相关论文: Explainable Machine Learning for ICU Readmission P…
Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome, reflecting both clinical severity and resource utilization. Accurate…
A hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a significant problem in the healthcare domain, as they lead to…
ICU readmission is associated with longer hospitalization, mortality and adverse outcomes. An early recognition of ICU re-admission can help prevent patients from worse situation and lower treatment cost. As the abundance of Electronics…
Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe…
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice,…
Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that imposes a significant burden on healthcare systems. ICU readmissions among AP patients are common, especially in severe cases, with rates…
Heart attack remain one of the greatest contributors to mortality in the United States and globally. Patients admitted to the intensive care unit (ICU) with diagnosed heart attack (myocardial infarction or MI) are at higher risk of death.…
Unplanned intensive care unit (ICU) readmission rate is an important metric for evaluating the quality of hospital care. Efficient and accurate prediction of ICU readmission risk can not only help prevent patients from inappropriate…
Sepsis is a leading cause of mortality in intensive care units (ICUs), yet existing research often relies on outdated datasets, non-reproducible preprocessing pipelines, and limited coverage of clinical interventions. We introduce…
This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for…
Hospital readmission, defined as patients being re-hospitalized shortly after discharge, is a critical concern as it impacts patient outcomes and healthcare costs. Identifying patients at risk of readmission allows for timely interventions,…
Hospital readmissions remain a challenge for healthcare systems, especially among patients with chronic conditions such as diabetes. Unplanned readmissions within 30 days are costly, strain hospital resources, and can indicate poor care…
Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction: lack of explainability, absence of deployment reliability infrastructure, and inadequate…
Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies--that is, to enable interventions to be targeted to the patients who will presumably benefit most. Despite taking advantage of…
Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days, after the discharge. The motivation is to help health…
Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have…
Early identification of intensive care patients at risk of in-hospital mortality enables timely intervention and efficient resource allocation. Despite high predictive performance, existing machine learning approaches lack transparency and…
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed…
Hospital readmissions are expensive and reflect the inadequacies in healthcare system. In the United States alone, treatment of readmitted diabetic patients exceeds 250 million dollars per year. Early identification of patients facing a…
Hospital readmission has become a critical metric of quality and cost of healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20% of patients who are readmitted within 30 days of discharge. Although several…