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Exploration of artificial intelligence use with ARIES in multiple myeloma research

Please always quote using this URN: urn:nbn:de:bvb:20-opus-197231
  • Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information ExtractionBackground: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.show moreshow less

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Metadaten
Author: Sophia Loda, Jonathan Krebs, Sophia Danhof, Martin Schreder, Antonio G. Solimando, Susanne Strifler, Leo Rasche, Martin Kortüm, Alexander Kerscher, Stefan Knop, Frank Puppe, Hermann Einsele, Max Bittrich
URN:urn:nbn:de:bvb:20-opus-197231
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Medizinische Fakultät / Medizinische Klinik und Poliklinik II
Language:English
Parent Title (English):Journal of Clinical Medicine
ISSN:2077-0383
Year of Completion:2019
Volume:8
Issue:7
Pagenumber:999
Source:Journal of Clinical Medicine 2019, 8(7), 999; https://doi.org/10.3390/jcm8070999
DOI:https://doi.org/10.3390/jcm8070999
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:artificial intelligence; multiple myeloma; natural language processing; ontology; real world evidence
Release Date:2020/02/28
Date of first Publication:2019/07/09
Open-Access-Publikationsfonds / Förderzeitraum 2019
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International