MATCHING UNSTRUCTURED TEXT TO CLINICAL ONTOLOGIES

    公开(公告)号:US20240112804A1

    公开(公告)日:2024-04-04

    申请号:US18375914

    申请日:2023-10-02

    申请人: Google LLC

    摘要: A computer-implemented method for matching unstructured text to ontology entities in a clinical ontology is described. The method includes receiving one or more clinical notes associated with a patient; for each of the one or more clinical notes: extracting, using a neural network, one or more text spans from unstructured text in each clinical note, each of the one or more text spans identifying a respective input phrase in the unstructured text; for each of the one or more text spans, matching, using a text matcher, the text span with a respective output ontology entity from an ontology, the respective output ontology entity relating to a clinical condition of the patient; and outputting data defining the one or more text spans and the respective output ontology entity for each of the one or more text spans.

    SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS

    公开(公告)号:US20240111999A1

    公开(公告)日:2024-04-04

    申请号:US18375960

    申请日:2023-10-02

    申请人: Google LLC

    IPC分类号: G06N3/0455 G06N3/048

    CPC分类号: G06N3/0455 G06N3/048

    摘要: A multi-task neural network system is described. The system includes a shared neural network configured to receive as input a text span from a clinical note, and for each of one or more text segments in the text span, processing the text segment to generate a set of text segment embeddings. The system further includes a segmentation neural network configured to, for each of the one or more text segments, process the respective set of text segment embeddings to determine whether the text segment is a section title or not. The system further includes a section type classification neural network configured to, for each of the one or more text segments, process the respective set of text segment embeddings to classify the text segment into a section type of a plurality of section types.