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- W4294975803 abstract "Assigning International Classification of Diseases (ICD) codes based on doctors' clinical diagnoses has historically been a difficult task performed by highly trained clinical coding experts. Recently, attempts have been made to use machine learning techniques at a coarse-grained level to automatically generate lists of medical codes from doctors' notes; however, the results are often difficult to interpret and validate. In this paper, we propose a fine-grained approach that focuses on one diagnosis at a time. We use ontology-based human knowledge to extract semantically related sentences from doctor's notes to support the use of deep learning for reliable training and classification. This fine-grained deep learning approach significantly reduces training load and improves scalability while providing users with a rationale for ICD code prediction. To demonstrate the effectiveness and advantages of our approach, we apply it to the MIMIC-III dataset and show how ICD-9 codes can be automatically assigned to clinical diagnoses." @default.
- W4294975803 created "2022-09-08" @default.
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- W4294975803 date "2022-08-01" @default.
- W4294975803 modified "2023-10-15" @default.
- W4294975803 title "Fine-Grained ICD Code Assignment Using Ontology-Based Classification" @default.
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- W4294975803 doi "https://doi.org/10.1109/iri54793.2022.00058" @default.
- W4294975803 hasPublicationYear "2022" @default.
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