AIMC Topic: Machine Learning

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Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.

Journal of cancer survivorship : research and practice
PURPOSE: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim ...

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Transplantation
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: pa...

EstimATTR: A Simplified, Machine-Learning-Based Tool to Predict the Risk of Wild-Type Transthyretin Amyloid Cardiomyopathy.

Journal of cardiac failure
BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM), an increasingly recognized cause of heart failure (HF), often remains undiagnosed until later stages of the disease.

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Surgical innovation
Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification o...

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images.

Medical engineering & physics
The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization ...

Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Sensors (Basel, Switzerland)
Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modifie...

Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale.

Scientific reports
This study used deep neural networks and machine learning models to predict facial landmark positions and pain scores using the Feline Grimace Scale (FGS). A total of 3447 face images of cats were annotated with 37 landmarks. Convolutional neural net...

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely prom...

Four attributes of intelligence, a thousand questions.

Biological cybernetics
Jeff Hawkins is one of those rare individuals who speaks the languages of both AI and neuroscience. In his recent book, "A Thousand Brains: A New Theory of Intelligence", Hawkins proposes that current learning algorithms lack four attributes which wi...

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

Frontiers in public health
AIMS: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway.