ArticleJournal of clinical medicine2019
Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A domain-specific annotation framework and expert-annotated corpus for structured analysis of German HNSCC pathology reports.BMC research notes · 2026Article
- Review
- Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models.BMC medical informatics and decision making · 2026Article
- On the use of natural language processing to implement the target trial framework using unstructured data from the electronic health record.Global epidemiology · 2025Article
- Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.JCO clinical cancer informatics · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Does medication-related osteonecrosis of the jaw affect survival of patients with Multiple Myeloma?: Exploring a large single center database using artificial intelligence.Clinical and experimental medicine · 2023Article
- The Urgent Need for Precision Medicine in Cancer and Its Microenvironment: The Paradigmatic Case of Multiple Myeloma.Journal of clinical medicine · 2022Article
- The Route of the Malignant Plasma Cell in Its Survival Niche: Exploring "Multiple Myelomas".Cancers · 2022Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNatural 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.
methodsOur 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.
resultsOur system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98.
conclusionsOur 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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