This $305,000 project grant awarded by the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training program (CFDA 93.859), supports the development of a clinical decision support system (CDSS) called Sepsis FLO. The CDSS is designed to precisely guide blood volume diagnosis and treatment for sepsis patients. Key activities include: 1) Developing an alpha prototype of the CDSS through user-centered design; 2) Validating three functional...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS) under CFDA Program 93.859 - Biomedical Research and Research Training, provides $312,974 to the University of California, San Diego (UCSD) to develop multi-modal foundation models for early sepsis detection. The goal is to create accurate, efficient, and interpretable machine learning models that can leverage large-scale electronic health record data to identify early signs of sepsis, a life-threatening...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $1,200,000 to the University of California, San Diego (UCSD) to develop advanced foundation models and algorithms for early detection of sepsis using clinical time series data. The 4-year project aims to create innovative technologies, including a new transformer-based model and optimization techniques, to accurately and efficiently process complex clinical...
This $455,000 Project Grant awarded by the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859) aims to decrease pediatric sepsis mortality. The key products and services to be delivered include: Development of an AI-guided early identification tool for sepsis in children. Sepsis phenotyping and creation of digital twin patient models to predict personalized treatment responses. Creation of an extracellular vesicle-based...
The federal Project Grant award R01AI188576, funded by the National Institute of Allergy and Infectious Diseases (NIAID) under the Allergy and Infectious Diseases Research program (CFDA 93.855), is focused on developing a human-centered artificial intelligence (HCAI) system to improve early prediction and decision-making for sepsis. The key products and services to be delivered under this $444,506 award include: 1) creating a deidentified database with complete electronic health record (EHR)...
This $700,000 Project Grant award from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) supports the clinical validation of Prenosis, Inc.'s IMMUNIX platform, a precision medicine endotyping solution for sepsis. The project aims to: 1) validate the IMMUNOSCAN multiplex rapid test and mapping functions to Luminex immunoassays; 2) validate a parsimonious model for point-of-care endotyping; and 3) demonstrate the ability to...
This $294,244 Project Grant award, funded by the Department of Health and Human Services (DHHS) Agency for Healthcare Research and Quality (AHRQ) under the Research on Healthcare Costs, Quality and Outcomes (CFDA 93.226) program, supports research to identify sepsis phenotypes associated with antibiotic-resistant pathogens. The project aims to leverage machine learning techniques, including large language models applied to clinical notes, to augment electronic health record (EHR) data and define...
This $729,821 Project Grant award from the National Institute of Environmental Health Sciences (NIEHS) under the Medical Library Assistance (CFDA 93.879) program supports the development of artificial intelligence (AI) and machine learning (ML) methods for real-time monitoring and updating of clinical decision support (CDS) systems. The goal is to reduce health disparities that may arise from the use of CDS tools. The key products of this work include: Fair ML models trained on retrospective...
This federal Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), is funding the development of a continuous, real-time, automated smart catheter analyzer for early identification of sepsis through urine biomarkers. The $412,250 award to Arizona State University (ASU) will enable the design, development, and refinement of this smart catheter device prototype over a 2-year R61 (Phase I) and 1-year R33...
This $1,199,514 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) aims to develop a comprehensive and scalable risk prediction model, termed "PRECISE", that fuses imaging and non-imaging data to enable early detection of pancreatic ductal adenocarcinoma (PDAC) in asymptomatic individuals. The key products and services to be delivered include: Development of deep learning models to segment imaging biomarkers from abdominal...
