{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:10:04Z","timestamp":1765357804061,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Over the past two decades, there have seen an ever-increasing amount of patient safety reports yet the capacity of extracting useful information from the reports remains limited. Classification of patient safety reports is the first step of performing a downstream analysis. In practice, the manual review processes for classification are labor-intense. Studies have shown that the reports are often mislabeled or unclassifiable based on the pre-defined categories, which presents a notable data quality problem. In this study, we investigated the multi-labeled nature of patient safety reports. We argue that understanding multi-labeled nature of reports is a key to disclose the complex relations between many components during the courses and development of medical errors. Accordingly, we developed automated multi-label text classifiers to process patient safety reports. The experiments demonstrated feasibility and efficiency of a combination of multi-label algorithms in the benchmark comparison. Grounded on our experiments and results, we provided suggestions on how to implement automated classification of patient safety reports in the clinical settings.<\/jats:p>","DOI":"10.3233\/978-1-61499-830-3-1070","type":"book-chapter","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T23:19:26Z","timestamp":1740266366000},"source":"Crossref","is-referenced-by-count":2,"title":["Automated Classification of Multi-Labeled Patient Safety Reports: A Shift from Quantity to Quality Measure"],"prefix":"10.3233","author":[{"family":"Liang Chen","sequence":"additional","affiliation":[]},{"family":"Gong Yang","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2017: Precision Healthcare through Informatics"],"original-title":[],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T23:35:07Z","timestamp":1740267307000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-829-7&spage=1070&doi=10.3233\/978-1-61499-830-3-1070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-830-3-1070","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2017]]}}}