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An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification.Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this allows for a clinically translatable model that aptly parallels the workflow for thyroid nodule assessment. The multitask approach is centered on an anomaly detection (AD) module that can be integrated with any U-Net architecture variant to improve image-level nodule detection. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9,888 images, and annotated by collaborating radiologists. Of the evaluated models, a multi-scale UNet (MSUNet) with AD achieved the highest F1 score of 0.829 and image-wide Dice similarity coefficient of 0.782 on our hold-out test set. Furthermore, models were evaluated on two external validations datasets to demonstrate generalizability and robustness to data variability. Ultimately, the proposed architecture is an automated multitask method that expands on previous methods by successfully both detecting and segmenting nodules in ultrasound.

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.

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.

Background Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from other disorders. We developed a machine learning algorithm for the prediction of MDS one year prior to clinical diagnosis of the disease. Methods Retrospective analysis was performed on 790,470 patients over the age of 45 seen in the United States between 2007 and 2020. A gradient boosted decision tree model (XGB) was built to predict MDS diagnosis using vital signs, lab results, and demographics from the prior two years of patient data. The XGB model was compared to logistic regression (LR) and artificial neural network (ANN) models. The models did not use blast percentage and cytogenetics information as inputs. Predictions were made one year prior to MDS diagnosis as determined by International Classification of Diseases (ICD) codes, 9th and 10th revisions. Performance was assessed with regard to area under the receiver operating characteristic curve (AUROC). Results On a hold-out test set, the XGB model achieved an AUROC value of 0.87 for prediction of MDS one year prior to diagnosis, with a sensitivity of 0.79 and specificity of 0.80. The XGB model was compared against LR and ANN models, which achieved an AUROC of 0.838 and 0.832, respectively. Conclusions Machine learning may allow for early MDS diagnosis MDS and more appropriate treatment administration.

Additional file 1: Fig. S1. Machine learning workflow. Table S1. Inputs used to train the machine learning algorithm. Table S2. International Classification of Diseases, Tenth Revision (ICD-10) codes used to identify emergency department encounters for acute heart failure. Table S3. Hyperparameter Optimization for the Top5F and 33F models. For each model, the first row lists the values of the optimal hyperparameters selected during model training. For each subsequent row, a single (bolded) hyperparameter is altered, and all other hyperparameters are unchanged.