Medical prognosis based on patient similarity and expert feedback

Fei Wang, Jianying Hu, Jimeng Sun

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

Abstract

Prognosis refers to the prediction of the future health status of a patient. Providing prognostic insight to clinicians is critical for physician decision support. In this paper we present a collaborative disease prognosis strategy leveraging the information of the clinically similar patient cohort, using a Local Spline Regression (LSR) based similarity measure. To improve the reliability of the approach, the algorithm can also incorporate physician's feedback in the form of whether the patients in a retrieved cohort are indeed similar to the query patient. The proposed methodology was tested on a real clinical data set containing records of over two hundred thousand patients over three years. We report the retrieval as well as prognosis performance to demonstrate the effectiveness of the system.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages1799-1802
Number of pages4
StatePublished - Dec 1 2012
Externally publishedYes
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1211/15/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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