AIMC Topic: Machine Learning

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Putative cell type discovery from single-cell gene expression data.

Nature methods
We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells...

Cannabis use is associated with a small increase in the risk of postoperative nausea and vomiting: a retrospective machine-learning causal analysis.

BMC anesthesiology
BACKGROUND: Cannabis legalization may contribute to an increased frequency of chronic use among patients presenting for surgery. At present, it is unknown whether chronic cannabis use modifies the risk of postoperative nausea and vomiting (PONV).

Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks.

TheScientificWorldJournal
The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity am...

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries.

American journal of obstetrics and gynecology
BACKGROUND: The process of childbirth is one of the most crucial events in the future health and development of the offspring. The vulnerability of parturients and fetuses during the delivery process led to the development of intrapartum monitoring m...

Supervised Machine Learning: A Brief Primer.

Behavior therapy
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine l...

An automated machine learning approach to predict brain age from cortical anatomical measures.

Human brain mapping
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a m...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

International journal of medical informatics
BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and com...

Machine learning on drug-specific data to predict small molecule teratogenicity.

Reproductive toxicology (Elmsford, N.Y.)
Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes ...

Assessment and prediction of spine surgery invasiveness with machine learning techniques.

Computers in biology and medicine
BACKGROUND: The interest in Minimally Invasive Surgery (MIS) techniques has greatly increased in the recent years due to their significant advantages, both in terms of outcome improvement and cost reduction. Also in spine surgery, MIS is now applicab...