AIMC Topic: Risk Factors

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Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment.

Journal of acquired immune deficiency syndromes (1999)
OBJECTIVE: Universal HIV screening programs are costly, labor intensive, and often fail to identify high-risk individuals. Automated risk assessment methods that leverage longitudinal electronic health records (EHRs) could catalyze targeted screening...

Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in EspaƱa [MADR-E]). The aim is to ...

Factors Associated with HIV Viral Suppression Among Transgender Women in Lima, Peru.

LGBT health
PURPOSE: Globally, transgender women (TGW) experience a high burden of adverse health outcomes, including a high prevalence of HIV and sexually transmitted infections (STIs) as well as psychiatric disorders and substance use disorders. To address gap...

WAITING DAAS LIST MORTALITY IMPACT IN HCV CIRRHOTIC PATIENTS.

Arquivos de gastroenterologia
BACKGROUND: The infection for the hepatitis C virus (HCV) is a leading cause of liver-related morbidity and mortality through its evolution to liver cirrhosis, end-stage liver complications and hepatocellular carcinoma. Currently, the new drugs for t...

A comparison of logistic regression and artificial neural networks in predicting central lymph node metastases in papillary thyroid microcarcinoma.

Annali italiani di chirurgia
OBJECTIVE: Prophylactic central lymph node dissection(CLND) is a controversial issue in papillary thyroid microcarcinoma( PTMC) patients without lymphatic metastasis. Artificial neural network(ANN) has been proposed as an alternative statistical tech...

AI Tackles Hospital Infections: Machine Learning Is Helping Clinicians.

IEEE pulse
For Ashley Zappia (Figure 1), getting her hands dirty was part of her job. Even though she always tried to remain as clean as possible, her work as a nursing aide at a Southern California hospital required a lot of diapering, changing, and other hand...

Does Robotic Beating Heart Connector Totally Endoscopic Coronary Artery Bypass Bridge the Gender Gap in Coronary Bypass Surgery?

Innovations (Philadelphia, Pa.)
OBJECTIVE: Previous studies have shown that women carry a higher risk of morbidity and mortality after coronary artery bypass surgery. We investigated gender differences in risk factors and outcomes in our patients undergoing robotic beating heart co...

Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide ex...

Comparison of Machine Learning Algorithms for the Prediction of Preventable Hospital Readmissions.

Journal for healthcare quality : official publication of the National Association for Healthcare Quality
A diverse universe of statistical models in the literature aim to help hospitals understand the risk factors of their preventable readmissions. However, these models are usually not necessarily applicable in other contexts, fail to achieve good discr...