As a serious blood infection disease, sepsis is characterized by a high mortality risk and many complications. Accurate assessment of mortality risk of patients with sepsis can help physicians in Intensive Care Unit make optimal clinical decisions, w...
BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We exten...
BACKGROUND: Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create dig...
Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. Accurate and reliable predictive tools that enable early risk stratification can facilitate intervent...
Neural networks : the official journal of the International Neural Network Society
Aug 12, 2023
Accurate estimation of in-hospital mortality based on patients' physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality iss...
The European journal of health economics : HEPAC : health economics in prevention and care
Aug 6, 2023
We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effe...
Predicting the patient's in-hospital mortality from the historical Electronic Medical Records (EMRs) can assist physicians to make clinical decisions and assign medical resources. In recent years, researchers proposed many deep learning methods to pr...
Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models ...
BACKGROUND: Early Warning Scores (EWS) monitor inpatient deterioration predominantly using vital signs. We evaluated inpatient outcomes after implementing an Artificial Intelligence (AI) based intervention in our local EWS.
Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-lea...
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