Work

MedCV: An Interactive Visualization System for Patient Cohort Identification from Medical Claim Data

Year: 2022

Type: conference-paper

Abstract: Healthcare providers generate a medical claim after every patient visit. A medical claim consists of a list of medical codes describing the diagnosis and any treatment provided during the visit. Medical claims have been popular in medical research as a data source for retrospective cohort studies. This paper introduces a medical claim visualization system (MedCV) that supports cohort selection from medical claim data. MedCV was developed as part of a design study in collaboration with clinical researchers and statisticians. It helps a researcher to define inclusion rules for cohort selection by revealing relationships between medical codes and visualizing medical claims and patient timelines. Evaluation of our system through a user study indicates that MedCV enables domain experts to define high-quality inclusion rules in a time-efficient manner.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3511808.3557157

Authors: Ashis Kumar Chanda, Brian L. Egleston, Tian Bai, Slobodan Vucetic

Institutions: Temple University, Fox Chase Cancer Center

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Cancer Institute

Awards: Comprehensive Cancer Center Program at Fox Chase, Deep learning for representation of codes used for SEER-Medicare claims research