Medical data mining for early deterioration warning in general hospital wards

Yi Mao, Yixin Chen, Gregory Hackmann, Minmin Chen, Chenyang Lu, Marin Kollef, Thomas C. Bailey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

Data mining on medical data has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Every year, 4-17% of patients undergo cardiopulmonary or respiratory arrest while in hospitals. Early prediction techniques have become an apparent need in many clinical area. Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, based on data mining technology, we propose an early warning system (EWS) designed to identify the signs of clinical deterioration and provide early warning for serious clinical events. Our EWS is designed to provide reliable early alarms for patients at the general hospital wards (GHWs). EWS automatically identifies patients at risk of clinical deterioration based on their existing electronic medical record. The main task of EWS is a challenging classification problem on high-dimensional stream data with irregular, multi-scale data gaps, measurement errors, outliers, and class imbalance. In this paper, we propose a novel data mining framework for analyzing such medical data streams. The framework addresses the above challenges and represents a practical approach for early prediction and prevention based on data that would realistically be available at GHWs. We assess the feasibility of the proposed EWS approach through retrospective study that includes data from 28,927 visits at a major hospital. Finally, we apply our system in a real-time clinical trial and obtain promising results. This project is an example of multidisciplinary cyber-physical systems involving researchers in clinical science, data mining, and nursing staff in the hospital. Our early warning algorithm shows promising result: the transfer of patients to ICU was predicted with sensitivity of 0.4127 and specificity of 0.950 in the real time system.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages1042-1049
Number of pages8
DOIs
StatePublished - 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 11 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Country/TerritoryCanada
CityVancouver, BC
Period12/11/1112/11/11

Keywords

  • Bootstrap aggregating
  • EMA (exponential moving average)
  • Early warning system
  • Exploratory undersampling
  • Logistic regression

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