As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
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.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.