What question did this study set out to answer?

Develop and evaluate ontological attention ensemble models to accurately predict standardized ICD diagnostic and procedural codes from unstructured clinical documentation.

January 1, 2019Open Access

Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text

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Develop and evaluate ontological attention ensemble models to accurately predict standardized ICD diagnostic and procedural codes from unstructured clinical documentation.
Designed deep learning architectures that integrate domain-specific medical ontologies into attention mechanisms to process unstructured clinical text.
Trained and evaluated ensemble models to capture complex hierarchical relationships and semantic concepts across multi-label ICD coding targets.
Ontological attention networks effectively identified context-relevant clinical concepts corresponding to target diagnostic categories.
Ensemble modeling over ontological attention representations improved classification performance over standard baseline models for automated ICD coding.

Implication

Machine learning study demonstrates enhanced ICD code prediction from unstructured clinical text, highlighting the utility of ontological attention ensembles.

Abstract

Matus Falis, Maciej Pajak, Aneta Lisowska, Patrick Schrempf, Lucas Deckers, Shadia Mikhael, Sotirios Tsaftaris, Alison O’Neil. Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019). 2019.

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Cite This Study

Falis et al. (2019) studied this question.

synapsesocial.com/papers/6a11dd9f8095bbd48eb4da0b https://doi.org/10.18653/v1/d19-6220

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Implication

Machine learning study demonstrates enhanced ICD code prediction from unstructured clinical text, highlighting the utility of ontological attention ensembles.

Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text

Key Points

Implication

Abstract

Discussion

Cite This Study

Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text

Key Points

Implication

Abstract

Discussion

Cite This Study

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