December 1, 2025 • Miami, Florida Our foundational research, "Predicting Sepsis Onset in ICUs: A Graph Neural Network Framework with Uncertainty Estimation," has been accepted by ACM Health, showcasing the Deep-CDS technical performance that forms the basis of the Medicasci® Sepsis Risk Prediction system. Stay tuned for the our full ACM Health manuscript.
September 1, 2025 • Miami, Florida INFOTECH Soft has successfully completed the NIGMS-funded SBIR grant "Deep Learning Semantic Data Lake for Clinical Decision Support". Deep-CDS is a cloud-based semantic data lake for clinical decision support that integrates data across a hospital’s informatics resources to capture a comprehensive picture of each patient’s health status and utilizes a deep graph generative model to assess risk of developing sepsis-related symptoms and complication
March 2, 2023 • Miami, Florida INFOTECH Soft has begun Phase II research and development of our NIGMS-funded SBIR grant "Deep Learning Semantic Data Lake for Clinical Decision Support". Deep-CDS is a cloud-based semantic data lake for clinical decision support that integrates data across a hospital’s informatics resources to capture a comprehensive picture of each patient’s health status and utilizes a deep graph generative model to assess risk of developing sepsis-related symptoms
September 7, 2022 • Miami, Florida The National Institute of General Medical Sciences has funded our SBIR grant "Deep Learning Semantic Data Lake for Clinical Decision Support". Deep-CDS is a cloud-based semantic data lake for clinical decision support that integrates data across a hospital’s informatics resources to capture a comprehensive picture of each patient’s health status and utilizes a deep graph generative model to assess risk of developing sepsis-related symptoms an
May 11, 2021 • Miami, Florida INFOTECH Soft has been awarded US Patent 11,003,661 awarded for “System for rapid ingestion, semantic modeling and semantic querying over computer clusters“. May, 11, 2021 A computer-implemented system within a computational cluster for aggregating data from a plurality of heterogeneous data sets into a homogeneous representation within a distributed, fault tolerant data source for the subsequent high-throughput retrieval, integration, and ana