AIMC Topic: Machine Learning

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Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

PloS one
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 20...

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

PloS one
Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum fr...

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

PloS one
BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital morta...

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

PloS one
Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to n...

mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks.

IEEE journal of biomedical and health informatics
This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that balance high classification performance with robust privacy protection against membership inference attacks (MIA). We introduce a comprehensive approach t...

Hierarchical Graph Transformer With Contrastive Learning for Gene Regulatory Network Inference.

IEEE journal of biomedical and health informatics
Gene regulatory networks (GRNs) are crucial for understanding gene regulation and cellular processes. Inferring GRNs helps uncover regulatory pathways, shedding light on the regulation and development of cellular processes. With the rise of high-thro...

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

IEEE journal of biomedical and health informatics
Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorder (ASD). However, current domain adaptation methods primarily focus on classifying data in a single target domain with the assistance of one or multip...

Acoustic COVID-19 Detection Using Multiple Instance Learning.

IEEE journal of biomedical and health informatics
In the COVID-19 pandemic, a rigorous testing scheme was crucial. However, tests can be time-consuming and expensive. A machine learning-based diagnostic tool for audio recordings could enable widespread testing at low costs. In order to achieve compa...

Frailty Modeling Using Machine Learning Methodologies: A Systematic Review With Discussions on Outstanding Questions.

IEEE journal of biomedical and health informatics
Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic...