Predicting Patient's Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics

Shahram Ebadollahi, Jimeng Sun, David Gotz, Jianying Hu, Daby Sow, Chalapathy Neti

Research output: Contribution to journalArticlepeer-review

Abstract

Providing near-term prognostic insight to clinicians helps them to better assess the near-term impact of their decisions and potential impending events affecting the patient. In this work, we present a novel system, which leverages inter-patient similarity for retrieving patients who display similar trends in their physiological time-series data. Data from the retrieved patient cohort is then used to project patient data into the future to provide insights for the query patient. 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. In the experiments we report the effectiveness of the inter-patient similarity measure and the accuracy of the projection of patients' data. We also discuss the visual interface that conveys the near-term prognostic decision support to the user.

Original languageEnglish (US)
Pages (from-to)192-196
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2010
StatePublished - Jan 1 2010
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

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