Clinical text datasets for medical artificial intelligence and large language models—a systematic review
Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …
clinical text data, which contain extensive and diverse information and serve as the …
Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities
Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …
machine learning supporting integrated modeling of structured and unstructured data is an …
Causal-structure driven augmentations for text ood generalization
The reliance of text classifiers on spurious correlations can lead to poor generalization at
deployment, raising concerns about their use in safety-critical domains such as healthcare …
deployment, raising concerns about their use in safety-critical domains such as healthcare …
Predictive Modeling for Hospital Readmissions for Patients with Heart Disease: An updated review from 2012-2023
W Zhang, W Cheng, K Fujiwara… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Hospital readmissions are a major concern for healthcare leaders, policy makers, and
patients, resulting in adverse health outcomes and imposing an increased burden on …
patients, resulting in adverse health outcomes and imposing an increased burden on …
The added value of text from Dutch general practitioner notes in predictive modeling
Objective This work aims to explore the value of Dutch unstructured data, in combination
with structured data, for the development of prognostic prediction models in a general …
with structured data, for the development of prognostic prediction models in a general …
Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty
Prediction of bacteremia is a clinically important but challenging task. An artificial
intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding …
intelligence (AI) model has the potential to facilitate early bacteremia prediction, aiding …
[HTML][HTML] Extracting medical information from free-text and unstructured patient-generated health data using natural language processing methods: feasibility study with …
Background Patient-generated health data (PGHD) captured via smart devices or digital
health technologies can reflect an individual health journey. PGHD enables tracking and …
health technologies can reflect an individual health journey. PGHD enables tracking and …
Predicting future falls in older people using natural language processing of general practitioners' clinical notes
Background Falls in older people are common and morbid. Prediction models can help
identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity …
identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity …
Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model
Objectives Little is known about proactive risk assessment concerning emergency
department (ED) visits and hospitalizations in patients with heart failure (HF) who receive …
department (ED) visits and hospitalizations in patients with heart failure (HF) who receive …
Prediction of high-risk donors for kidney discard and nonrecovery using structured donor characteristics and unstructured donor narratives
J Sageshima, P Than, N Goussous, N Mineyev… - JAMA …, 2024 - jamanetwork.com
Importance Despite the unmet need, many deceased-donor kidneys are discarded or not
recovered. Inefficient allocation and prolonged ischemia time are contributing factors, and …
recovered. Inefficient allocation and prolonged ischemia time are contributing factors, and …