Modernization of the healthcare sector has led to the introduction of wider and newer varieties of medical devices in hospitals. Consequently, there are increasing numbers of infectious complications related to medical devices. However, managing and monitoring the risk of medical devices are difficult and costly. The hospitals and the healthcare device service providers require effective means to manage the healthcare device maintenance to provide better patient care. To address this issue, we propose a data mining pipeline to classify medical devices based on mortality rates and ICD-10 codes. We utilize the decision tree grouping method to build a connection between the mortality dataset and ICD-10 codes. We anticipate that the results of this study will assist with healthcare providers identify risks associated with medical devices based on how many deaths are caused due to the improper use or use of faulty medical instruments during the treatment.
Burkul, Prapti; Umapathy, Karthikeyan; Asaithambi, Asai; and Huang, Haiyan, "Data Mining Pipeline for Performing Decision Tree Analysis On Mortality Dataset With ICD-10 Codes" (2020). SAIS 2020 Proceedings. 28.