Alle Papers – Machine Learning im Gesundheitswesen

Early prediction of relapse in schizophrenia: an application of receiver operating characteristic (ROC) methods.

Predicting 30-Day Readmissions in Patients With Heart Failure Using Administrative Data: A Machine Learning Approach

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold

Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study

Probabilistic modeling personalized treatment pathways using electronic health records

Integrating Graph Contextualized Knowledge into Pre-trained Language Models

Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Biomedical Informatics

Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22

<tt>TWIN-GPT</tt> : Digital Twins for Clinical Trials via Large Language Model

A Method for Generating Synthetic Electronic Medical Record Text

Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms

Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill

Automatic Evaluation of FHR Recordings from CTU-UHB CTG Database

Multimorbidity increases the risk of dementia: a 15 year follow-up of the SHARE study

Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications

A large language model–based generative natural language processing framework fine‐tuned on clinical notes accurately extracts headache frequency from electronic health records

Use of Large Language Models to Predict Neuroimaging

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Voodoo Machine Learning for Clinical Predictions

Do stakeholder needs differ? - Designing stakeholder-tailored Explainable Artificial Intelligence (XAI) interfaces

Artificial Intelligence–Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study

Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents

Predicting Length of Stay for Psychiatric Diagnosis-related Groups Using Neural Networks