Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.

Background: Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may provide a method for identifying these individuals, and could be automated to directly alert providers in real time. Objective: This study applies ML techniques to electronic health record (EHR) data to identify an integrated risk-prediction model that can be deployed to predict risk of drug-induced QT prolongation. Methods: We examined harmonized data from the UCHealth EHR and identified inpatients who had received a medication known to prolong the QT interval. Using a binary outcome of the development of a QTc interval >500 ms within 24 hours of medication initiation or no ECG with a QTc interval >500 ms, we compared multiple machine learning methods by classification accuracy and performed calibration and rescaling of the final model. Results: We identified 35,639 inpatients who received a known QT-prolonging medication and an ECG performed within 24 hours of administration. Of those, 4,558 patients developed a QTc > 500 ms and 31,081 patients did not. A deep neural network with random oversampling of controls was found to provide superior classification accuracy (F1 score 0.404; AUC 0.71) for the development of a long QT interval compared with other methods. The optimal cutpoint for prediction was determined and was reasonably accurate (sensitivity 71%; specificity 73%). Conclusions: We found that deep neural networks applied to EHR data provide reasonable prediction of which individuals are most susceptible to drug-induced QT prolongation. Future studies are needed to validate this model in novel EHRs and within the physician order entry system to assess the ability to improve patient safety.

3D computer-aided design, 3D printing and fabrication techniques can be applied to fabricate affordable, high-quality educational models for technical training that are capable of differentiating proficiency levels among users.

Men treated with androgen deprivation therapy for rising PSA after failed local therapy will often develop castrate resistance, and the appearance of metastases predicts a poor prognosis. Thus, researchers have long sought to prolong the onset of metastasis in patients with nonmetastatic castration-resistant prostate cancer (CRPC). Until 2018, patients in this group had no FDA-approved treatment options. They were typically managed with androgen-deprivation therapy (ADT) to maintain castrate systemic testosterone levels and given approved therapies for metastatic CRPC once metastases appeared. However, third-generation androgen receptor inhibitors (ARIs) have dramatically changed the treatment paradigm, having shown the ability to extend metastasis-free survival (MFS) significantly over ADT alone in Phase 3 trials. The newest of these, darolutamide, prolonged MFS 22 months over placebo while also improving a host of secondary and exploratory endpoints such as overall survival (OS), prostate-specific antigen (PSA) progression and time to pain progression, chemotherapy initiation, and symptomatic skeletal events. Among third-generation ARIs, darolutamide is unique in that it incorporates two pharmacologically active diastereomers and has demonstrated resistance to all known androgen receptor (AR) mutations. Additionally, patients taking darolutamide appear to experience comparatively few central nervous system-related adverse events (AEs) such as fatigue and falls, and no increases in seizures have been reported in the drug’s clinical or preclinical development. Various authors attribute the low incidence of CNS-related AEs to darolutamide’s minimal penetration of the blood–brain barrier (BBB). Other side effects ranging from hot flashes to hypothyroidism also occurred at rates similar to those of the placebo arm in Phase 3. As ADT in itself raises cardiovascular risk, the cardiovascular safety of third-generation antiandrogens as a category warrants continued scrutiny. In total, however, published data suggest that darolutamide provides a reasonable option for patients with nonmetastatic CRPC. Ongoing research will determine darolutamide’s potential role in additional disease states such as localized and castration-sensitive PCa.

Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.