Key result
AdaBoost model using NLP of clinical narratives predicts ACS in ED patients with ~94% accuracy.
Why the study?
ACS remains a leading cause of mortality and morbidity, motivating a reliable prediction framework to serve as a diagnostic support tool to prevent misdiagnoses in chest pain patients.
Does a hybrid machine learning and natural language processing model accurately predict acute coronary syndrome in patients presenting to the emergency department with chest pain?
Observational (n=16,096)
No
Does a hybrid machine learning and natural language processing model accurately predict acute coronary syndrome in patients presenting to the emergency department with chest pain?
Effect estimate: F1-score 0.943
A hybrid machine learning model using natural language processing of clinical narratives can accurately predict acute coronary syndrome in emergency department patients presenting with chest pain.
May enhance ED ACS triage; hypothesis-generating and requires prospective validation before adoption.
Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity. Predicting the associated risks of patients with chest pain using electronic health record data can help identify those needing more tailored care. This study proposes the development of a reliable prediction framework to serve as a diagnostic support tool for preventing misdiagnoses among patients with clinical concerns for ACS. Data were collected from an urban, demographically diverse hospital in Detroit, Michigan, for patients presenting to the emergency department (ED) with primary chief complaints of chest pain from January 2017 to August 2020. This study incorporated term frequency-inverse document frequency features from free-text summaries, which contain anecdotal symptom descriptions and are among the first data points provided upon entering the ED. The analysis included 16,096 patients with clinical concerns for ACS and trained three machine learning models, logistic regression, AdaBoost, and linear discriminant analysis, across different data processing stages to predict patients with ACS from non-ACS etiology. The AdaBoost model outperformed the other two models with an accuracy of 94% and an F1-score of 0.943 in predicting ACS on the testing data. This study identified key independent factors from patient demographics, comorbidities, and clinical narrative data that predicted ACS in patients. The prediction framework can serve as a decision-support tool to classify ACS and inform physicians about better ACS risk factors.
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Emakhu et al. (2023) conducted an observational in Acute coronary syndrome (n=16,096). AdaBoost machine learning model vs. Logistic regression and linear discriminant analysis was evaluated on Predicting patients with ACS from non-ACS etiology (F1-score 0.943). An AdaBoost machine learning model incorporating natural language processing of clinical narratives predicted acute coronary syndrome in emergency department patients with 94% accuracy.
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