AMIA Annu Symp Proc - Machine learning for risk prediction of acute coronary syndrome.

Tópicos

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Resumo

Acute coronary syndrome (ACS) accounts for 1.36 million hospitalizations and billions of dollars in costs in the United States alone. A major challenge to diagnosing and treating patients with suspected ACS is the significant symptom overlap between patients with and without ACS. There is a high cost to over- and under-treatment. Guidelines recommend early risk stratification of patients, but many tools lack sufficient accuracy for use in clinical practice. Prognostic indices often misrepresent clinical populations and rely on curated data. We used random forest and elastic net on 20,078 deidentified records with significant missing and noisy values to develop models that outperform existing ACS risk prediction tools. We found that the random forest (AUC = 0.848) significantly outperformed elastic net (AUC=0.818), ridge regression (AUC = 0.810), and the TIMI (AUC = 0.745) and GRACE (AUC = 0.623) scores. Our findings show that random forest applied to noisy and sparse data can perform on par with previously developed scoring metrics.

Resumo Limpo

acut coronari syndrom ac account million hospit billion dollar cost unit state alon major challeng diagnos treat patient suspect ac signific symptom overlap patient without ac high cost undertreat guidelin recommend earli risk stratif patient mani tool lack suffici accuraci use clinic practic prognost indic often misrepres clinic popul reli curat data use random forest elast net deidentifi record signific miss noisi valu develop model outperform exist ac risk predict tool found random forest auc signific outperform elast net auc ridg regress auc timi auc grace auc score find show random forest appli noisi spars data can perform par previous develop score metric

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