AIMC Topic: Machine Learning

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Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing.

British journal of cancer
BACKGROUND: It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for ident...

Exploring Heterogeneity in Cost-Effectiveness Using Machine Learning Methods: A Case Study Using the FIRST-ABC Trial.

Medical care
OBJECTIVE: The aim of this study was to explore heterogeneity in the cost-effectiveness of high-flow nasal cannula (HFNC) therapy compared with continuous positive airway pressure (CPAP) in children following extubation.

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.

Journal of chemical information and modeling
We present a novel and interpretable approach for assessing small-molecule binding using context explanation networks. Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to...

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.

Sensors (Basel, Switzerland)
Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditiona...

Long non-coding RNAs in biomarking COVID-19: a machine learning-based approach.

Virology journal
BACKGROUND: The coronavirus pandemic that started in 2019 has caused the highest mortality and morbidity rates worldwide. Data on the role of long non-coding RNAs (lncRNAs) in coronavirus disease 2019 (COVID-19) is scarce. We aimed to elucidate the r...

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.

BMC medical informatics and decision making
BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive ca...

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.

BMC medical informatics and decision making
PURPOSE: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, b...

NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.

BMC genomics
BACKGROUNDS: The single-pass long reads generated by third-generation sequencing technology exhibit a higher error rate. However, the circular consensus sequencing (CCS) produces shorter reads. Thus, it is effective to manage the error rate of long r...

Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.

Scientific reports
Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied area. This retrospective cohort study, conducted in a medical center in Taiwan from 2015 to 2020, enr...