AIMC Topic: Risk Factors

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Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction ...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophren...

Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.

Journal of gynecologic oncology
OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.

Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions.

JAMA network open
IMPORTANCE: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.

Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures.

The Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons
Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and internal fixation (ORIF). Several patient-related risk factors may contribute to poor short-term outcomes, and machine learning may be a valua...

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome.

BMC medical informatics and decision making
BACKGROUND: Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important ...

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis.

BMJ open
INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to...