A system for mining temporal physiological data streams for advanced prognostic decision support

Jimeng Sun, Daby Sow, Jianying Hu, Shahram Ebadollahi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a mining system that can predict the future health status of the patient using the temporal trajectories of health status of a set of similar patients. The main novelties of this system are its use of stream processing technology for handling the incoming physiological time series data and incorporating domain knowledge in learning the similarity metric between patients represented by their temporal data. The proposed approach and system were tested using the MIMIC II database, which consists of physiological waveforms, and accompanying clinical data obtained for ICU patients. The study was carried out on 1500 patients from this database. In the experiments we report the efficiency and throughput of the stream processing unit for feature extraction, the effectiveness of the supervised similarity measure both in the context of classification and retrieval tasks compared to unsupervised approaches, and the accuracy of the temporal projections of the patient data.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages1061-1066
Number of pages6
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

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

Other

Other10th IEEE International Conference on Data Mining, ICDM 2010
CountryAustralia
CitySydney, NSW
Period12/14/1012/17/10

Keywords

  • Patient similarity
  • Physiological streams

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'A system for mining temporal physiological data streams for advanced prognostic decision support'. Together they form a unique fingerprint.

Cite this