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

Abstract

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.

Big and Deep Hype and Hope: On the Special Issue for Deep Learning and Big Data in Healthcare

“…In [4], daily questionnaires from mobile applications are shown to suffer from missing data, and they are completed with a principled approach based on long-term-short-term memory networks, with representative case applications given by their use for activity, food, and biometrics in athletes. In [5], adverse drug reactions are analyzed from medical records of patients with Stevens-Johnson Syndrome, using a generalized sequential pattern algorithm for early detection of their onset. In this work, the results provided with clinical information relevant for different medication groups, which can be useful for the involved physicians to stop the administration of suspected drugs in the early stages, are mined.…”