Automated Classification of Multi-Labeled Patient Safety Reports:A Shift from Quantity to Quality Measure
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.
Patient Safety Machine Learning
Chen Liang Yang Gong
Louisiana Tech University,Ruston,Louisiana,USA School of Biomedical Informatics,University of Texas Health Science Center,Houston,Texas,USA
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
1070-1074
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）