) for low-resource languages, in addition to developing transfer learning techniques for high-low resource settings especially in the area of NER.
Denis Newman-Griffis, PhD
Denis Newman-Griffis received his PhD in the Speech and Language Technologies (SLaTe) lab at The Ohio State University, in the department of Computer Science and Engineering. He is currently a Postdoctoral Scholar in the Department of Biomedical Informatics at the University of Pittsburgh. His research includes exploration of how Natural Language Processing (NLP) techniques can be utilized to help model a patient's level of functioning based on their free text clinical records. His research deals primarily with learning semantic representations for words and concepts/entities, and using these as a tool in transferring machine learning knowledge between domains. In particular, his interests include adapting both general-domain NLP and biomedical NLP techniques to the unique challenges of extracting information about patient functioning.
Carolyn Rose, PhD
Dr. Carolyn Rosé is a Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. She serves as a consultant at the NIH Clinical Center in the Department of Epidemiology and Biostatistics where she works on topics in medical NLP including concept normalization, event extraction, event ordering, and co-reference. Her broader research program focuses on computational modeling of discourse to enable scientific understanding the social and pragmatic nature of conversational interaction of all forms, and using this understanding to build intelligent computational systems for improving collaborative interactions. Her research group’s highly interdisciplinary work, published in over 250 peer reviewed publications, is represented in the top venues of 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards in 3 of these fields. She is a Past President and Inaugural Fellow of the International Society of the Learning Sciences, Senior member of IEEE, Founding Chair of the International Alliance to Advance Learning in the Digital Era, and Co-Editor-in-Chief of the International Journal of Computer-Supported Collaborative Learning. She also serves as a 2020-2021 AAAS Fellow under the Leshner Institute for Public Engagement with Science, with a focus on public engagement with Artificial Intelligence.
Howard Goldman MD, PhD
Dr. Howard Goldman is Professor of Psychiatry, University of Maryland School of Medicine. He is a mental health policy researcher with a special interest in disability policy and practices. He is an elected member of the National Academy of Social Insurance and the National Academy of Medicine, where he chairs a Standing Committee providing advice to the Social Security Administration.
Christine McDonough, PT, PhD
Dr. McDonough is an assistant professor in the Department of Physical Therapy and in Orthopaedic Surgery. Her primary areas of research include the prevention of work disability. development and testing of patient-centered outcome measures using item response theory and computer adaptive testing methods, clinical and health services research in fall and fracture prevention and management for older adults, the measurement of function for work disability determination and rehabilitation, and cost-effectiveness of alternative management approaches for musculoskeletal disorders. She provides methodological expertise and serves as Editor of Clinical Practice Guidelines for the Orthopaedic Section and Academy of Geriatric Physical Therapy of the American Physical Therapy Association (APTA) and Chair of the Scientific Advisory Panel for the APTA Physical Therapy Outcomes Registry. She has worked for several years on research design for commercialization of technologies and medical devices in rehabilitation and pediatrics.
Elizabeth (Beth) Marfeo, PhD, PMH, OT
Dr. Marfeo received her B.S. degree from the Medical College of Georgia, with a major in Occupational Therapy. She graduated from Yale School of Public Health earning a MPH in Health Policy, then continued her research training and academic career at Boston University School of Public Health where she received a PhD in Health Services Research. Dr. Marfeo is currently an Associate Professor (with tenure) at Tufts University in the Departments of Occupational Therapy and Community Health. Dr. Marfeo is also the founder and Director of the Health and Productive Aging Lab. Dr. Marfeo’s research expertise is in the field of health services research, specifically in health outcomes measurement and development. As a clinically trained occupational therapist, Dr. Marfeo integrates her background in rehabilitation sciences with health services methodologies to conduct research in the following areas: work disability evaluation and policy; physical function and mental health assessment; and promoting productive aging and participation among older adults. Dr. Marfeo’s interdisciplinary research approach integrates paradigms of disability, aging, public health, and rehabilitation sciences. Dr. Marfeo has a diverse portfolio of funded research and numerous publications in leading peer-reviewed journals in the fields health outcomes measurement, occupational therapy, and health policy. Along with her research, Dr. Marfeo is dedicated to professional service and development. Dr. Marfeo is a Quality Advisor to the American Occupational Therapy Association. In this role Dr. Marfeo provides expert advice and guidance related to proposed quality measures that may impact the practice of occupational therapy (e.g. National Quality Forum, Centers for Medicare & Medicaid Services). Dr. Marfeo also was named as part of the Technical Expert Panel (TEP) for the Refinement of Long-Term Care Hospital, Inpatient Rehabilitation Facility, Skilled Nursing Facility/Nursing Facility, and Home Health Function Measures. Dr. Marfeo is also committed to promoting diversity, equity, inclusion and social justice through her research, teaching, and mentoring and was named a Tufts University’s Tisch Social-Emotional Learning for Equity and Civic Teaching Fellow for the 2020-2021 academic year.