AIMC Topic: Machine Learning

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Opportunities and challenges using artificial intelligence in ADME/Tox.

Nature materials
A recent conference organized a panel of scientists representing small and big pharma companies, who work at the interface of machine learning (ML) and absorption, distribution, metabolism, excretion, and toxicology (ADME/Tox). With the recent rebirt...

Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces.

Medicine and science in sports and exercise
INTRODUCTION: Musculoskeletal injury rates in military personnel remain unacceptably high. Application of machine learning algorithms could be useful in multivariate models to predict injury in this population. The purpose of this study was to invest...

Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risk...

Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.

Stroke
Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. Thi...

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.

American journal of epidemiology
Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of...

An outcome model approach to transporting a randomized controlled trial results to a target population.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the tar...

A Machine Learning-Based Predictive Model of Return to Work After Sick Leave.

Journal of occupational and environmental medicine
OBJECTIVE: This study aims to build a predictive model for "return to work" (RTW) after sick leave by using a machine-learning algorithm.

A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

Toxicological sciences : an official journal of the Society of Toxicology
The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidim...