AIMC Topic: Machine Learning

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[Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions].

Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology
To screen the influencing factors of hypertensive heart disease (HHD), establish the predictive model of HHD, and provide early warning for the occurrence of HHD. Select the patients diagnosed as hypertensive heart disease or hypertensionfrom Janua...

Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning.

Families, systems & health : the journal of collaborative family healthcare
Frequent emergency department (ED) use has been operationalized in research, clinical practice, and policy as number of visits to the ED, despite the fact that this definition lacks empirical evidence and theoretical foundation. To date, there are no...

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and a...

Computing the Hazard Ratios Associated With Explanatory Variables Using Machine Learning Models of Survival Data.

JCO clinical cancer informatics
PURPOSE: The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models have been developed and applied to the surv...

Independent Validation of a Comprehensive Machine Learning Approach Predicting Survival After Radiotherapy for Bone Metastases.

Anticancer research
BACKGROUND/AIM: The aim of this study was to analyze the survival predictions obtained from a web platform allowing for computation of the so-called Bone Metastases Ensemble Trees for Survival (BMETS). This prediction model is based on a machine lear...

External Validation of the Bone Metastases Ensemble Trees for Survival (BMETS) Machine Learning Model to Predict Survival in Patients With Symptomatic Bone Metastases.

JCO clinical cancer informatics
PURPOSE: The Bone Metastases Ensemble Trees for Survival (BMETS) model uses a machine learning algorithm to estimate survival time following consultation for palliative radiation therapy for symptomatic bone metastases (SBM). BMETS was developed at a...

Generative models for predicting chemical composition of gallstones.

European review for medical and pharmacological sciences
OBJECTIVE: As we know, gallstones are a gallbladder disease with high incidence around the world. As the population has aged and living habits have changed, the incidence of the disease is increasing year by year. Gallstones are mainly classified int...

Letters.

The journal of the American Academy of Psychiatry and the Law

Opening the black box of AI-Medicine.

Journal of gastroenterology and hepatology
One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a "black box." Machine Learning (ML) can assist in rea...