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The hospitalized safety environment of patients is a priority-concerned by the healthcare services for ensuring patient safety and healthcare quality during inpatient admission in hospitals. Particularly, the applications of accident event aviso system (AEAS) and knowledge discovery for identifying accident event aviso category (AEAC) will have assistance to determine potential hazards and reduce medical errors to enhance patient safety and healthcare quality issues in such a complexity healthcare system of hospitals and are vitally important in the healthcare industry. Thus, this work proposes a hybrid procedure to highlight such an interesting issue. The proposed procedure constitutes five components, expert feature-screening approach, the C4.5 algorithm of decision tree, a cumulative probability distribution approach, the LEM2 algorithm of rough sets, and a rule filter technique. An experimental dataset was retrieved from the AEAS of existing hospital databases. The results from implemented experiments indicate that the proposed procedure is capable of effects to remove a redundant attribute, achieve an improved classification performance, offer tools based on the knowledge of aviso system, and provide sufficient information to solving AEAC problems encountered by patients and hospitals, thereby benefiting interested parties.

Extracting Accident Event Aviso Rules by Soft Computing Techniques in the Healthcare Industry