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Diagnosis and appropriate intervention for myocardial infarction (MI) are time‐sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.

The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.

Sacrificial templates for patterning perfusable vascular networks in engineered tissues have been constrained in architectural complexity, owing to the limitations of extrusion-based 3D printing techniques. Here, we show that cell-laden hydrogels can be patterned with algorithmically generated dendritic vessel networks and other complex hierarchical networks by using sacrificial templates made from laser-sintered carbohydrate powders. We quantified and modulated gradients of cell proliferation and cell metabolism emerging in response to fluid convection through these networks and to diffusion of oxygen and metabolites out of them. We also show scalable strategies for the fabrication, perfusion culture and volumetric analysis of large tissue-like constructs with complex and heterogeneous internal vascular architectures. Perfusable dendritic networks in cell-laden hydrogels may help sustain thick and densely cellularized engineered tissues, and assist interrogations of the interplay between mass transport and tissue function. Cell-laden hydrogels can be patterned with algorithmically generated sacrificial dendritic vessel networks made of laser-sintered carbohydrate powders.

Interventions to better prevent or manage Clostridioides difficile infection (CDI) may significantly reduce morbidity, mortality, and healthcare spending.We present a retrospective study using electronic health record data from over 700 United States hospitals. A subset of hospitals was used to develop machine learning algorithms (MLAs); the remaining hospitals served as an external test set. Three MLAs were evaluated: gradient-boosted decision trees (XGBoost), Deep Long Short Term Memory neural network, and one-dimensional convolutional neural network. MLA performance was evaluated with area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, diagnostic odds ratios and likelihood ratios.The development dataset contained 13,664,840 inpatient encounters with 80,046 CDI encounters; the external dataset contained 1,149,088 inpatient encounters with 7,107 CDI encounters. The highest AUROCs were achieved for XGB, Deep Long Short Term Memory neural network, and one-dimensional convolutional neural network via abstaining from use of specialized training techniques, resampling in isolation, and resampling and output bias in combination, respectively. XGBoost achieved the highest AUROC.MLAs can predict future CDI in hospitalized patients using just 6 hours of data. In clinical practice, a machine-learning based tool may support prophylactic measures, earlier diagnosis, and more timely implementation of infection control measures.

The efficacy of implantable medical devices is limited by the longevity of devices in the body environment. Due to the aqueous and mobile-ion rich environment of tissue, robust and long-lasting encapsulation materials are critical for chronic implants. Assessing the reliability of medical devices is commonly performed through saline soak tests with reactive oxidative species at elevated temperatures and lifetime data are fit to an Arrhenius model to predict lifetime under physiological conditions. While effective, these systems often require frequent human involvement to maintain system temperature and reactive oxidative species concentration, as well as monitor sample lifetime, which makes long term testing of multiple samples difficult. Here we present an automated, low-cost, low-solution volume, and high-throughput reactive accelerated aging system to assay many thin film samples in an easy and low maintenance manner. The efficacy of up to 16 thin film coating samples can be assessed by our system through in-situ current leakage tests in a mock biological environment. We validate our system by aging thermal oxide and a-SiC thin films at 93 °C with 20 mM H 2 O 2 . Our system shows early failure of the thermal oxide compared to the a-SiC, in agreement with the current literature.

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.

Supplemental material, Supplementary_Materials for Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction by Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert, Anna Lynn-Palevsky, Sidney Le, Angier Allen, Emily Pellegrini, Abigail Green-Saxena, Christopher Barton, Grant Fletcher, Lisa Shieh, Philip B Stark, Uli Chettipally, David Shimabukuro, Mitchell Feldman and Ritankar Das in Health Informatics Journal

PurposeThis study evaluates a machine-learning-based mortality prediction tool.Materials and MethodsWe conducted a retrospective study with data drawn from three academic health centers. Inpatients of at least 18 years of age and with at least one observation of each vital sign were included. Predictions were made at 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated on hold-out test data from the same institution and data from the remaining institutions. Predictions were compared to those of qSOFA and MEWS using area under the receiver operating characteristic curve (AUROC).ResultsFor training and testing on data from a single institution, machine learning predictions averaged AUROCs of 0.97, 0.96, and 0.95 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, the algorithm achieved AUROC up to 0.95, 0.93, and 0.91, for 12-, 24-, and 48-hour predictions, respectively. MEWS and qSOFA had average 48-hour AUROCs of 0.86 and 0.82, respectively.ConclusionThis algorithm may help identify patients in need of increased levels of clinical care.