Exploration of artificial intelligence use with ARIES in multiple myeloma research
Zitieren Sie bitte immer diese 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.…
Autor(en): | 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 |
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URN: | urn:nbn:de:bvb:20-opus-197231 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Medizinische Fakultät / Medizinische Klinik und Poliklinik II | |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Journal of Clinical Medicine |
ISSN: | 2077-0383 |
Erscheinungsjahr: | 2019 |
Band / Jahrgang: | 8 |
Heft / Ausgabe: | 7 |
Seitenangabe: | 999 |
Originalveröffentlichung / Quelle: | Journal of Clinical Medicine 2019, 8(7), 999; https://doi.org/10.3390/jcm8070999 |
DOI: | https://doi.org/10.3390/jcm8070999 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 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 | |
Freie Schlagwort(e): | artificial intelligence; multiple myeloma; natural language processing; ontology; real world evidence |
Datum der Freischaltung: | 28.02.2020 |
Datum der Erstveröffentlichung: | 09.07.2019 |
Open-Access-Publikationsfonds / Förderzeitraum 2019 | |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |