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Open AccessArticle

Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan

1
Department of Industrial Management, National Taiwan University of Science and Technology (Taiwan Tech), Taipei 10607, Taiwan
2
Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24301, Taiwan
3
Department of Industrial and Business Management, Chang Gung University, Taoyuan 33302, Taiwan
4
Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
5
College of Medicine, National Taiwan University, Taipei 10051, Taiwan
6
College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2434; https://doi.org/10.3390/app9122434
Received: 20 May 2019 / Revised: 10 June 2019 / Accepted: 12 June 2019 / Published: 14 June 2019
(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)
Medication is designed to cure diseases, but serious risks can arise from severe adverse drug reactions (ADRs). ADRs can lead to emergency room visits and hospitalization, straining healthcare resources and, thus, they have strong implications for public health. Stevens–Johnson Syndrome (SJS) is one ADR and comprises the highest proportion of all drug relief cases in Taiwan. Pharmacovigilance involves the collection, detection, assessment, monitoring, and prevention of ADRs, including SJS. Most medical specialists are not fully aware of the risk of drug-induced SJS. Consequently, various drugs may be prescribed to susceptible patients for a great variety of diseases and, in turn, cause SJS. In this research, medical records of SJS patients were retrieved from the Taiwan National Health Insurance Research Database, and the Generalized Sequential Patterns (GSP) algorithm was used to find the sequential patterns of diseases before SJS onset. Then we mined the sequential patterns of medications prescribed in each disease pattern. Afterwards, we detected significant associations of each pattern of diseases and medications prescribed among age groups with statistical analysis. We found that, first, most patients developed SJS after being prescribed the causative medications fewer than four times. Second, Respiratory System Diseases (RSDs) appeared in disease sequential patterns of all lengths. Patterns involving RSDs were more frequent than others. Third, NSAIDs, H2-antagonists for peptic ulcer, penicillin antibiotics, theophylline bronchodilators, and cephalosporin antibiotics were the most frequent medications prescribed. Fourth, we found that patients in certain age groups had higher risks of developing SJS. This study aimed to mine the sequential patterns of diseases contracted and medications prescribed before patients developed SJS in Taiwan. This useful information can be provided to physicians so that they can stop the administration of suspected drugs to avoid evolution towards more severe cases. View Full-Text
Keywords: Stevens–Johnson Syndrome; sequential pattern mining; adverse drug reactions; healthcare; data mining Stevens–Johnson Syndrome; sequential pattern mining; adverse drug reactions; healthcare; data mining
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Ou-Yang, C.; Chou, S.-C.; Juan, Y.-C.; Wang, H.-C. Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan. Appl. Sci. 2019, 9, 2434.

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