AI Medical Compendium Journal:
Frontiers in digital health

Showing 11 to 20 of 33 articles

Role of artificial intelligence in early detection of congenital heart diseases in neonates.

Frontiers in digital health
In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the ...

Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.

Frontiers in digital health
BACKGROUND: High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The a...

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study.

Frontiers in digital health
BACKGROUND: Recently, crowding in emergency departments (EDs) has become a recognised critical factor impacting global public healthcare, resulting from both the rising supply/demand mismatch in medical services and the paucity of hospital beds avail...

Evaluation of 6 years of eHealth data in the alcohol use disorder field indicates improved efficacy of care.

Frontiers in digital health
BACKGROUND: Predictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing.

Frontiers in digital health
Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving s...

Listen to the patients! Identifying CML patients' needs analyzing patient-generated content with AI-driven methodologies.

Frontiers in digital health
BACKGROUND: Various patient support programs exist to provide successful therapy options for patients. Pharmaceutical companies are increasingly recognizing the importance of actively supporting patients in their long-term treatment. In order to effe...

Your robot therapist is not your therapist: understanding the role of AI-powered mental health chatbots.

Frontiers in digital health
Artificial intelligence (AI)-powered chatbots have the potential to substantially increase access to affordable and effective mental health services by supplementing the work of clinicians. Their 24/7 availability and accessibility through a mobile p...

Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients.

Frontiers in digital health
INTRODUCTION: Accurately predicting patient outcomes is crucial for improving healthcare delivery, but large-scale risk prediction models are often developed and tested on specific datasets where clinical parameters and outcomes may not fully reflect...

Pediatric Otoscopy Video Screening With Shift Contrastive Anomaly Detection.

Frontiers in digital health
Ear related concerns and symptoms represent the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered diseases of the middle and external presents ...