Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. Conclusions A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.

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Objective: Poor risk stratification of thyroid nodules by ultrasound has motivated the need for a deep learning-based approach for nodule segmentation. This paper demonstrates the effectiveness of a multitask approach to detect ultrasounds containing potential nodules and segment nodules on the suspected images. Methods: Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images. A novel anomaly detection (AD) module, to classify suspicious ultrasound images, was integrated with various state of the art segmentation architectures. The trained models were evaluated on a portion of the in-house dataset, as well as two external validation (EV) sets, to understand how the AD module affected segmentation performance. Results: The addition of AD to the architectures improved image-level nodule detection, evidenced by the increase in F1 scores and image-wide Dice similarity coefficient. Of the models with AD, MSUNet-AD had the highest F1 score of 0.829; however, there was a decrease in DSC on just images with nodules (DSC+) from 0.726 to 0.627. This drop was observed across all models when AD was added; however, closer analysis of DSC+by nodule size revealed that this difference was not significant in larger nodules, which are more likely to be clinically relevant. Finally, evaluation of MSUNet with and without AD on the EV datasets demonstrated comparable performance with the UCLA dataset. Conclusion: The proposed architecture is an automated multitask method that can both detect and segment nodules in ultrasound. Performance on the EV datasets demonstrates generalizability of the model.

Purpose Fractures in older adults are a significant cause of morbidity and mortality, particularly for post-menopausal women with osteoporosis. Prevention is key for managing fractures in this population and may include identifying individuals at high fracture risk and providing therapeutic treatment to mitigate risk. This study aimed to develop a machine learning fracture risk prediction tool to overcome the limitations of existing methods by incorporating additional risk factors and providing short-term risk predictions. Methods We developed a machine learning model to predict risk of major osteoporotic fractures and femur (hip) fractures in a retrospective cohort of post-menopausal women. Models were trained to generate predictions at 3, 5, and 10 year prediction windows. The model used only ICD codes, basic demographics, vital sign measurements, lab results and medication usage from a proprietary national longitudinal electronic health record repository to make predictions. Results The algorithms obtained area under the receiver operating characteristic values of 0.83, 0.81, and 0.79 for prediction of major osteoporotic fractures at 3, 5, and 10 year windows, respectively. The algorithms also obtained AUROC values of 0.79, 0.75, and 0.75 for prediction of femur fractures at 3, 5, and 10 year windows, respectively. For all models, when sensitivity was fixed at 0.80, average specificity was 0.615. Conclusion Machine learning clinical decision support may inform clinical efforts at early detection of high-risk individuals, mitigating their risk and for establishing clinical research cohorts with well-defined patient populations.

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