AIMC Topic: Machine Learning

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Towards an Explainable AI-Based Tool to Predict Preterm Birth.

Studies in health technology and informatics
Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women' objective r...

Fairness in Artificial Intelligence: Regulatory Sanbox Evaluation of Bias Prevention for ECG Classification.

Studies in health technology and informatics
As the use of artificial intelligence within healthcare is on the rise, an increased attention has been directed towards ethical considerations. Defining fairness in machine learning is a well explored topic with an extensive literature. However, suc...

Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification.

Studies in health technology and informatics
Deep Learning architectures for time series require a large number of training samples, however traditional sample size estimation for sufficient model performance is not applicable for machine learning, especially in the field of electrocardiograms ...

The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models.

Studies in health technology and informatics
Even though the interest in machine learning studies is growing significantly, especially in medicine, the imbalance between study results and clinical relevance is more pronounced than ever. The reasons for this include data quality and interoperabi...

Can Synthetic Images Improve CNN Performance in Wound Image Classification?

Studies in health technology and informatics
For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large ...

Prediction of Mental Health Support of Employee Perceiving by Using Machine Learning Methods.

Studies in health technology and informatics
Employees' mental health addresses concerns in the technology industry phenomenon. Machine Learning (ML) approaches show promise in predicting mental health problems and identifying related factors. This study used three machine learning models on OS...

In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts.

Studies in health technology and informatics
Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Jap...

Few-Shot and Prompt Training for Text Classification in German Doctor's Letters.

Studies in health technology and informatics
To classify sentences in cardiovascular German doctor's letters into eleven section categories, we used pattern-exploiting training, a prompt-based method for text classification in few-shot learning scenarios (20, 50 and 100 instances per class) usi...

Model and Strategy for Predicting and Discovering Drug-Drug Interactions.

Studies in health technology and informatics
Taking several medications at the same time is an increasingly common phenomenon in our society. The combination of drugs is certainly not without risk of potentially dangerous interactions. Taking into account all possible interactions is a very com...

Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning.

Toxicological sciences : an official journal of the Society of Toxicology
We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We ...