Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Autor:	Dai HJ; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.; National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan.; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.; Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Chen CC; Electromagnetic Sensing Control and AI Computing System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan., Mir TH; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.; National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan., Wang TY; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.; National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan., Wang CK; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.; Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC.; Advanced Technology Laboratory, Chunghwa Telecom Laboratories, Taoyuan, Taiwan, ROC., Chang YC; National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan., Yu SJ; Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Shen YW; Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan., Huang CJ; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan., Tsai CH; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Wang CY; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Chen HJ; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Weng PS; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Lin YX; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan., Chen SW; Intelligent System Laboratory, Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan., Tsai MJ; Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Juang SF; Department of Medical Information, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan., Wu SY; Department of Medical Information, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan., Tsai WT; Department of Medical Information, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan., Huang MY; Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan.; Department of Radiation Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Huang CJ; Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan., Yang CJ; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.; Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan., Liu PZ; Health Promotion Administration, Ministry of Health and Welfare, Taipei 10341, Taiwan., Huang CW; Health Promotion Administration, Ministry of Health and Welfare, Taipei 10341, Taiwan., Huang CY; Health Promotion Administration, Ministry of Health and Welfare, Taipei 10341, Taiwan., Wang WYC; Waikato Management School, University of Waikato, Hamilton, New Zealand., Chong IW; Division of Chest Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan., Yang YH; National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Apr 07; Vol. 24, pp. 322-333. Date of Electronic Publication: 2024 Apr 07 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2024.04.007
Abstrakt:	Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes. Competing Interests: The authors declare no conflict of interest. (© 2024 The Authors.)
Databáze:	MEDLINE
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