AI Medical Compendium Journal:
BMC medical informatics and decision making

Showing 51 to 60 of 718 articles

Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Artificial intelligence (AI), which emulates human intelligence through knowledge-based heuristics, has transformative impacts across various industries. In the global healthcare sector, there is a pressing need for advanced risk assessme...

Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.

BMC medical informatics and decision making
Hyperuricemia has seen a continuous increase in incidence and a trend towards younger patients in recent years, posing a serious threat to human health and highlighting the urgency of using technological means for disease risk prediction. Existing ri...

Machine learning based model for the early detection of Gestational Diabetes Mellitus.

BMC medical informatics and decision making
BACKGROUND: Gestational Diabetes Mellitus (GDM) is one of the most common medical complications during pregnancy. In the Gulf region, the prevalence of GDM is higher than in other parts of the world. Thus, there is a need for the early detection of G...

Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

BMC medical informatics and decision making
OBJECTIVE: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, ...

Risk-based evaluation of machine learning-based classification methods used for medical devices.

BMC medical informatics and decision making
BACKGROUND: In the future, more medical devices will be based on machine learning (ML) methods. In general, the consideration of risks is a crucial aspect for evaluating medical devices. Accordingly, risks and their associated costs should be taken i...

Advancing AI-driven thematic analysis in qualitative research: a comparative study of nine generative models on Cutaneous Leishmaniasis data.

BMC medical informatics and decision making
BACKGROUND: As part of qualitative research, the thematic analysis is time-consuming and technical. The rise of generative artificial intelligence (A.I.), especially large language models, has brought hope in enhancing and partly automating thematic ...

An intelligent multi-attribute decision-making system for clinical assessment of spinal cord disorder using fuzzy hypersoft rough approximations.

BMC medical informatics and decision making
The data for diagnosing spinal cord disorder (SCD) are complex and often confusing, making it difficult for established diagnostic techniques to yield reliable results. This issue frequently necessitates expensive testing to get an accurate diagnosis...

AI-Driven decision-making for personalized elderly care: a fuzzy MCDM-based framework for enhancing treatment recommendations.

BMC medical informatics and decision making
BACKGROUND: Global healthcare systems face enormous challenges due to the ageing population, demanding novel measures to assure long-term efficacy and viability. The expanding senior population, which requires specialised and efficient healthcare sol...

Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.

BMC medical informatics and decision making
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease r...

A systematic review of large language model (LLM) evaluations in clinical medicine.

BMC medical informatics and decision making
BACKGROUND: Large Language Models (LLMs), advanced AI tools based on transformer architectures, demonstrate significant potential in clinical medicine by enhancing decision support, diagnostics, and medical education. However, their integration into ...