Alle Papers – Machine Learning im Gesundheitswesen

Multi-modal sequence learning for Alzheimer’s disease progression prediction with incomplete variable-length longitudinal data

Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models

Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals’ perspectives

Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

Advanced Survival Models

A time series driven model for early sepsis prediction based on transformer module

Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms

Fine-grained Patient Similarity Measuring using Deep Metric Learning

Multi-channel fusion LSTM for medical event prediction using EHRs

A Metaphoric Investigation on Prediction of Heart Disease using Machine Learning

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Imputation of Missing Data in Electronic Health Records Based on Patients’ Similarities

Machine Self-awareness

ChatGPT and conversational artificial intelligence: Friend, foe, or future of research?

Artificial Intelligence in Critical Care

Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

The information value of clinical data

Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach

A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease

Interpretation of machine learning predictions for patient outcomes in electronic health records

Assessing asthma severity based on claims data: a systematic review

Machine learning in patient flow: a review

A CNN-based novel solution for determining the survival status of heart failure patients with clinical record data: numeric to image

Stratification of the severity of critically ill patients with classification trees

Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery