AIMC Topic: Machine Learning

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Prediction of successful aging using ensemble machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive an...

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features.

Computational and mathematical methods in medicine
Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it ...

Logistic Regression With Machine Learning Sheds Light on the Problematic Sexual Behavior Phenotype.

Journal of addiction medicine
OBJECTIVES: There has been a longstanding debate about whether the mechanisms involved in problematic sexual behavior (PSB) are similar to those observed in addictive disorders, or related to impulse control or to compulsivity. The aim of this report...

Automatic Evolution of Machine-Learning-Based Quantum Dynamics with Uncertainty Analysis.

Journal of chemical theory and computation
The machine learning approaches are applied in the dynamical simulation of open quantum systems. The long short-term memory recurrent neural network (LSTM-RNN) models are used to simulate the long-time quantum dynamics, which are built based on the k...

Prediction of Prognosis in Patients with Trauma by Using Machine Learning.

Medicina (Kaunas, Lithuania)
We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Th...

MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

IEEE transactions on medical imaging
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they oft...

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study.

BMC bioinformatics
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. ...

Comparing two machine learning approaches in predicting lupus hospitalization using longitudinal data.

Scientific reports
Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by flares ranging from mild to life-threatening. Severe flares and complications can require hospitalizations, which account for most of the direct costs of SLE ca...

Perception without preconception: comparison between the human and machine learner in recognition of tissues from histological sections.

Scientific reports
Deep neural networks (DNNs) have shown success in image classification, with high accuracy in recognition of everyday objects. Performance of DNNs has traditionally been measured assuming human accuracy is perfect. In specific problem domains, howeve...

Ensemble classification combining ResNet and handcrafted features with three-steps training.

Physiological measurement
This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to integrate deep and classic machine learning features into a single model, exploring the proper structur...