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...
The American journal of tropical medicine and hygiene
Aug 18, 2017
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 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...
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.
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...
Diabetes research and clinical practice
Nov 9, 2016
AIMS: Evaluate efficacy and hypoglycaemia according to concomitant oral antidiabetes drug (OAD) in people with type 2 diabetes initiating insulin glargine 100U/mL (Gla-100) or neutral protamine Hagedorn (NPH) insulin once daily.
Circulation. Cardiovascular quality and outcomes
Nov 8, 2016
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.
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...
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 ...
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