AIMC Topic: Aged

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Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learni...

Food for thought: a qualitative assessment of medical trainee and faculty perceptions of nutrition education.

BMC medical education
BACKGROUND: The American Society of Clinical Nutrition recommends 37 to 44 h of undergraduate medical nutrition education. The Total Health Curriculum at Geisinger Commonwealth School of Medicine (GCSOM) contains 14 h of objective-based nutritional i...

A new risk assessment model of venous thromboembolism by considering fuzzy population.

BMC medical informatics and decision making
BACKGROUND: Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the impact of class-imbalance pro...

An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.

BMC musculoskeletal disorders
BACKGROUND: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for patient care and resource allocation. Predicting prolonged dependence on mechanical ventilation is cruc...

Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.

Alzheimer's research & therapy
BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the mos...

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features.

BMC medical imaging
BACKGROUND: To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict cl...

Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients.

PloS one
OBJECTIVE: To understand the prevalence and associated risk factors of lower extremity arterial disease (LEAD) in Chinese diabetic patients and to construct a risk prediction model.