A Prospective Comparison of Large Language Models for Early Prediction of Sepsis.

We present a comparative study on the performance of two popular open-source large language models for early prediction of sepsis: Llama-3 8B and Mixtral 8x7B. The primary goal was to determine whether a smaller model could achieve comparable predictive accuracy to a significantly larger model in the context of sepsis prediction using clinical data.Our proposed LLM-based sepsis prediction system, COMPOSER-LLM, enhances the previously published COMPOSER model, which utilizes structured EHR data to generate hourly sepsis risk scores.

Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.

Objectives: This study describes the development and deployment of a machine learning (ML) model called Vent.io to predict mechanical ventilation (MV).

Materials And Methods: We trained Vent.

Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.

Objectives: Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).

Materials And Methods: Nephrocast was trained and validated, temporally and prospectively, using electronic health record data of adult patients admitted to the ICU in the University of California San Diego Health (UCSDH) between January 1, 2016 and June 22, 2024.

The impact of laboratory data missingness on sepsis diagnosis timeliness.

Objective: To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

Materials And Methods: In adult patients admitted to 2 University of California San Diego (UCSD) hospitals from January 1, 2021 to June 30, 2024, we evaluated the relative time of organ failure ( ) and time of clinical suspicion of sepsis ( ) in patients with sepsis according to the Centers for Medicare & Medicaid Services (CMS) definition.

Results: Of the patients studied, 48.

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.

Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Background: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance.

Objective: Can a deep learning deterioration prediction model (Deep Learning Enhanced Triage and Emergency Response for Inpatient Optimization [DETERIO]) based on a consensus definition of deterioration (the Adult Inpatient Decompensation Event [AIDE] criteria) and that approaches deterioration as a state "value-estimation" problem outperform a commercially available deterioration score?

Derivation Cohort: The derivation cohort contained retrospective patient data collected from both inpatient services (inpatient) and emergency departments (EDs) of two hospitals within the University of California San Diego Health System.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments.

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.

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

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

Unsupervised Detection and Correction of Model Calibration Shift at Test-Time.

The wide adoption of predictive models into clinical practice require generalizability across hospitals and maintenance of consistent performance across time. Model calibration shift, caused by factors such as changes in prevalence rates or data distribution shift, can affect the generalizability of such models. In this work, we propose a model calibration detection and correction (CaDC) method, specifically designed to utilize only unlabeled data at a target hospital.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments.

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.

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

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

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.

Background: Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models.

Objective: The aim of this study was to optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility.

Methods: We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities.

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems.

Inclusion of social determinants of health improves sepsis readmission prediction models.

Objective: Sepsis has a high rate of 30-day unplanned readmissions. Predictive modeling has been suggested as a tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most predictive factors in such models are not actionable.

Artificial intelligence sepsis prediction algorithm learns to say "I don't know".

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

A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU.

Objectives: To train a model to predict vasopressor use in ICU patients with sepsis and optimize external performance across hospital systems using domain adaptation, a transfer learning approach.

Design: Observational cohort study.

Setting: Two academic medical centers from January 2014 to June 2017.

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives.