Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.

Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.

Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes.

INTRODUCTION: To identify predictors of hypoglycemia and five other clinical and economic outcomes among treated patients with type 2 diabetes (T2D) using machine learning and structured data from a large, geographically diverse administrative claims database. METHODS: A retrospective cohort study design was applied to Optum Clinformatics claims data indexed on first antidiabetic prescription date. A hypothesis-free, Bayesian machine learning analytics platform (GNS Healthcare REFS™: Reverse Engineering and Forward Simulation) was used to build ensembles of generalized linear models to predict six outcomes defined in patients' 1-year post-index claims history, including hypoglycemia, antidiabetic class persistence, glycated hemoglobin (HbA1c) target attainment, HbA1c change, T2D-related inpatient admissions, and T2D-related medical costs. A unified set of 388 variables defined in patients' 1-year pre-index claims history constituted the set of predictors for all REFS models. RESULTS: The derivation cohort comprised 453,487 patients with a T2D diagnosis between 2014 and 2017. Patients with comorbid conditions had the highest risk of hypoglycemia, including those with prior hypoglycemia (odds ratio [OR] = 25.61) and anemia (OR = 1.29). Other identified risk factors included insulin (OR = 2.84) and sulfonylurea use (OR = 1.80). Biguanide use (OR = 0.75), high blood glucose (> 125 mg/dL vs. < 100 mg/dL, OR = 0.47; 100-125 mg/dL vs. < 100 mg/dL, OR = 0.53), and missing blood glucose test (OR = 0.40) were associated with reduced risk of hypoglycemia. Area under the curve (AUC) of the hypoglycemia model in held-out testing data was 0.77. Patients in the top 15% of predicted hypoglycemia risk constituted 50% of observed hypoglycemic events, 26% of T2D-related inpatient admissions, and 24% of all T2D-related medical costs. CONCLUSIONS: Machine learning models built within high-dimensional, real-world data can predict patients at risk of clinical outcomes with a high degree of accuracy, while uncovering important factors associated with outcomes that can guide clinical practice. Targeted interventions towards these patients may help reduce hypoglycemia risk and thereby favorably impact associated economic outcomes relevant to key stakeholders.