Physics of the Medical Record

Hripcsak, George

Title:

Physics of the Medical Record

dc.contributor.author	Hripcsak, George
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing	en_US
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Data Engineering and Science	en_US
dc.contributor.corporatename	Columbia University. Dept. of Biomedical Informatics	en_US
dc.date.accessioned	2017-03-22T19:54:11Z
dc.date.available	2017-03-22T19:54:11Z
dc.date.issued	2017-03-16
dc.description	Presented on March 16, 2017 at 2:30 p.m. in the TSRB Auditorium.	en_US
dc.description	George Hripcsak, MD, MS, is Vivian Beaumont Allen Professor and Chair of Columbia University’s Department of Biomedical Informatics and Director of Medical Informatics Services for NewYork-Presbyterian Hospital/Columbia Campus. Dr. Hripcsak’s current research focus is on the clinical information stored in electronic health records and on the development of next-generation health record systems. Using nonlinear time series analysis, machine learning, knowledge engineering, and natural language processing, he is developing the methods necessary to support clinical research and patient safety initiatives.	en_US
dc.description	Runtime: 61:31 minutes	en_US
dc.description.abstract	Dr. George Hripcsak will discuss the national push for electronic health records that will make an unprecedented amount of clinical information available for research, with approximately one billion patient visits documented per year in the US. These data may lead to discoveries that improve understanding of biology, aid the diagnosis and treatment of disease, and permit the inclusion of diverse populations and rare diseases. Health record data also bring challenges, with missing and inaccurate data and substantial bias. Some of the bias comes about because the data are generated as part of the health care process. For example blood tests done at night select for patients who are sicker. We employ nonlinear time series methods borrowed from other fields to address the challenges, and we attempt to model and correct for biases due to the health care process. We incorporate mechanistic models to constrain the search space to create accurate predictions despite limited training sets and missing values.	en_US
dc.format.extent	61:31 minutes
dc.identifier.uri	http://hdl.handle.net/1853/56542
dc.language.iso	en_US	en_US
dc.relation.ispartofseries	Computational Health Distinguished Lecture
dc.subject	Data mining	en_US
dc.subject	Electronic health records	en_US
dc.title	Physics of the Medical Record	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	College of Computing
local.relation.ispartofseries	Computational Health Distinguished Lecture Series
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication	63e6facf-2e47-484f-8ca4-fabfa3d60564