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Automatic Identification of Co-Occuring Patient Events

Published: 02 October 2016 Publication History

Abstract

With the explosion of data in healthcare, there is a growing need to develop intelligent methods for automatically mining and implementing analyses from these data. In clinical applications, longitudinal patient records are often stored in disparate systems or locations in a non-integreted manner, adding work to providers and researchers to effectively utilize the information. Recent work with treatment recommendation systems has begun to help clinicians overcome these challenges, but there remains a need for concise synthesis, abstraction, and presentation of this temporal information. We present a method for unsupervised classification of common co-occuring medication administration and patient surgical events in electronic medical record data using vector-space analysis and unsupervised cluster classification. This work was done independent of domain expertice and demonstrates a method of identifying co-occuring patient events.

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cover image ACM Conferences
BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
October 2016
675 pages
ISBN:9781450342254
DOI:10.1145/2975167
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 October 2016

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Author Tags

  1. EMR
  2. Longitudinal Data
  3. Structured Data
  4. Unsupervised Classification
  5. Vector-Space Models

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  • (2018)Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid ReviewJCO Clinical Cancer Informatics10.1200/CCI.18.00010(1-16)Online publication date: Dec-2018
  • (2018)Automated Strategic Prioritization Matchmaking Tool to Facilitate Federal–Community Adaptation ImplementationJournal of Water Resources Planning and Management10.1061/(ASCE)WR.1943-5452.0000994144:12Online publication date: Dec-2018

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