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...
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...
Hierarchical Condition Category (HCC) coding plays a pivotal role in federally regulated risk adjustment payment models, ensuring accurate reimbursement for health insurance plans and better care for managed populations. Providers...
There is overwhelming evidence from academic research and industry benchmarks that domain-specific and task-specific large language models outperform general-purpose LLMs across multiple dimensions: Accuracy, veracity, human preference, and cost. This...
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