TY  - JOUR
A1  - Loda, Sophia
A1  - Krebs, Jonathan
A1  - Danhof, Sophia
A1  - Schreder, Martin
A1  - Solimando, Antonio G.
A1  - Strifler, Susanne
A1  - Rasche, Leo
A1  - Kortüm, Martin
A1  - Kerscher, Alexander
A1  - Knop, Stefan
A1  - Puppe, Frank
A1  - Einsele, Hermann
A1  - Bittrich, Max
T1  - Exploration of artificial intelligence use with ARIES in multiple myeloma research
JF  - Journal of Clinical Medicine
N2  - 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.
KW  - natural language processing
KW  - ontology
KW  - artificial intelligence
KW  - multiple myeloma
KW  - real world evidence
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197231
SN  - 2077-0383
VL  - 8
IS  - 7
ER  - 
TY  - JOUR
A1  - Kempf, Sebastian
A1  - Krug, Markus
A1  - Puppe, Frank
T1  - KIETA: Key-insight extraction from scientific tables
JF  - Applied Intelligence
N2  - An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.
KW  - table extraction
KW  - table understanding
KW  - ontology
KW  - key-insight extraction
KW  - information extraction
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324180
SN  - 0924-669X
VL  - 53
IS  - 8
ER  -