An AI-based solution that delivers a future-proof model using transfer learning which can be used to convert source-agnostic unstructured data into structured data. It supports the classification of artifacts and...
Providence St. Joseph Health’s (PSJH) unstructured data de-identification methodology relies on pre-trained BiLSTM-CNN-Char NER models provided by John Snow Labs. The PSJH Data science department evaluated John Snow Labs models...
A modern health care system generates a wide range of clinical data formats including structured, unstructured, image, and high-frequency waveform data. The volume of data is also growing quickly, enabling...
Medicare risk adjustment is a rule-based calculation, based on seven variables: ICD Codes of the patient’s diagnoses, age, gender, eligibility segment, entitlement reason, Medicaid status, and if the patient is...
Accelerating progress in personalized healthcare requires learning the causal relationships between diseases, genes, treatments, medications, labs, and other clinical information – at scale over a large population and time range....
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