Developing a data mining approach to investigate association between physician prescription and patient outcome - a study on re-hospitalization in Stevens-Johnson Syndrome

Stevens-Johnson syndrome (SJS) is a potentially life-threatening skin reaction. Drugs are the major causes for cases of SJS. While treating patients with SJS, the first and most important step is to identify and discontinue any possible responsible drugs. However, potential drugs that may lead to SJS are many and encompass various therapeutic areas. Very few physicians are familiar with the potential risk of all these drugs. If properly treated, most SJS cases are expected to recover without much sequelae. All drugs that have been associated with SJS should be avoided in these patients to prevent recurrence. If the physicians fail to identify and discontinue the drugs causing SJS, or even adding new drugs related to SJS, the patient may get worse or SJS may recur. These conditions can cause SJS patients to be re-hospitalized. Currently the reasons for re-hospitalization of SJS patients in Taiwan are not known. This study uses Taiwan National Health Insurance Research Database to analyze the causes of re-hospitalization for cases of SJS. First, we classified prescription history of re-hospitalized patients through the rule-based classification method. Secondly, by using the basic prescription actions, we identified drug association patterns. Then, by employing A-priori algorithm, pairs of drugs with relatively higher frequency of appearance were identified and their degrees of association were measured by using selected symmetric and asymmetric association mining methods. Finally, by listing and ranking up these pairs of drugs according to the value of support based on their degrees of association, we provide prescribing physicians with possible means of increasing the awareness and reducing re-hospitalization of SJS patients.