Alle Papers – Machine Learning im Gesundheitswesen

What went wrong and when? Instance-wise feature importance for time-series black-box models

Put It Back: Entity Typing with Language Model Enhancement

Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing

Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence

Predictive Big Data Analytics using the UK Biobank Data

Using patient-reportable clinical history factors to predict myocardial infarction

Nuclear Medicine from a Novel Perspective: Buvat and Weber Talk with OpenAI’s ChatGPT

Early detection of cognitive impairment in the elderly based on Bayesian mining using speech prosody and cerebral blood flow activation

Passive Digital Signature for Early Identification of Alzheimer's Disease and Related Dementia

Use of Many Covariates in Clinical Trials

On modeling credit defaults: A probabilistic Boolean network approach

A novel medical text classification model with Kalman filter for clinical decision making

Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis

Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism

Multilingual chief complaint classification for syndromic surveillance: An experiment with Chinese chief complaints

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients

Neural variational sparse topic model for sparse explainable text representation

Constructing a disease database and using natural language processing to capture and standardize free text clinical information

The Dependence of Machine Learning on Electronic Medical Record Quality

Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images

Machine Learning Applications in Solid Organ Transplantation and Related Complications

A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology

Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy

Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline

Physical Mathematical Evaluation of the Cardiac Dynamic Applying the Zipf-Mandelbrot Law