AIMC Topic: Aged

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A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplast...

Integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke.

Chemical communications (Cambridge, England)
We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke. In particular, we develop an optimal model t...

Integrating Socially Assistive Robots into Japanese Nursing Care.

Studies in health technology and informatics
This paper presents experiences of integrating assistive robots in Japanese nursing care through semi-structured interviews and site observations at three nursing homes in Japan during the spring of 2019. The study looked at experiences with the robo...

Social Robots for Elderly Care: An Inventory of Promising Use Cases and Business Models.

Studies in health technology and informatics
This paper discusses a study that aimed to elicit promising application areas and potential business models for social robotics in healthcare. For this goal, we conducted focus groups with care professionals and the management of elderly care organiz...

Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

Open heart
OBJECTIVE: Electrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is di...

Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

International journal of neural systems
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imagi...

Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Neurosurgical focus
OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing d...

Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms.

The bone & joint journal
AIMS: The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance.