Key result
Adding clinical notes to structured data improves machine learning detection of coronary microvascular dysfunction.
Why the study?
Coronary microvascular dysfunction is a major cause of ischemia but is challenging to diagnose due to lack of CMD-specific screening measures.
Does integrating unstructured clinical narratives into machine learning models improve the detection of patients with coronary microvascular dysfunction compared to structured data alone?
Does integrating unstructured clinical narratives into machine learning models improve the detection of patients with coronary microvascular dysfunction compared to structured data alone?
Integrating unstructured clinical narratives into machine learning models significantly improves the detection of coronary microvascular dysfunction compared to relying on structured data alone.
While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 National Research Consensus Conference on Gender-Specific Research in Emergency Care. In this study, we utilized methods from machine learning that leverage structured and unstructured narratives in clinical notes to detect patients with CMD. We have shown that structured data are not sufficient to detect CMD and integrating unstructured data in the computational model boosts the performance significantly.
No takes yet. Share an insight, caveat, or question.
Fodeh et al. (2019) studied Coronary microvascular dysfunction (CMD). Machine learning model integrating structured and unstructured clinical notes vs. Structured data alone was evaluated on Detection of patients with CMD. Machine learning models integrating unstructured clinical notes significantly improved the detection of coronary microvascular dysfunction compared to using structured data alone.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: