Exploration of artificial intelligence use with ARIES in multiple myeloma research

Loda S, Krebs J, Danhof S, Schreder M, Solimando AG, Strifler S, Rasche L, Kortuem M, Kerscher A, Knop S, Puppe F, Einsele H, Bittrich M (2019)


Publication Type: Journal article

Publication year: 2019

Journal

Book Volume: 8

Article Number: 999

Journal Issue: 7

DOI: 10.3390/jcm8070999

Abstract

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 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.

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How to cite

APA:

Loda, S., Krebs, J., Danhof, S., Schreder, M., Solimando, A.G., Strifler, S.,... Bittrich, M. (2019). Exploration of artificial intelligence use with ARIES in multiple myeloma research. Journal of Clinical Medicine, 8(7). https://doi.org/10.3390/jcm8070999

MLA:

Loda, Sophia, et al. "Exploration of artificial intelligence use with ARIES in multiple myeloma research." Journal of Clinical Medicine 8.7 (2019).

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