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Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.

Journal of the American College of Surgeons
BACKGROUND: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the ...

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Archives of toxicology
Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of ...

Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.

International journal of environmental research and public health
While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interaction...

Expanding the drug discovery space with predicted metabolite-target interactions.

Communications biology
Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite-host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potenti...

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification.

IEEE transactions on pattern analysis and machine intelligence
Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep learning neural networks, especially the Convolutional Neural Networks (CNNs). Most of the existing works focused on classif...

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

PloS one
BACKGROUND: Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. T...

Using neural networks to mine text and predict metabolic traits for thousands of microbes.

PLoS computational biology
Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. I...

Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation.

IEEE transactions on medical imaging
Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the ...

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases.

PloS one
As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented i...