NLP model outperforms ICD-10 codes for detecting bleeding events


 Accurately tracking adverse events in hospitals is notoriously difficult when relying solely on billing codes. A study in JMIR Medical Informatics pitted a Natural Language Processing (NLP) model against standard ICD-10 coding to detect bleeding events in patient discharge summaries. The NLP model was trained to "read" the unstructured text of doctor's notes rather than just looking for pre-assigned codes.

The findings were clear: the NLP model significantly outperformed the traditional coding method, identifying true bleeding events that the billing codes missed. This has major implications for hospital safety surveillance and clinical research, as it proves that relying on administrative data alone likely underestimates the true rate of complications. The authors argue that NLP should become a standard layer in quality assurance workflows.

Read the original article at: https://medinform.jmir.org/2025/1/e67837


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