This Project Grant award from the National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), funds the development and validation of a machine learning-based fetal monitoring system to predict and prevent fetal hypoxia. The $261,310 award will support the creation of a high-sensitivity, high-specificity predictive model using a multi-site dataset of 50,000 fetal monitoring tracings coupled with...
This Project Grant award from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), provides $434,500 to the Research Institute at Nationwide Children's Hospital to develop an interpretable machine learning model that leverages maternal and infant electronic health record data to identify neonates in Level III NICUs who are most likely to benefit from early-life genome...
This $250,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of algorithms for real-time dynamic risk identification and monitoring of streaming data, particularly in the domains of electronic medical records, mobile health, and supply chain. The key objectives are to create a unified framework for dynamic risk detection that can be incorporated into...
This Cooperative Agreement award from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) provides $943,819 to Neocare Innovations, Inc. to develop an integrated biomedical platform and custom algorithm to optimize feeding protocols for preterm infants in neonatal intensive care units (NICUs). The project aims to create a device that uses big data on breathing patterns to quantify the synchronization of breathing and swallowing in...
This Project Grant award of $291,523 from the National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), aims to develop an AI-driven Total Parenteral Nutrition (TPN) platform for premature newborns in neonatal intensive care units (NICUs). The key goals are to create standardized TPN units for cost-effective and personalized nutrition, and validate the TPN2.0 platform against the best standard of...
The National Science Foundation awarded a $275,990 Small Business Technology Transfer Phase I Project Grant to CB Enterprises of Santa Monica, California under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084). The grant funds the development of an integrated biomedical platform and custom algorithm to optimize feeding protocols for preterm infants. The platform utilizes sensors and data analytics to precisely measure infant breathing patterns during feeding, providing...
This Project Grant award of $903,135 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of an automated pain recognition artificial intelligence (AI)-powered monitoring system (PRAMS) for newborn babies. The goal is to create a system grounded in biological evidence of pain and supervised by nurses, leveraging a novel hybrid federated learning approach to train the AI model across diverse clinical...
This Cooperative Agreement award of $1,278,689 from the National Institute of Neurological Disorders and Stroke (NINDS), under the Drug Use and Addiction Research Programs (CFDA 93.279), aims to develop an automated Neonatal Pain Monitoring System (NPMS) and pain management protocol to objectively measure and minimize postoperative pain in newborns. The project has two key objectives: 1) Develop and validate AI-driven neonatal pain biomarker signatures and an automated NPMS to continuously...
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 Project Grant award of $642,859 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), under the Child Health and Human Development Extramural Research program (CFDA 93.865), will support research to develop and validate approaches for detecting and predicting prescribing errors in inpatient pediatric settings. The key goals are to: 1) validate an algorithmic approach to detect pediatric prescribing errors at scale, 2) validate the use of...