Human pluripotent stem cell-based in vitro models that reflect human physiology have the potential to reduce the number of drug failures in clinical trials and offer a cost-effective approach for assessing chemical safety. Here, human embryonic stem (ES) cell-derived neural progenitor cells, endothelial cells, mesenchymal stem cells, and microglia/ macrophage precursors were combined on chemically defined polyethylene glycol hydrogels and cultured in serum-free medium to model cellular interactions within the developing brain. The precursors self-assembled into 3D neural constructs with diverse neuronal and glial populations, interconnected vascular networks, and ramified microglia. Replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed neurogenesis, vasculature development, and microglia genes. Linear support vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constructs treated with 34 toxic and 26 nontoxic chemicals. The predictive model was evaluated using two standard hold-out testing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an unbiased blinded trial using a single holdout set of 10 additional chemicals. The linear support vector produced an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 of 10 chemicals in the blinded trial.organoid | machine learning | tissue engineering | differentiation | toxicology T here is a pressing need for improved methods to assess the safety of drugs and other compounds (1-5). Success rates for drug approval are declining despite higher research and development spending (6), and clinical trials often fail due to toxicities that were not identified through animal testing (7). In addition, most of the chemicals in commerce have not been rigorously assessed for safety despite growing concerns over the potential impact of industrial and environmental exposures on human health (2-5). Animal models are costly, time consuming, and fail to recapitulate many aspects of human physiology, which has motivated agencies such as the National Institutes of Health (NIH) and the US Environmental Protection Agency (EPA) to initiate programs that emphasize human cellular approaches for assessing the safety of drugs (1) and environmental chemicals (2, 3). In vitro cellular models that accurately reflect human physiology have the potential to improve the prediction of drug toxicity early in the development pipeline (1) and would provide a cost-effective approach for testing other sources of chemical exposure, including food additives, cosmetics, pesticides, and industrial chemicals (2-5).The human brain is particularly sensitive to toxic insults during development and early childhood (8), and there is growing concern that exposure to environmental chemicals may be linked to the rising incidence of neurodevelopmental disorders worldwide (4). Human brain development is mediated by highly coordinated cellular interactions between functionally ...

Background: Machine learning is increasingly used for risk stratification in healthcare. Achieving accurate predictive models does not improve outcomes if they cannot be translated into efficacious intervention. Here we examine the potential utility of an automated risk-stratification and referral intervention to screen older adults for fall risk after ED visits. Objective: This study evaluated several machine learning methodologies for the creation of a risk stratification algorithm using electronic health record (EHR) data, and estimated the effects of a resultant intervention based on algorithm performance in test data. Methods: Data available at the time of ED discharge was retrospectively collected and separated into training and test datasets. Algorithms were developed to predict the outcome of return visit for fall within 6 months of an ED index visit. Models included random forests, AdaBoost, and regression-based methods. We evaluated models both by area under the receiver operating

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Background/objectives: Despite a high prevalence and association with poor outcomes, screening to identify cognitive impairment (CI) in the emergency department (ED) is uncommon. Identification of high-risk subsets of older adults is a critical challenge to expanding screening programs. We developed and evaluated an automated screening tool to identify a subset of patients at high risk for CI. Methods:In this secondary analysis of existing data collected for a randomized control trial, we developed machine-learning models to identify patients at higher risk of CI using only variables available in electronic health record (EHR). We used records from 1736 community-dwelling adults age > 59 being discharged from three EDs. Potential CI was determined based on the Blessed Orientation Memory Concentration (BOMC) test, administered in the ED. A nested cross-validation framework was used to evaluate machine-learning algorithms, comparing area under the receiver-operator curve (AUC) as the primary metric of performance.Results: Based on BOMC scores, 121 of 1736 (7%) participants screened positive for potential CI at the time of their ED visit. The best performing algorithm, an XGBoost model, predicted BOMC positivity with an AUC of 0.72. With a classification threshold of 0.4, this model had a sensitivity of 0.73, a specificity of 0.64, a negative predictive value of 0.97, and a positive predictive value of 0.13. In a hypothetical ED with 200 older adult visits per week, the use of this model would lead to a decrease in the in-person screening burden from 200 to 77 individuals in order to detect 10 of 14 patients who would fail a BOMC. Conclusion:This study demonstrates that an algorithm based on EHR data can define a subset of patients at higher risk for CI. Incorporating such an See related editorial by Hirshon in this issue.