AIMC Topic: Risk Factors

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Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk ...

Predicting lymphoma outcomes and risk factors in patients with primary Sjögren's Syndrome using gradient boosting tree ensembles.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Primary Sjogren's Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS patients are prone to lymphoma development. In this work, we use clinical data from 449 pSS patients...

Detection and Classification of Chronic Total Occlusion lesions using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular disease (CVD) is one of the diseases with the highest mortality rate in modern society, while chronic total occlusion (CTO) is the initial factor that influences the success rate of percutaneous coronary intervention (PCI), which is on...

A Predictive Model for Determining Patients Not Requiring Prolonged Hospital Length of Stay After Elective Primary Total Hip Arthroplasty.

Anesthesia and analgesia
BACKGROUND: Hospital length of stay (LOS) is an important quality metric for total hip arthroplasty. Accurately predicting LOS is important to expectantly manage bed utilization and other hospital resources. We aimed to develop a predictive model for...

Quantifying risk factors in medical reports with a context-aware linear model.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behavi...

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

JCO clinical cancer informatics
PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can ...

Exploring the survival prognosis of lung adenocarcinoma based on the cancer genome atlas database using artificial neural network.

Medicine
The aim of this study was to investigate the clinical factors affecting the survival prognosis of lung adenocarcinoma, and to establish a predictive model of survival prognosis of lung adenocarcinoma by artificial neural network.Download the cancer g...

Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces.

Medicine and science in sports and exercise
INTRODUCTION: Musculoskeletal injury rates in military personnel remain unacceptably high. Application of machine learning algorithms could be useful in multivariate models to predict injury in this population. The purpose of this study was to invest...

Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...

Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, mak...