AIMC Topic: Humans

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Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.

Molecular informatics
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The ...

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches.

Molecular informatics
Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is es...

Why your doctor is not an algorithm: Exploring logical principles of different clinical inference methods using liver transplantation as a model.

Gastroenterologia y hepatologia
The development of machine learning (ML) tools in many different medical settings is largely increasing. However, the use of the resulting algorithms in daily medical practice is still an unsolved challenge. We propose an epistemological approach (i....

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

International journal of computer assisted radiology and surgery
PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficul...

Digital twins: reimagining the future of cardiovascular risk prediction and personalised care.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the potential to redefine cardiovascular risk prevention. Digital twinning combines vast amounts of data fr...

Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentation Mastopexy.

Aesthetic plastic surgery
BACKGROUND: Single-stage mastopexy augmentation is a much-debated intervention due to its complexity and the associated relatively high complication rates. This study aimed to reevaluate the risk factors for these complications using a novel approach...

Layer-selective deep representation to improve esophageal cancer classification.

Medical & biological engineering & computing
Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliabili...

Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models.

Medical & biological engineering & computing
This paper presents the implementation of two automated text classification systems for prostate cancer findings based on the PI-RADS criteria. Specifically, a traditional machine learning model using XGBoost and a language model-based approach using...

Heterogeneous graph convolutional network for multi-view semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a novel approach to semantic representation learning from multi-view datasets, distinct from most existing methodologies which typically handle single-view data individually, maintaining a shared semantic link across the multi-vie...

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.

Biological psychiatry
BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for chan...