Health Analytics

Health Analytics

The Health Analytics Center builds upon the work of teams of Columbia researchers drawn from the fields of medicine, biology, public health, informatics, computer science, applied mathematics, and statistics. The mission of the center is to improve the health of individuals and the healthcare system through data-driven methods and understanding of health processes. The Health Analytics Center is located at the Columbia University Medical Center.

COMMITTEE

CHAIR: Itsik Pe'er, Columbia Engineering | Computer Science
CO-CHAIR: Olena Mamykina, Vagelos College of Physicians and Surgeons | Biomedical Informatics
Andrea Califano, Vagelos College of Physicians and Surgeons | Systems Biology, Biomedical Informatics, and Biochemistry and Molecular Biophysics
Tal Danino, Columbia Engineering | Biomedical Engineering
Jeff Goldsmith, Mailman School of Public Health | Biostatistics
Xiaofu He, Vagelos College of Physicians and Surgeons | Psychiatry
Christoph Juchem, Columbia Engineering | Biomedical Engineering
Andrew Laine, Columbia Engineering | Biomedical Engineering and Vagelos College of Physicians and Surgeons | Radiology
Jacqueline Merrill, School of Nursing and Vagelos College of Physicians and Surgeons | Biomedical Informatics
Karthik Natarajan, Vagelos College of Physicians and Surgeons | Biomedical Informatics
Adler Perotte, Vagelos College of Physicians and Surgeons | Biomedical Informatics
Samuel Sia, Columbia Engineering | Biomedical Engineering
Nicholas Tatonetti, Vagelos College of Physicians and Surgeons | Biomedical Informatics, Systems Biology and Medicine
Harris Wang, Vagelos College of Physicians and Surgeons | Systems Biology, and Pathology and Cell Biology
Chaolin Zhang, Vagelos College of Physicans and Surgeons | Systems Biology, and Biochemistry and Molecular Biophysics

AFFILIATED MEMBERS

David Albers, Vagelos College of Physicians and Surgeons | Biomedical Informatics
Dimitris Anastassiou, Columbia Engineering | Electrical Engineering
Andrea Baccarelli, Mailman School of Public Health | Environmental Health Sciences
Suzanne Bakken, School of Nursing and Vagelos College of Physicians and Surgeons | Biomedical Informatics
Elias Bareinboim, Columbia Engineering | Computer Science
Peter Bearman, Arts and Sciences | Sociology
Maura Boldrini, Vagelos College of Physicians and Surgeons | Psychiatry
Lewis M. Brown, Arts and Sciences | Biological Sciences
Carri Chan, Graduate School of Business
Jan Claassen, Vagelos College of Physicians and Surgeons | Neurology
Noemie Elhadad, Vagelos College of Physicians and Surgeons | Biomedical Informatics
Steven Ellis, Vagelos College of Physicians and Surgeons | Psychiatry
Steven Feiner, Columbia Engineering | Computer Science
Julio Fernandez, Arts and Sciences | Biological Sciences
Sandro Galea, Mailman School of Public Health | Epidemiology
Linda V. Green, Graduate School of Business
Christine Hendon, Columbia Engineering | Electrical Engineering
Elizabeth Hillman, Columbia Engineering | Biomedical Engineering
George Hripcsak, Vagelos College of Physicians and Surgeons | Biomedical Informatics
R. Stanley Hum, Vagelos College of Physicians and Surgeons | Pediatrics
Iuliana Ionita-Laza, Mailman School of Public Health | Biostatistics
Joshua Jacobs, Columbia Engineering | Biomedical Engineering
Marianthi-Anna Kioumourtzoglou, Mailman School of Public Health | Environmental Health Sciences
David Knowles, Columbia Engineering | Computer Science
Samory Kpotufe, Arts and Sciences | Statistics
Paul Kurlansky, Vagelos College of Physicians and Surgeons | Surgery
Elaine Larson, School of Nursing
Joel E. Lavine, Vagelos College of Physicians and Surgeons | Pediatrics
Aurel A. Lazar, Columbia Engineering | Electrical Engineering
Guohua Li, Vagelos College of Physicians and Surgeons | Anesthesiology and Mailman School of Public Health | Epidemiology
Frank R. Lichtenberg, Graduate School of Business
Soojin Park, Vagelos College of Physicians and Surgeons | Neurology
Rimma Perotte, Vagelos College of Physicians and Surgeons | Biomedical Informatics
Lynn Petukhova, Vagelos College of Physicians and Surgeons | Dermatology
Molly Przeworski, Arts and Sciences | Biological Sciences and Vagelos College of Physicians and Surgeons | Systems Biology
Raul Rabadan, Vagelos College of Physicians and Surgeons | Systems Biology
Kai Ruggeri, Mailman School of Public Health | Health Policy and Management
Ansaf Salleb-Aouissi, Columbia Engineering | Computer Science
Catherine Schevon, Vagelos College of Physicians and Surgeons | Neurology
Katharina Schultebraucks, Vagelos College of Physicians and Surgeons | Emergency Medicine
Jeffrey Shaman, Mailman School of Public Health | Environmental Health Sciences
Yufeng Shen, Vagelos College of Physicians and Surgeons | Systems Biology and Biomedical Informatics
Jeanette Stingone, Mailman School of Public Health | Epidemiology
Brent Stockwell, Arts and Sciences | Biological Sciences and Chemistry
Christian S. Stohler, School of Dental Medicine
Simon Tavaré, Arts and Sciences | Statistics and Biological Sciences
Raju Tomer, Arts and Sciences | Biological Sciences
Max Topaz, School of Nursing
Van-Anh Truong, Columbia Engineering | Industrial Engineering & Operations Research
Dennis Vitkup, Vagelos College of Physicians and Surgeons | Systems Biology and Biomedical Informatics
Melanie Wall, Mailman School of Public Health | Biostatistics
Yuanjia Wang, Mailman School of Public Health | Biostatistics
Chunhua Weng, Vagelos College of Physicans and Surgeons | Biomedical Informatics
Chris Wiggins, Columbia Engineering | Applied Physics and Applied Mathematics and Vagelos College of Physicians and Surgeons | Systems Biology
Sunmoo Yoon, Vagelos College of Physicans and Surgeons | Medicine

CURRENT AND PREVIOUS PROJECTS

      1. Social media sites such as Twitter and Facebook, as well as more specialized sites such as Yelp, host massive amounts of content by users about their real-life experiences and opinions. This effort, in collaboration with the New York City Department of Health and Mental Hygiene (NYC DOHMH), focuses on the detection of disease outbreaks in New York City restaurants. The goal of the project is to identify and analyze the unprecedented volumes of user-contributed opinions and comments about restaurants on social media sites, to extract reliable indicators of otherwise-unreported disease outbreaks associated with the restaurants. The NYC DOHMH analyzes these indicators, as they are produced, to decide when additional action is merited. This project is developing non-traditional information extraction technology --over redundant, noisy, and often ungrammatical text-- for a public health task of high importance to society at large.

      1. Cancer is an individual disease—unique in how it develops and behaves in every patient. Systematic characterization of cancer genomes has revealed a staggering complexity and heterogeneity of aberrations among individuals. More recently appreciated that intra-tumor heterogeneity is of critical importance, each tumor harboring sub-populations that vary in clinically important phenotypes such as drug sensitivity. We use genomic technologies to track tumor response to drug and develop computational machine learning algorithms to piece together an understanding of this data deluge towards personalized cancer care. We methods focus on questions such as (1) Identify the genetic determinants of cancer and drug resistance. (2) Model how these aberrations lead tumor networks to go awry, arming the cancer with ability to abnormally grow, metastasize and evade drugs. (3) Understand what part of the tumor network to target by identifying tumor vulnerabilities and potential synergy of drug combinations. (4) Characterize tumor heterogeneity, including drug resistant and tumor initiating subpopulations. Treatment that is based not only on understanding which components go wrong, but also how these go wrong in each individual patient, will improve cancer therapeutics.

      1. Clinicians in the Neuro-ICU may be confronted daily by over 200 time-related variables for each patient; yet we know from cognitive science that people are only able to understand the relatedness of 2 variables without help. We are investigating how to help clinicians make sense of real-time streams of physiological data as well as of their relationships and trends. The objective of this project is to demonstrate that interactive data visualizations designed to transform and consolidate complex multimodal physiological data into integrated interactive displays will reduce clinician cognitive load and will result in reductions in medical error and improvements in patient care, safety, and efficiency. This project is a collaboration between Dr. J. Michael Schmidt in Neurology, Division of Critical Care and Draper Laboratory. It is funded by the DoD Telemedicine & Advanced Technology Research Center (TATRC) and the Dana Foundation.

      1. Physicians treating patients in the clinic, on the floor, or in the emergency room are faced with an overwhelming amount of complex information about their patients, with little time to review it. HARVEST is an interactive patient record summarization system, which aims to support physicians in their information workflow. It extracts content from the patient notes, where key clinical information resides, aggregates and presents information through time. HARVEST is currently deployed at NewYork-Presbyterian hospital. It relies on a distributed platform for processing data as they get pushed into the electronic health record. We are now investigating summarization models of patient records that identify their co-morbidities and their status through time, by modeling all observations in the record, from the notes to laboratory test measurements and other structured information like billing codes. This project is a collaboration between Dr. Noémie Elhadad in Biomedical Informatics, Dr. Chris Wiggins in Applied Physics and Applied Mathematics, and NewYork-Presbyterian hospital.


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