Key result
Incorporating mental health information extracted from unstructured clinical notes into machine learning models improved mortality prediction, increasing average precision from 71% to 75%.
Why the study?
Critical social and behavioral health factors are often trapped in unstructured EHR notes, leading to an over-reliance on clinical data that may cause disparities in outcome predictions.
Does incorporating unstructured mental health data from EHRs improve mortality prediction in ICU patients compared to structured clinical data alone?
Observational (n=1,058)
No
Does incorporating unstructured mental health data from EHRs improve mortality prediction in ICU patients compared to structured clinical data alone?
Incorporating unstructured mental health data from clinical notes significantly improves the accuracy and robustness of machine learning models for predicting mortality in ICU patients.
Answers are grounded in this paper and cite their sources.
May enhance ICU mortality prediction via EHR notes; hypothesis-generating and requires prospective validation before adoption.
The integration of Electronic Health Records (EHRs) with Machine Learning (ML) models has become imperative in examining patient outcomes due to the vast amounts of clinical data they provide. However, critical information regarding social and behavioral factors that affect health, such as social isolation, stress, and mental health complexities, is often recorded in unstructured clinical notes, hindering its accessibility. This has resulted in an over-reliance on clinical data in current EHR-based research, potentially leading to disparities in health outcomes. This study aims to evaluate the impact of incorporating patient-specific context from unstructured EHR data on the accuracy and stability of ML algorithms for predicting mortality, using the MIMIC III database. Results from the study confirmed the significance of incorporating patient-specific information into prediction models, leading to a notable improvement in the discriminatory power and robustness of the ML algorithms. Furthermore, the findings underline the importance of considering non-clinical factors related to a patient’s daily life, in addition to clinical factors, when making predictions about patient outcomes. These results have significant ramifications for improving ML in clinical decision support and patient outcome predictions.
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Noaeen et al. (2023) conducted an observational in Critical illness (n=1,058). Inclusion of unstructured mental health data in machine learning models vs. Structured clinical data only was evaluated on Mortality prediction (Average Precision) (4% improvement). Incorporating mental health information extracted from unstructured clinical notes into machine learning models improved mortality prediction, increasing average precision from 71% to 75%.
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