DEEP-CDS: DEEP LEARNING SEMANTIC DATA LAKE FOR CLINICAL DECISION SUPPORT - MORE THAN 5 MILLION PATIENTS ARE ADMITTED ANNUALLY TO UNITED STATES ICUS WITH AVERAGE MORTALITY RATE REPORTED RANGING FROM 8-19%, OR ABOUT 500,000 DEATHS ANNUALLY. SEPSIS IS THE LEADING CAUSE OF IN-HOSPITAL MORTALITY, WHERE ONE IN THREE INPATIENT DEATHS ARE DUE TO SEPSIS. INCIDENCE OF SEPSIS HAS BEEN INCREASING WITH 1.7 MILLION SEPSIS CASES AND 270,000 DEATHS PER YEAR. EARLY IDENTIFICATION OF DETERIORATION HAS BEEN SHOWN TO REDUCE THE NEED FOR PATIENT TRANSFER TO HIGHER CARE UNITS, REDUCE LENGTHS OF STAY, AND IMPROVE SURVIVAL RATES. EACH HOUR OF DELAY IN ICU ADMISSION HAS BEEN ASSOCIATED WITH A 1.5% INCREASED RISK OF ICU DEATH AND A 1% INCREASE IN RISK OF HOSPITAL DEATH. MANY STUDIES SUPPORT THAT THERE IS AN INCREASE IN MORTALITY RATE FOR EVERY HOUR DELAY IN ANTIBIOTICS. PAIRING PATIENT RISK STRATIFICATION WITH APPROPRIATE LEVELS OF HOSPITAL INTERVENTION IS ESSENTIAL TO REDUCE RISK OF MORTALITY. PATIENTS IN INTERMEDIATE UNITS BETWEEN THE LEVELS OF MONITORING FOUND IN FLOOR UNITS AND ICUS ARE ESPECIALLY DIFFICULT TO PREDICT POSSIBILITY OF CONDITION DETERIORATION. AUTOMATED MONITORING, ALERTS, AND TREND ANALYSIS ARE ESSENTIAL TO IDENTIFYING AND PROACTIVELY INTERVENING PATIENTS UNDER DURESS. CURRENT METHODS OF MONITORING PATIENT HEALTH HAVE LOW SPECIFICITY AND HAVE SIGNIFICANT ROOM FOR IMPROVEMENT. THIS PROJECT WILL DEVELOP DEEP-CDS, A CLOUD-BASED DEEP LEARNING SYSTEM FOR CONTEXT-SENSITIVE CLINICAL DECISION SUPPORT IN MONITORING AND PREDICTING THE DETERIORATION OF PATIENT HEALTH AND PROGRESSION OF SEPSIS RISK FACTORS IN REAL-TIME TO IMPROVE OUTCOMES AND OPTIMIZE THE MANAGEMENT OF CARE ACROSS THE HOSPITAL POPULATION. TO SUPPORT THE CLINICAL CARE TEAM, DEEP-CDS PROVIDES TEAM MEMBERS WITH (A) A CLINICAL CARE KNOWLEDGEBASE, (B) AN EARLY WARNING SCORE FOR DETERIORATING HEALTH CONDITIONS, (C) A MODEL FOR PREDICTING SEPTIC CONDITIONS, (D) EVIDENCE-BASED CLINICAL PRACTICE GUIDELINES, AND (E) VISUALIZATION OF PATIENT HEALTH STATUS TRENDS. DEEP-CDS ADDRESSES NIGMS PRIORITIES FOR SMALL BUSINESS DEVELOPMENT OF SEPSIS DIAGNOSTICS AND THERAPEUTICS, NOT-GM-20- 028: 1) DIAGNOSTIC TOOLS FOR EMERGENCY DEPARTMENT SETTINGS; 2) PREDICTIVE CLINICAL ALGORITHMS AND POINT-OF-CARE DIAGNOSTICS; 3) TECHNOLOGIES THAT COMBINE VARIOUS TYPES OF DATA FOR DIAGNOSIS OF SEPSIS PATIENTS; AND 4) CLINICAL DECISION SUPPORT, INCLUDING USE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES, TO DEVELOP TOOLS FOR EARLY RECOGNITION OF SEPSIS, ASSESSMENT OF TREATMENT RESPONSES AND PATIENT DETERIORATION, AND LONG-TERM PROGNOSIS PREDICTION IN VARIOUS CARE SETTINGS.