Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

Objective: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Materials And Methods: We conducted a multi-center retrospective cohort study using data from the All of Us data repository.

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

Objectives: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables.

Design: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data.

Settings: Thirty-five hospitals across the United States from 2017 to 2021.

Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data.

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk.

Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability.

Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study.

Background: Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient's physiological state and the interventions they receive.

Objective: We aimed to develop a novel approach for deriving clinical phenotypes using unsupervised learning and transition modeling.

Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data.

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk.

AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production.

Sepsis, a dysregulated immune response to infection, has been the leading cause of morbidity and mortality in critically ill patients. Multiple studies have demonstrated improved survival outcomes when early treatment is initiated for septic patients. In our previous work, we developed a real-time machine learning algorithm capable of predicting onset of sepsis four to six hours prior to clinical recognition.