AIMC Topic: Machine Learning

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Designing sensitive viral diagnostics with machine learning.

Nature biotechnology
Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learne...

Building artificial intelligence and machine learning models : a primer for emergency physicians.

Emergency medicine journal : EMJ
There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emer...

Biochemical algorithm to identify individuals with ALPL variants among subjects with persistent hypophosphatasaemia.

Orphanet journal of rare diseases
BACKGROUND: Hypophosphatasia (HPP) is a rare and underdiagnosed condition characterized by deficient bone and teeth mineralization. The aim of this study was first, to evaluate the diagnostic utility of employing alkaline phosphatase (ALP) threshold ...

Developing stacking ensemble models for multivariate contamination detection in water distribution systems.

The Science of the total environment
This study presents a new stacking ensemble model for contamination event detection using multiple water quality parameters. The stacking model consists of a number of machine learning base predictors and a meta-predictor, and it is trained using cro...

Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering.

Annual review of chemical and biomolecular engineering
This article reviews recent developments in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation for the discovery, design, and optimization of soft and biological materials. The design and eng...

Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Journal of digital imaging
Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of mult...

An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both an...

CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints.

Journal of chemical theory and computation
Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, including accuracy...

The role of artificial intelligence in pancreatic surgery: a systematic review.

Updates in surgery
Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for p...