文献类型：期刊文献

英文题名：Re-ordered fuzzy conformance checking for uncertain clinical records

作者：Zhang, Sicui[1,2,6];Genga, Laura[3];Dekker, Lukas[4];Nie, Hongchao[5];Lu, Xudong[2];Duan, Huilong[2];Kaymak, Uzay[6]

机构：[1]Shaoxing Univ, Sci & Technol Dept, Shaoxing, Peoples R China;[2]Zhejiang Univ, Sch Biomed Engn & Instrumental Sci, Hangzhou, Peoples R China;[3]Eindhoven Univ Technol, Sch Ind Engn, Eindhoven, Netherlands;[4]Catharina Hosp, Cardiol Dept, Eindhoven, Netherlands;[5]Philips Res, Eindhoven, Netherlands;[6]Eindhoven Univ Technol, Jheronimus Acad Data Sci, Eindhoven, Netherlands

年份：2024

卷号：149

外文期刊名：JOURNAL OF BIOMEDICAL INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:001150272000001)、、EI(收录号：20240115314670)、Scopus(收录号：2-s2.0-85181064128)、WOS

基金：This research has received funding from the Brain Bridge Project sponsored by Philips Research.

语种：英文

外文关键词：Process mining; Conformance checking; Fuzzy sets; Uncertainty; Clinical deviations; Event-log re-ordering

外文摘要：Modern hospitals implement clinical pathways to standardize patients' treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts' knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.