AI Medical Compendium Journal:
BMC medical informatics and decision making

Showing 101 to 110 of 718 articles

Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning.

BMC medical informatics and decision making
INTRODUCTION: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).

Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the ...

Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.

BMC medical informatics and decision making
BACKGROUND: In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students' diagnostic abilities through interactive patient interviews (simulated patient interactions). By analyzi...

Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BMC medical informatics and decision making
BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (Ms...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

BMC medical informatics and decision making
This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review and random-effect meta-analysis summarizing predictive model developm...

Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.

BMC medical informatics and decision making
BACKGROUND: Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for t...

Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.

BMC medical informatics and decision making
BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographi...

Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machin...

A radiomics and deep learning nomogram developed and validated for predicting no-collapse survival in patients with osteonecrosis after multiple drilling.

BMC medical informatics and decision making
PURPOSE: Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.