AIMC Topic: Risk Factors

Clear Filters Showing 2011 to 2020 of 2857 articles

Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.

Digestive diseases and sciences
Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evalua...

Computer-assisted prediction of atherosclerotic intimal thickness based on weight of adrenal gland, interleukin-6 concentration, and neural networks.

The Journal of international medical research
OBJECTIVE: Atherosclerosis (AS) is the main pathological basis of ischemic cardio-cerebrovascular diseases, and the intimal thickness (IT) of large arteries is regarded as a powerful evaluation indicator for AS. We established an effective neural net...

Efficient learning from big data for cancer risk modeling: A case study with melanoma.

Computers in biology and medicine
BACKGROUND: Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and computational performance. We present a case study of a cloud-based approach to learning from de-identifi...

Predicting coronary artery disease: a comparison between two data mining algorithms.

BMC public health
BACKGROUND: Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data minin...

Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants.

Journal of Korean medical science
BACKGROUND: Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.

Predicting childhood obesity using electronic health records and publicly available data.

PloS one
BACKGROUND: Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. ...

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.

Journal of biomedical informatics
OBJECTIVE: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated...

Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

BioMed research international
BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive ...

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC).