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

Su-Jin Shin, Je-Yong Oh, Sungrae Park, Minki Kim, and Il-Chul Moon, Hierarchical Prescription Pattern Analysis with Symptom Labels, Workshop on Biological Data Mining and Its Applications in Healthcare, International Conference on Data Mining (ICDM Workshop 2020), Atlantic City, New Jersey, USA, Nov 14-17, 2015

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 : 

https://ieeexplore.ieee.org/document/7395669