Sepsis is a major public health concern with significant morbidity, mortality, and healthcare expenses. Early detection antibiotic treatment of sepsis improve outcomes. However, although professional critical care societies have proposed new clinical criteria that aid recognition, the fundamental need for early remains unmet. In response, researchers algorithms detection, but directly comparing such methods has not been possible because different patient cohorts, variables criteria,...

Abstract Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess deep-learning model (COMPOSER) for prediction We completed before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within UC San Diego Health System. included 6217 adult septic patients from...

Atrial Fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, with a prevalence of 2% community. Not only it associated reduced quality life, but also increased risk stroke and myocardial infarction. Unfortunately, many cases AF are clinically silent undiagnosed, long-term monitoring difficult. Nonetheless, efforts at at-risk individuals detecting may yield significant public health benefit, as new-onset, asymptomatic would receive preventive therapies anticoagulants...

The PhysioNet/Computing in Cardiology Challenge focused on the early detection of sepsis from clinical data.A total 40,336 patient records two distinct hospital systems were shared with participants while 22,761 three sequestered as hidden test sets.Each record contained up to 40 measurements vital sign, laboratory, and demographics data for over 2.5 million hourly time windows 15 points.We used Sepsis-3 criteria define onset sepsis.We challenged design automated, opensource algorithms...

Abstract Sepsis is a leading cause of morbidity and mortality worldwide. Early identification sepsis important as it allows timely administration potentially life-saving resuscitation antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), deep learning model for the early prediction sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift data...

Detection of atrial fibrillation (AF), a type cardiac arrhythmia, is difficult since many cases AF are usually clinically silent and undiagnosed. In particular paroxysmal form that occurs occasionally, has higher probability being undetected. this work, we present an attention based deep learning framework for detection episodes from sequence windows. Time-frequency representation 30 seconds recording windows, over 10 minute data segment, fed sequentially into convolutional neural network...

This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and deep convolutional neural network (CNN).

The PhysioNet/Computing in Cardiology Challenge focused on the early detection of sepsis from clinical data. A total 40,336 patient records two distinct hospital systems were shared with participants while 22,761 three sequestered as hidden test sets. Each record contained up to 40 measurements vital sign, laboratory, and demographics data for over 2.5 million hourly time windows 15 points. We used Sepsis-3 criteria define onset sepsis.We challenged design automated, open-source algorithms...

Sepsis has a high rate of 30-day unplanned readmissions. Predictive modeling been suggested as tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most factors in such are not actionable.Data from patients enrolled the AllofUs Research Program cohort 35 hospitals were used develop multicenter validated sepsis-related model that incorporates clinical social determinants health (SDH) predict cases identified using concepts represented...

Objective and Approach: Sepsis, a dysregulated immune-mediated host response to infection, is the leading cause of morbidity mortality in critically ill patients. Indices heart rate variability complexity (such as entropy) have been proposed surrogate markers neuro-immune system dysregulation with diseases such sepsis. However, these indices only provide an average, one dimensional description complex neuro-physiological interactions. We propose novel multiscale network construction analysis...

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes patient care at a new institution (site-specific training) comes the cost performance degradation when applied external cohorts. To exploit full potential cross-institutional big data, learning systems must gain ability transfer their knowledge across institutional boundaries and without forgetting previously learned patterns. In this work, we developed privacy-preserving algorithm named...

Abstract Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection intervention have been shown improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated the COMPOSER enhance early sepsis prediction. For high-uncertainty predictions, LLM...

Ventricular contractions in healthy individuals normally follow the of atria to facilitate more efficient pump action and cardiac output. With a ventricular ectopic beat (VEB), volume within ventricles are pumped body's vessels before receiving blood from atria, thus causing inefficient circulation. VEBs tend cause perturbations instantaneous heart rate time series, making analysis variability inappropriate around such events, or requiring special treatment (such as signal averaging)....

OBJECTIVES: To train a model to predict vasopressor use in ICU patients with sepsis and optimize external performance across hospital systems using domain adaptation, transfer learning approach. DESIGN: Observational cohort study. SETTING: Two academic medical centers from January 2014 June 2017. PATIENTS: Data were analyzed 14,512 (9,423 at the development site 5,089 validation site) who admitted an met Center for Medicare Medicaid Services definition of severe either before or during stay....

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

BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential earlier clinical interventions that improve patient outcomes. Current are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, poor performance. OBJECTIVE: Can deep learning prediction model (Deep Learning Enhanced Triage Emergency Response Inpatient Optimization [DETERIO]) based consensus definition (the Adult...

Changes in heart rate (HR) and locomotor activity reflect changes autonomic physiology, behavior, mood. These systems may involve interrelated neural circuits that are altered psychiatric illness, yet their interactions poorly understood. We hypothesized between HR could be used to discriminate patients with schizophrenia from controls, would less able non-psychiatric controls.HR were recorded via wearable patches 16 19 healthy controls. Measures of signal complexity calculated over multiple...

Sepsis, a life-threatening organ dysfunction, is clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock failure, making one of the leading causes morbidity mortality in hospitals. Early detection timely antibiotics administration known save lives. In this work, we design prediction algorithm based on data from electronic health records (EHR) using deep learning approach. While most existing EHR-based...

ABSTRACT IMPORTANCE Objective and early identification of hospitalized patients, particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is great importance aid in delivering timely treatment. OBJECTIVE To develop, externally validate prospectively test a transparent deep learning algorithm for predicting 24 hours advance the need patients COVID-19. DESIGN Observational cohort study SETTING Two academic medical centers from January 01, 2016...

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present scalable, cloud-based, fault-tolerant platform is capable extracting processing electronic health record (EHR) data for any patient at time following admission transferring results back the EHR. This has been successfully deployed within UC San Diego Health system utilizes interoperable standards to enable...

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.