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Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.
International journal of cardiology
Jun 15, 2024
BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.
Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models.
The American journal of emergency medicine
Jun 15, 2024
OBJECTIVES: Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle ...
Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts.
EBioMedicine
Jun 15, 2024
BACKGROUND: T-cell immune infiltrates are robust prognostic variables in localised colon cancer. Evaluation of prognosis using artificial intelligence is an emerging field. We evaluated whether machine learning analysis improved prediction of patient...
Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.
Abdominal radiology (New York)
Jun 15, 2024
PURPOSE: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data.
CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.
Urolithiasis
Jun 15, 2024
Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were ...
Detection of Parkinson disease using multiclass machine learning approach.
Scientific reports
Jun 15, 2024
Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure rema...
Predictive model for assessing malnutrition in elderly hospitalized cancer patients: A machine learning approach.
Geriatric nursing (New York, N.Y.)
Jun 15, 2024
BACKGROUND: Malnutrition is prevalent among elderly cancer patients. This study aims to develop a predictive model for malnutrition in hospitalized elderly cancer patients.
Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.
International journal of computer assisted radiology and surgery
Jun 14, 2024
PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As thes...
Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.
Internal and emergency medicine
Jun 14, 2024
This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of ischemic left ventricular systolic dysfunction(LVSD) patients receiving either percutaneous coronary ...
Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.
ESC heart failure
Jun 14, 2024
AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes....
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