{"doi":"10.3233/shti231032","title":"Rule-Based Natural Language Processing Pipeline to Detect Medication-Related Named Entities: Insights for Transfer Learning","abstract":"<jats:p>We document the procedure and performance of a rule-based NLP system that, using transfer learning, automatically extracts essential named entities related to drug errors from Japanese free-text incident reports. Subsequently, we used the rule-based annotated data to fine-tune a pre-trained BERT model and examined the performance of medication-related incident report prediction. The rule-based pipeline achieved a macro-F1-score of 0.81 in an internal dataset and the BERT model fine-tuned with rule-annotated data achieved a macro-F1-score of 0.97 and 0.75 for named entity recognition and relation extraction tasks, respectively. The model can be deployed to other, similar problems in medication-related clinical texts.</jats:p>","journal":"Studies in Health Technology and Informatics","year":2024,"id":602828,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1546222,"name":"Neil Waters","orcid":null,"position":2,"is_corresponding":false},{"id":1546223,"name":"Nicholas I-Hsien Kuo","orcid":null,"position":3,"is_corresponding":false},{"id":1486625,"name":"Jiaxing Liu","orcid":"0009-0002-0832-4985","position":4,"is_corresponding":false},{"id":1546221,"name":"Zoie S.Y. Wong","orcid":"0000-0003-4499-9779","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Rule-Based Natural Language Processing Pipeline to Detect Medication-Related Named Entities: Insights for Transfer Learning","abstract":"<jats:p>We document the procedure and performance of a rule-based NLP system that, using transfer learning, automatically extracts essential named entities related to drug errors from Japanese free-text incident reports. Subsequently, we used the rule-based annotated data to fine-tune a pre-trained BERT model and examined the performance of medication-related incident report prediction. The rule-based pipeline achieved a macro-F1-score of 0.81 in an internal dataset and the BERT model fine-tuned with rule-annotated data achieved a macro-F1-score of 0.97 and 0.75 for named entity recognition and relation extraction tasks, respectively. The model can be deployed to other, similar problems in medication-related clinical texts.</jats:p>","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38269876","pmcid":null,"openalex_id":"https://openalex.org/W4391229640","authors":[],"funders":[],"total_grants":0,"fwci":2.7012,"citation_percentile":0.90346774,"influential_citations":0,"citation_trend":[{"year":2025,"count":3}],"oa_status":"hybrid","license":"cc-by-nc","oa_locations":[{"url":"https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI231032","host_type":"book series"},{"url":"https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI231032","host_type":"publisher"},{"url":"https://doi.org/10.3233/shti231032","host_type":"book series"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38269876","host_type":"repository"}],"fields_of_study":["Topic Modeling","Biomedical Text Mining and Ontologies","Natural Language Processing Techniques"],"mesh_terms":["Machine Learning","Humans","Learning","Medication Errors","Natural Language Processing","Recognition, Psychology"],"keywords":["Pipeline (software)","Computer science","Natural language processing","Artificial intelligence","Named-entity recognition","Macro","F1 score","Transfer of learning","Relationship extraction","Information extraction","Patient Safety","Medication Errors","Incident Reports","Multi-task Learning","Transfer Learning","Bert","Rule-based Nlp"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T20:25:27.638562Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}