the paper is under consideration at Pattern Recognition Letters. This is a workshop paper for a non-archival purpose without no proceeding publication.

Shin, S.-J., J.Y. Oh, S.R. Park, M.K. Kim, and I.-C. Moon. 2015. “Hierarchical Prescription Pattern Analysis with Symptom Labels.” In Workshop on Biological Data Mining and Its Applications in Healthcare, International Conference on Data Mining. Atlantic City, NJ.

Abstract : 

 Identifying the prescription patterns would be a useful and interesting goal from multiple perspectives. Firstly, the identified patterns could expand the horizon of the medical practice knowledge. Secondly, the identified prescription patterns can be evaluated by subject-matter experts to label some of the patterns as anomaly calling for further investigation, i.e., prescription costs for insurance companies. Recently, the Health Insurance Review & Assessment Service (HIRA), South Korea, released a dataset on about six millions prescriptions on sampled population over three years. This paper presents the statistical modeling details of Tag Hierarchical Topic Models (Tag-HTM) and the application of Tag-HTM to the HIRA dataset. The application of Tag-HTM revealed a hierarchical structure of medicine-symptom distributions, which would be a new information to medical practitioners given that previous disease classification was mainly done by the anatomical and the disease cause aspects. Also, Tag-HTM was able to isolate the prescription patterns with higher medical costs as a branch of hierarchical clustering, and this cluster would be a prescription collection of interests to subject-matter experts in the insurance companies.

@INPROCEEDINGS{7395669, 
author={S. J. Shin and J. Y. Oh and S. Park and M. Kim and I. C. Moon}, 
booktitle={2015 IEEE International Conference on Data Mining Workshop (ICDMW)}, 
title={Hierarchical Prescription Pattern Analysis with Symptom Labels}, 
year={2015}, 
pages={178-187}, 
keywords={data analysis;health care;medical administrative data processing;pattern classification;pattern clustering;HIRA dataset;Health Insurance Review and Assessment Service;South Korea;Tag-HTM;disease classification;hierarchical clustering;hierarchical prescription pattern analysis;insurance companies;medical practice knowledge;medicine-symptom distribution;prescription pattern identification;statistical modeling;symptom labels;tag hierarchical topic models;Analytical models;Data models;Databases;Insurance;Medical diagnostic imaging;Medical services;Probabilistic logic}, 
doi={10.1109/ICDMW.2015.138}, 
month={Nov},}

Source Website : 

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