AIMC Topic: Aged

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Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study.

The British journal of clinical psychology
OBJECTIVE: This study assessed predictors of stress, anxiety and depression during the COVID-19 pandemic using a large number of demographic, COVID-19 context and psychological variables.

Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database.

Computer methods and programs in biomedicine
BACKGROUND: Studies have found that first primary cancer (FPC) survivors are at high risk of developing second primary breast cancer (SPBC). However, there is a lack of prognostic studies specifically focusing on patients with SPBC.

nnU-Net-based deep-learning for pulmonary embolism: detection, clot volume quantification, and severity correlation in the RSPECT dataset.

European journal of radiology
OBJECTIVES: CT pulmonary angiography is the gold standard for diagnosing pulmonary embolism, and DL algorithms are being developed to manage the increase in demand. The nnU-Net is a new auto-adaptive DL framework that minimizes manual tuning, making ...

Automatic quantification of scapular and glenoid morphology from CT scans using deep learning.

European journal of radiology
OBJECTIVES: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis.

Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population.

Diabetes research and clinical practice
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.

Construction of prediction models for novel subtypes in patients with arteriosclerosis obliterans undergoing endovascular therapy: an unsupervised machine learning study.

Journal of cardiothoracic surgery
BACKGROUND: Arteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is increasingly used for limb salvage in ASO patients, but the outcomes vary. The development of prediction mode...

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study.

BMC geriatrics
BACKGROUND: Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geria...

Development of machine learning models for patients in the high intrahepatic cholangiocarcinoma incidence age group.

BMC geriatrics
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) has a poor prognosis and is understudied. Based on the clinical features of patients with ICC, we constructed machine learning models to understand their importance on survival and to accurately deter...