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Yang X, Huang W, Zhao W, Zhou X, Shi N, Xia Q. Exploring Acute Pancreatitis Clinical Pathways Using a Novel Process Mining Method. Healthcare (Basel) 2023; 11:2529. [PMID: 37761726 PMCID: PMC10531471 DOI: 10.3390/healthcare11182529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/05/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
Mining process models of medical behavior from electronic medical records is an effective way to optimize clinical pathways. However, clinical medical behavior is an extremely complex field with high nonlinearity and variability, and thus we need to adopt a more effective method. In this study, we developed a fuzzy process mining method for complex clinical pathways. Firstly, we designed a multi-level expert classification system with fuzzy values to preserve finer details. Secondly, we categorized medical events into long-term and temporary events for more specific data processing. Subsequently, we utilized electronic medical record (EMR) data of acute pancreatitis spanning 9 years, collected from a large general hospital in China, to evaluate the effectiveness of our method. The results demonstrated that our modeling process was simple and understandable, allowing for a more comprehensive representation of medical intricacies. Moreover, our method exhibited high patient coverage (>0.94) and discrimination (>0.838). These findings were corroborated by clinicians, affirming the accuracy and effectiveness of our approach.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China;
| | - Wei Huang
- Pancreatitis Center, Department of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu 610041, China;
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (W.Z.); (X.Z.)
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (W.Z.); (X.Z.)
| | - Na Shi
- Pancreatitis Center, Department of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu 610041, China;
| | - Qing Xia
- Pancreatitis Center, Department of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu 610041, China;
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Cuendet MA, Gatta R, Wicky A, Gerard CL, Dalla-Vale M, Tavazzi E, Michielin G, Delyon J, Ferahta N, Cesbron J, Lofek S, Huber A, Jankovic J, Demicheli R, Bouchaab H, Digklia A, Obeid M, Peters S, Eicher M, Pradervand S, Michielin O. A differential process mining analysis of COVID-19 management for cancer patients. Front Oncol 2022; 12:1043675. [PMID: 36568192 PMCID: PMC9768429 DOI: 10.3389/fonc.2022.1043675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/19/2022] [Indexed: 12/12/2022] Open
Abstract
During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care.
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Affiliation(s)
- Michel A. Cuendet
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States
| | - Roberto Gatta
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alexandre Wicky
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Camille L. Gerard
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- The Francis Crick Institute, London, United Kingdom
| | - Margaux Dalla-Vale
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Erica Tavazzi
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Grégoire Michielin
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Julie Delyon
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nabila Ferahta
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Cesbron
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sébastien Lofek
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Huber
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jeremy Jankovic
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Rita Demicheli
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Hasna Bouchaab
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antonia Digklia
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Michel Obeid
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Solange Peters
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Manuela Eicher
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Institute of Higher Education and Research in Health Care, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Sylvain Pradervand
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Olivier Michielin
- Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- *Correspondence: Olivier Michielin,
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