AIMC Topic: Aged

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SmartPulse, a machine learning approach for calibration-free dynamic RF shimming: Preliminary study in a clinical environment.

Magnetic resonance in medicine
PURPOSE: A calibration-free pulse design method is introduced to alleviate artifacts in clinical routine with parallel transmission at high field, dealing with significant inter-subject variability, found for instance in the abdomen.

Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purp...

Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease.

Computers in biology and medicine
BACKGROUND: Given the increasing recognition of the significance of non-motor symptoms in Parkinson's disease, we investigate the optimal use of machine learning methods for the prediction of the Montreal Cognitive Assessment (MoCA) score at year 4 f...

Linguistic summarization of in-home sensor data.

Journal of biomedical informatics
INTRODUCTION: With the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by t...

An image-based deep learning framework for individualizing radiotherapy dose.

The Lancet. Digital health
BACKGROUND: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to i...

Comparison of machine learning models for seizure prediction in hospitalized patients.

Annals of clinical and translational neurology
OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h).

F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

NeuroImage. Clinical
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...

Incorporating Conversational Strategies in a Social Robot to Interact with People with Dementia.

Dementia and geriatric cognitive disorders
BACKGROUND: Socially assistive robots (SARs) have the potential to assist nonpharmacological interventions based on verbal communication to support the care of persons with dementia (PwDs). However, establishing verbal communication with a PwD is cha...

Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning.

Molecular & cellular proteomics : MCP
Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event....