Modelling patient states in intensive care patients
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2013
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| Online Access: | http://hdl.handle.net/1721.1/76985 |
| _version_ | 1826204491423154176 |
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| author | Kshetri, Kanak Bikram |
| author2 | Peter Szolovits and Rohit Joshi. |
| author_facet | Peter Szolovits and Rohit Joshi. Kshetri, Kanak Bikram |
| author_sort | Kshetri, Kanak Bikram |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T12:56:27Z |
| format | Thesis |
| id | mit-1721.1/76985 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:56:27Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/769852019-04-12T21:36:00Z Modelling patient states in intensive care patients Modeling evolution of patient state in ICU and response to medical interventions Kshetri, Kanak Bikram Peter Szolovits and Rohit Joshi. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 71-74). Extensive bedside monitoring in hospital Intensive Care Units (ICU) has resulted in a deluge of information on patient physiology. Consequently, clinical decision makers have to reason with data that is simultaneously large and high-dimensional. Mechanisms to compress these datasets while retaining their salient features are in great need. Previous work in this area has focused exclusively on supervised models to predict specific hazardous outcomes like mortality. These models, while effective, are highly specific and do not generalize easily to other outcomes. This research describes the use of non-parametric unsupervised learning to discover abstract patient states that summarize a patient's physiology. The resulting model focuses on grouping physiologically similar patients instead of predicting particular outcomes. This type of cluster analysis has traditionally been done in small, low-dimensional, error-free datasets. Since our real-world clinical dataset affords none of these luxuries, we describe the engineering required to perform the analysis on a large, high-dimensional, sparse, noisy and mixed dataset. The discovered groups showed cohesiveness, isolation and correspondence to natural groupings. These groups were also tested for enrichment towards survival, Glasgow Coma Scale values and critical heart rate events. In each case, we found groups which were enriched and depleted towards those outcomes. by Kanak Bikram Kshetri. M.Eng. 2013-02-14T15:35:19Z 2013-02-14T15:35:19Z 2011 2011 Thesis http://hdl.handle.net/1721.1/76985 825551809 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 74 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Kshetri, Kanak Bikram Modelling patient states in intensive care patients |
| title | Modelling patient states in intensive care patients |
| title_full | Modelling patient states in intensive care patients |
| title_fullStr | Modelling patient states in intensive care patients |
| title_full_unstemmed | Modelling patient states in intensive care patients |
| title_short | Modelling patient states in intensive care patients |
| title_sort | modelling patient states in intensive care patients |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/76985 |
| work_keys_str_mv | AT kshetrikanakbikram modellingpatientstatesinintensivecarepatients AT kshetrikanakbikram modelingevolutionofpatientstateinicuandresponsetomedicalinterventions |