AIMC Topic: Treatment Failure

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Machine Learning Model Predictors of Intrapleural Tissue Plasminogen Activator and DNase Failure in Pleural Infection: A Multicenter Study.

Annals of the American Thoracic Society
Intrapleural enzyme therapy (IET) with tissue plasminogen activator (tPA) and DNase has been shown to reduce the need for surgical intervention for complicated parapneumonic effusion/empyema (CPPE/empyema). Failure of IET may lead to delayed care an...

Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.

JCO clinical cancer informatics
PURPOSE: We developed a deep neural network that queries the lung computed tomography-derived feature space to identify radiation sensitivity parameters that can predict treatment failures and hence guide the individualization of radiotherapy dose. I...

Feasibility of salvage robotic partial nephrectomy after ablative treatment failure (UroCCR-62 study).

Minerva urology and nephrology
BACKGROUND: Ablative therapies (AT) are increasingly being offered to patients with kidney tumors. In cases of failure or local relapse, salvage surgery may be required. Such procedures often require an open approach, are difficult and have received ...

Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm.

International journal of mycobacteriology
BACKGROUND: Tuberculosis (TB) is a disease that mainly affects human lungs. It can be fatal if the treatment is delayed. This study investigates the prediction of treatment failure of TB patients focusing on the features which contributes mostly for ...

Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Transplantation
BACKGROUND: The ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritiz...

A mathematical framework for virtual IMRT QA using machine learning.

Medical physics
PURPOSE: It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The...

Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.

Journal of acquired immune deficiency syndromes (1999)
OBJECTIVE: Regular HIV RNA testing for all HIV-positive patients on antiretroviral therapy (ART) is expensive and has low yield since most tests are undetectable. Selective testing of those at higher risk of failure may improve efficiency. We investi...