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Deep Learning Radiomics Nomogram Based on MRI for Differentiating between Borderline Ovarian Tumors and Stage I Ovarian Cancer: A Multicenter Study.
Academic radiology
Jan 14, 2025
RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics nomogram (DLRN) based on T2-weighted MRI to distinguish between borderline ovarian tumors (BOTs) and stage I epithelial ovarian cancer (EOC) preoperatively.
Interpretable machine learning models for COPD ease of breathing estimation.
Medical & biological engineering & computing
Jan 14, 2025
Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of...
Predicting delayed neurological sequelae in patients with carbon monoxide poisoning using machine learning models.
Clinical toxicology (Philadelphia, Pa.)
Jan 14, 2025
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 Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.
JMIR aging
Jan 14, 2025
BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be considered an effective intervention for people with PD. Several findings in the literature emphasize th...
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
Jan 14, 2025
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
Jan 14, 2025
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
Jan 14, 2025
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...
Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.
Biomolecules & biomedicine
Jan 14, 2025
Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteoporotic fractures and leading to considerable health deterioration and increased mortality. In address...
Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.
Frontiers in immunology
Jan 14, 2025
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 ...
Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.
Annals of medicine
Jan 13, 2025
OBJECTIVE: We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.
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