A machine learning model utilizing unstructured admission records achieved an AUC of 72% for MACE prediction in ACS patients, outperforming GRACE and TIMI risk scores.
Observational (n=2,930)
No
Does a machine learning model utilizing unstructured admission records improve MACE prediction compared to GRACE and TIMI scores in patients with ACS?
Machine learning models utilizing unstructured admission records can effectively predict MACE in ACS patients early in their hospitalization, outperforming traditional GRACE and TIMI risk scores.
Effect estimate: AUC 72%
BACKGROUND: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. METHODS: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. RESULTS: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. CONCLUSIONS: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations.
Hu et al. (Tue,) conducted a observational in Acute coronary syndrome (n=2,930). Machine learning models using admission records vs. GRACE and TIMI risk scores was evaluated on MACE prediction (AUC 72%). A machine learning model utilizing unstructured admission records achieved an AUC of 72% for MACE prediction in ACS patients, outperforming GRACE and TIMI risk scores.