AIMC Topic: Middle Aged

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A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI).

An ensemble model for predicting dispositions of emergency department patients.

BMC medical informatics and decision making
OBJECTIVE: The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning's potential to predict ED disposit...

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

Breast cancer research : BCR
BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography conside...

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.

BMC medical informatics and decision making
OBJECTIVES: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of N...

External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington's disease.

Scientific reports
A crucial step in the clinical adaptation of an AI-based tool is an external, independent validation. The aim of this study was to investigate brain atrophy in patients with confirmed, progressed Huntington's disease using a certified software for au...

Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms.

Scientific reports
This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an effective algorithm for predicting co-infection in LN. This study included 111 non-infected LN pati...

Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.

BMJ health & care informatics
BACKGROUND: High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has...

Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study.

Multiple sclerosis and related disorders
BACKGROUND: Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contra...

Social Workers' Perspectives on Socially Isolated Older Adults Living with a Robot Companion.

Journal of gerontological social work
Addressing depression and social isolation among solo-living older adults in South Korea requires a multi-faceted approach. This study aims to explore the advantages and obstacles of a robotic elder care program by analyzing social workers' viewpoint...

Effects of tacrolimus on proteinuria in Chinese and Indian patients with idiopathic membranous nephropathy: the results of machine learning study.

International urology and nephrology
PURPOSE: The present study aims to explore the effects of tacrolimus on proteinuria in patients with idiopathic membranous nephropathy (IMN) and recommend an appropriate dosage schedule via machine learning method.