AIMC Topic: Machine Learning

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Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predic...

A Turing Test for Molecular Generators.

Journal of medicinal chemistry
Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecu...

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are ty...

How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.

International journal of molecular sciences
The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F population were genotype...

Machine learning identifies abnormal Ca transients in human induced pluripotent stem cell-derived cardiomyocytes.

Scientific reports
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca transients is crucial for evaluating cardiomyocyte function that requires l...

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Scientific reports
Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using F-fluorodeoxyglu...

Using machine learning to understand the implications of meteorological conditions for fish kills.

Scientific reports
Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality mon...

Deep transfer learning for reducing health care disparities arising from biomedical data inequality.

Nature communications
As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. H...

Rejoinder to Discussions on "Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep".

Biometrics
We thank the discussants for sharing their unique perspectives on the problem of designing automatic algorithm change protocols (aACPs) for machine learning-based software as a medical device. Both Pennello et al. and Rose highlighted a number of cha...

Discussion on "Approval policies for modifications to machine learning-based software as a medical device: A study of biocreep" by Jean Feng, Scott Emerson, and Noah Simon.

Biometrics
I applaud the authors of Feng (2020) for tackling a challenging statistical problem on approval policies for software as a medical device (SaMD). Their work exploring methodology that could autonomously build algorithmic change protocols soundly ext...