Medical prognosis based on patient similarity and expert feedback

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.