Data integration has been an enormous challenge in healthcare for decades. This session shows how recent advances across the AI ecosystem combine into a game changer: A solution that automatically converts large amounts of raw, multi-format, multi-modal, untrusted medical data into coherent longitudinal patient stories in an industry-standard format. This talk shows this John Snow Labs solution in action, including:1. Building a unified view of each patient over time.2. Building this unified patient view from multi-modal source data.3. Reasoning at the patient level.4. Predict risk scores and calculating clinical measures.5. Explaining results with full traceability.6. Building patient cohorts with natural language queries.
Data integration has been an enormous challenge in healthcare for decades. This session shows how recent advances across the AI ecosystem combine into a game changer: A solution that automatically...
The deployment of NVIDIA BioNeMo Large Language Model (LLM) on Oracle Cloud Infrastructure (OCI) with NVIDIA Inference Microservices represents a significant advancement in the application of artificial intelligence in biotechnology....
The MultiCaRe Dataset is a multimodal case report dataset that contains data from 75,382 open-access PubMed Central articles spanning the period from 1990 to 2023. It includes 96,428 clinical cases...
Dandelion Health is a provider of multimodal, longitudinal clinical data for healthcare innovators. This session shows how it built a de-identification process for free-text clinical notes, with John Snow Labs’...
The emergence of precision oncology necessitates a comprehensive understanding of how genetic, epigenetic, and other factors influence tumor behavior and response to treatment regimens. This understanding is crucial for translating...