AIMC Topic: Aged

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Dupless: Toward a patient-friendly approach for erectile dysfunction nature differentiation - a study of 291 penile duplex Doppler ultrasound assessments.

Andrology
BACKGROUND: Erectile dysfunction (ED) is a condition commonly classified as either psychogenic or organic. Traditional age-based categorizations are considered overly simplistic, yet many clinicians continue to rely on initial evaluation-patient symp...

ScreenDx, an artificial intelligence-based algorithm for the incidental detection of pulmonary fibrosis.

The American journal of the medical sciences
BACKGROUND: Nonspecific symptoms and variability in radiographic reporting patterns contribute to a diagnostic delay of the diagnosis of pulmonary fibrosis. An attractive solution is the use of machine-learning algorithms to screen for radiographic f...

Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction.

Clinical medicine (London, England)
OBJECTIVE: We aim to develop and validate an interpretable machine-learning model that can provide critical information for the clinical treatment of critically ill patients with acute myocardial infarction (AMI).

Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort.

Diabetes, obesity & metabolism
AIMS: This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms.

Contribution of Structure Learning Algorithms in Social Epidemiology: Application to Real-World Data.

International journal of environmental research and public health
Epidemiologists often handle large datasets with numerous variables and are currently seeing a growing wealth of techniques for data analysis, such as machine learning. Critical aspects involve addressing causality, often based on observational data,...

Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.

BMJ health & care informatics
OBJECTIVES: To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patients in real time using effluent-measured biomarkers of the mesothelial-to-mesenchymal transition (MMT)...

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

Breast cancer research : BCR
OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical features to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breas...

Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.

BMC geriatrics
OBJECTIVES: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.

Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.

Scientific reports
Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challen...