AIMC Topic: Incidence

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A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women.

Osteoarthritis and cartilage
OBJECTIVE: Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low...

A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China.

The American journal of tropical medicine and hygiene
It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the p...

Online cross-validation-based ensemble learning.

Statistics in medicine
Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinit...

Incidence and risk factors of inguinal hernia after robot-assisted radical prostatectomy.

World journal of surgical oncology
BACKGROUND: Robot-assisted radical prostatectomy (RARP) has now become a gold standard approach in radical prostatectomy. The aim of this study was to investigate incidence and risk factors of inguinal hernia (IH) after RARP.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

Epidemiology and infection
Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrat...

Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Using electronic health records data to predict events and onset of diseases is increasingly common. Relatively little is known, although, about the tradeoffs between data requirements and model utility.

Incidence of severe renal dysfunction among individuals taking warfarin and implications for non-vitamin K oral anticoagulants.

American heart journal
BACKGROUND: The purpose of this study is to assess incidence and risk factors for severe renal dysfunction in patients requiring oral anticoagulation to help guide initial drug choice and provide a rational basis for interval monitoring of renal func...

Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning.

PloS one
Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in ...