AIMC Topic: Outcome Assessment, Health Care
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A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Dec 22, 2021
OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in ...
Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Dec 16, 2021
OBJECTIVES: This study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implemen...
Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Oct 9, 2021
OBJECTIVES: Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health techno...
Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.
Scientific reports
Oct 1, 2021
The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained ...
Federated learning for predicting clinical outcomes in patients with COVID-19.
Nature medicine
Sep 15, 2021
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe ...
Assessing outcomes of ear molding therapy by health care providers and convolutional neural network.
Scientific reports
Sep 9, 2021
Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we comp...
Explainable machine-learning predictions for complications after pediatric congenital heart surgery.
Scientific reports
Aug 26, 2021
The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of thera...
Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.
Critical care (London, England)
Aug 17, 2021
BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine le...
Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.
Human brain mapping
Aug 13, 2021
Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing thre...
Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.
STAR protocols
Jul 2, 2021
The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pipeline which predicts outcomes from real-time sequential clinical data. The strategy implemented in ...
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