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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
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					<year>2026</year>
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					<volume>6</volume>
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				<issue>1</issue>
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					<title>Patient Safety Narratives as Structured Evidence: A Root-Cause Theme Extraction Framework for Learning Systems</title>
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            <given_name>Ali</given_name>
            <surname>Rezaei</surname>
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            <given_name>Hossein</given_name>
            <surname>Karimi</surname>
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					  <unstructured_citation>Denecke K. Evaluating large language models for analysing safety risks in healthcare incident reports. Stud Health Technol Inform. 2025;329:386-90.</unstructured_citation>
						 <doi>10.3233/SHTI250867</doi> 					</citation>
          					<citation key="rk-10.68159/t418605867-d767350c-6f9b-4ef3-832b-94599eb277d5">
					  <unstructured_citation>Young IJB, Luz S, Lone N. A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. Int J Med Inform. 2019;132:103971.</unstructured_citation>
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					  <unstructured_citation>Wang Y, Coiera E, Runciman W, Magrabi F. Can unified medical language system-based semantic representation improve automated identification of patient safety incident reports by type and severity? J Am Med Inform Assoc. 2020;27(10):1502-9.</unstructured_citation>
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					  <unstructured_citation>Evans HP, Anastassiou A, Edwards A, Hibbert P, Makeham M, Luzio S, et al. Automated classification of primary care patient safety incident report content and severity using supervised machine learning approaches. Health Inform J. 2020;26(4):3123-39.</unstructured_citation>
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					  <unstructured_citation>Denecke K. Concept-based retrieval from critical incident reports. Stud Health Technol Inform. 2017;236:1-7.</unstructured_citation>
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					  <unstructured_citation>Lear R, Godfrey C, O’Dowd H, Lear M, O’Dowd C. Co-producing a safe mobility and falls informatics platform to drive meaningful quality improvement in the hospital setting: a mixed-methods protocol for the insightFall study. BMJ Open. 2025;15(2):e082053.</unstructured_citation>
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					  <unstructured_citation>Chen H, Fong A, Ratwani RM. A machine learning approach with human-AI collaboration for automated classification of patient safety event reports: algorithm development and validation study. JMIR Hum Factors. 2024;11:e53378.</unstructured_citation>
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					  <unstructured_citation>Fong A, Ratwani RM. A machine learning approach to reclassifying miscellaneous patient safety event reports. J Patient Saf. 2021;17(8):e829-e833.</unstructured_citation>
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					  <unstructured_citation>Boxley C, Fujimoto M, Ratwani RM. A text mining approach to categorize patient safety event reports by medication error type. Sci Rep. 2023;13(1):18388.</unstructured_citation>
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					  <unstructured_citation>Islam S, Chen H, Cohen E, Wilson D, Alfred M. Evaluating active learning strategies for automated classification of patient safety event reports in hospitals. Proc Hum Factors Ergon Soc Annu Meet. 2024;68(1):465-9.</unstructured_citation>
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					  <unstructured_citation>Fong A, Howe JL, Adams KT, Ratwani RM. Identifying health information technology related safety event reports from patient safety event report databases. J Biomed Inform. 2018;86:135-42.</unstructured_citation>
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					  <unstructured_citation>Fong A, Ratwani RM. Using active learning to identify health information technology related patient safety events. Appl Clin Inform. 2017;8(1):35-46.</unstructured_citation>
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					  <unstructured_citation>Adadey A, Chou W, Drury L. Developing an analytical pipeline to classify patient safety event reports using optimized predictive algorithms. Methods Inf Med. 2021;60(5-6):147-61.</unstructured_citation>
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					  <unstructured_citation>Tabaie A, Sengupta S, Pruitt ZM, Fong A. A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ Open Qual. 2023;12(2):e002188.</unstructured_citation>
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					  <unstructured_citation>Liang C, Zhou S, Yao B, Hood D, Gong Y. Toward systems-centered analysis of patient safety events: improving root cause analysis by optimized incident classification and information presentation. Int J Med Inform. 2020;135:104053.</unstructured_citation>
						 <doi>10.1016/j.ijmedinf.2019.104053</doi> 					</citation>
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					  <unstructured_citation>Chen K, Rogers S, Yurkofsky M, Young J, Chao S, Bates DW, et al. AI-driven analysis of patient safety reports using large language models: an exploratory multiple methods study. BMJ Qual Saf. 2025.</unstructured_citation>
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