AIMC Topic: Machine Learning

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Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - i...

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

Proceedings of the National Academy of Sciences of the United States of America
The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Most significantly, we classify circadian genes using DNA sequence features genera...

Multiplex computational pathology for treatment response predication.

Cancer cell
Recently published in Science, AstroPath outlines a standardized workflow for multiplex immunofluorescence (mIF) panel development, imaging, and analysis; showcases its potential in biomarker discovery for predicting response to anti-PD-1 treatment; ...

Deep learning and the electrocardiogram: review of the current state-of-the-art.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for ...

Predictive and interpretable models via the stacked elastic net.

Bioinformatics (Oxford, England)
MOTIVATION: Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whet...

Learning hidden elasticity with deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnos...

Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.

Proceedings of the National Academy of Sciences of the United States of America
Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug ...

Learn to synchronize, synchronize to learn.

Chaos (Woodbury, N.Y.)
In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks...

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach.

JCO clinical cancer informatics
PURPOSE: Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning-based neutropenia prediction model that can be used to assess risk at the initiation of a chemother...