AIMC Topic: Machine Learning

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Interpretable machine learning models for survival prediction in prostate cancer bone metastases.

Scientific reports
Prostate cancer bone metastasis (PCBM) is a highly lethal condition with limited survival. Accurate survival prediction is essential for managing these typically incurable patients. However, existing clinical models lack precision. This study seeks t...

Machine learning models to predict the zero-fragment rate and lower pole access with FANS during flexible Ureteroscopy-an EAU section of endourology study.

World journal of urology
INTRODUCTION: Suction devices such as flexible and navigable suction ureteral access sheath (FANS) are promising tools to reach the zero-fragment rate (ZFR) after flexible ureteroscopy (FURS) and laser lithotripsy. FANS could especially be useful for...

Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn's disease.

Annals of medicine
BACKGROUND: Crohn's disease (CD) is a chronic inflammatory bowel disease, with infliximab (IFX) commonly used for treatment. However, no clinically applicable model currently exists to predict the response of patients with CD to IFX therapy. Given th...

Leveraging machine-learning techniques to detect recurrences in cancer registry data: A multi-registry validation study using German lung cancer data.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Cancer recurrence and progression, once seen as markers of poor prognosis, are now considered manageable aspects of long-term care. Advances in treatment have extended survival, emphasizing the need for representative epidemiological info...

Assessing chronic obstructive pulmonary disease risk based on exhalation and cough sounds.

Biomedical engineering online
BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD), a progressively worsening respiratory condition, severely impacts patient quality of life. Early risk assessment can improve treatment outcomes and lessen healthcare burdens. How...

Employing machine learning for early detection of poly-victimization in rural children: a survey study in China's Chaoshan region.

BMC public health
BACKGROUND: Poly-victimization (PV), encompassing multiple forms of victimization including physical abuse, emotional maltreatment, neglect, and peer violence, poses a significant public health challenge among children, particularly in rural areas wi...

Assessing psychological resilience and its influencing factors in the MSM population by machine learning.

Scientific reports
This study assesses the influence of social support, self-esteem, depression, and education on psychological resilience among men who have sex with men (MSM) to inform policy-making. Data were collected from 1,070 MSM via an online survey in Zhejiang...

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.

Scientific reports
Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 un...

A modular fluorescent camera unit for wound imaging.

Communications biology
Advanced imaging tools are revolutionizing the diagnosis, treatment, and monitoring of medical conditions, offering unprecedented insights into live cell behavior and biophysical markers. We introduce a modular, hand-held fluorescent microscope featu...

Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China.

BMJ open
OBJECTIVE: To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced decision-making and targeted health management in integrat...