AIMC Topic: Machine Learning

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Early prediction of severe acute pancreatitis using machine learning.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
BACKGROUND: Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Ri...

Protein function prediction using functional inter-relationship.

Computational biology and chemistry
With the growth of high throughput sequencing techniques, the generation of protein sequences has become fast and cheap, leading to a huge increase in the number of known proteins. However, it is challenging to identify the functions being performed ...

Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models.

BMC cardiovascular disorders
BACKGROUND: Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial...

Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.

Computational and mathematical methods in medicine
We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery d...

Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding.

Psychometrika
Recently, machine learning (ML) methods have been used in causal inference to estimate treatment effects in order to reduce concerns for model mis-specification. However, many ML methods require that all confounders are measured to consistently estim...

Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods.

Journal of environmental management
Understanding the mechanisms of pollutant removal in Wastewater Treatment Plants (WWTPs) is crucial for controlling effluent quality efficiently. However, the numerous treatment units, operational factors, and the underlying interactions between thes...

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Journal of chemical information and modeling
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to inclu...

Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data.

Biomarkers in medicine
We tested whether machine-learning algorithm could find biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencing data. Whole-exome sequencing data derived from 1181 female breast cancer patients with...

Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection.

Scientific reports
Accurate detection and risk stratification of latent tuberculosis infection (LTBI) remains a major clinical and public health problem. We hypothesize that multiparameter strategies that probe immune responses to Mycobacterium tuberculosis can provide...