AIMC Topic: Aged, 80 and over
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Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population.
The American journal of medicine
Apr 6, 2024
BACKGROUND: Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly ...
Predicting ischemic stroke patients' prognosis changes using machine learning in a nationwide stroke registry.
Medical & biological engineering & computing
Apr 5, 2024
Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outco...
Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.
Journal of medical imaging and radiation oncology
Apr 5, 2024
INTRODUCTION: Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique ...
Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence.
Current oncology (Toronto, Ont.)
Apr 2, 2024
Real-world evidence for patients with advanced -mutated non-small cell lung cancer (NSCLC) in Canada is limited. This study's objective was to use previously validated DARWEN artificial intelligence (AI) to extract data from electronic heath records ...
An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction.
European neurology
Apr 2, 2024
INTRODUCTION: Malignant cerebral edema (MCE) is a serious complication and the main cause of poor prognosis in patients with large-hemisphere infarction (LHI). Therefore, the rapid and accurate identification of potential patients with MCE is essenti...
Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma.
Journal of neuro-oncology
Apr 1, 2024
PURPOSE: To develop and validate a pathomics signature for predicting the outcomes of Primary Central Nervous System Lymphoma (PCNSL).
Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and...
Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.
Journal of applied gerontology : the official journal of the Southern Gerontological Society
Mar 31, 2024
This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) ...
Exploratory analysis using machine learning algorithms to predict pinch strength by anthropometric and socio-demographic features.
International journal of occupational safety and ergonomics : JOSE
Mar 30, 2024
. This study examines the role of different machine learning (ML) algorithms to determine which socio-demographic factors and hand-forearm anthropometric dimensions can be used to accurately predict hand function. . The cross-sectional study was cond...
Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.
Gynecologic oncology
Mar 29, 2024
OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.
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