AIMC Topic: Adult

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Predicting delayed neurological sequelae in patients with carbon monoxide poisoning using machine learning models.

Clinical toxicology (Philadelphia, Pa.)
INTRODUCTION: Delayed neurological sequelae is a common complication following carbon monoxide poisoning, which significantly affects the quality of life of patients with the condition. We aimed to develop a machine learning-based prediction model to...

Exploring the Potential of AI-Assisted Technology in Joint Range-of-Motion Measurements: A Reliability Study.

Medicina (Kaunas, Lithuania)
: Measuring joint range of motion (ROM) is essential for diagnosing and treating musculoskeletal diseases. However, most clinical measurements are conducted using conventional devices, and their reliability may significantly depend on the tester. Thi...

Prediction and unsupervised clustering of fertility intention among migrant workers based on machine learning: a cross-sectional survey from Henan, China.

BMC public health
BACKGROUND: Although China has implemented multiple policies to encourage childbirth, the results have been underwhelming. Migrant workers account for a considerable proportion of China's population, most of whom are of childbearing age. However, few...

Effect of feedback-integrated reflection, on deep learning of undergraduate medical students in a clinical setting.

BMC medical education
BACKGROUND: Reflection fosters self-regulated learning by enabling learners to critically evaluate their performance, identify gaps, and make plans to improve. Feedback, in turn, provides external insights that complement reflection, helping learners...

Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.

Scientific reports
The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlight...

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.

Journal of medical Internet research
BACKGROUND: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment meth...

Machine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.

Neurosurgical review
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical ...

Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM ris...

Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.

Neuroinformatics
In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus...

Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.

Frontiers in immunology
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by chronic hyperglycemia, insulin resistance, and immune system dysfunction. Immune cells, including T cells and monocytes, play a pivotal role in driving ...