{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T02:10:16Z","timestamp":1761790216361,"version":"3.44.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["RTG 2535"],"award-info":[{"award-number":["RTG 2535"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008349","name":"Universit\u00e4t Duisburg-Essen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008349","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This study investigates the potential of biomedical concepts\u2014genes, diseases, and chemicals\u2014in enhancing biomedical literature retrieval systems amidst the burgeoning volume of biomedical literature. We compare five traditional sparse approaches against hybrid BERT-based methods, to assess their effectiveness in leveraging biomedical concepts for improved retrieval accuracy. Our research poses critical questions on the necessity and efficiency of biomedical concepts in the era of advanced language models and evaluates if these concepts can further refine retrieval outcomes. Using datasets from Text REtrieval Conference Precision Medicine tracks (2017\u20132019), which are based on the Medline collection (30+ million biomedical publications) and NDCG@10 (Normalized Discounted Cumulative Gain) for evaluation, we demonstrate that biomedical concepts are indeed helpful for both hybrid and sparse retrieval strategies in the biomedical domain and that the performance of sparse and hybrid methods is comparable. We further demonstrate how our findings can be integrated into a live search system to support clinical practice.<\/jats:p>","DOI":"10.1007\/s41060-025-00724-z","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T13:18:01Z","timestamp":1738588681000},"page":"4409-4422","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integration of biomedical concepts for enhanced medical literature retrieval"],"prefix":"10.1007","volume":"20","author":[{"given":"Sameh","family":"Frihat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norbert","family":"Fuhr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"issue":"12","key":"724_CR1","doi-asserted-by":"publisher","first-page":"889","DOI":"10.7326\/0003-4819-149-12-200812160-00008","volume":"149","author":"MM Leeflang","year":"2008","unstructured":"Leeflang, M.M., Deeks, J.J., Gatsonis, C., Bossuyt, P.M.: Systematic reviews of diagnostic test accuracy. Ann. Intern. Med. 149(12), 889\u2013897 (2008)","journal-title":"Ann. Intern. Med."},{"issue":"1","key":"724_CR2","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.cell.2019.02.039","volume":"177","author":"NS Abul-Husn","year":"2019","unstructured":"Abul-Husn, N.S., Kenny, E.E.: Personalized medicine and the power of electronic health records. Cell 177(1), 58\u201369 (2019)","journal-title":"Cell"},{"issue":"suppl\u20132","key":"724_CR3","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1093\/hmg\/ddl183","volume":"15","author":"EJ Topol","year":"2006","unstructured":"Topol, E.J., Smith, J., Plow, E.F., Wang, Q.K.: Genetic susceptibility to myocardial infarction and coronary artery disease. Human Mol. Genet. 15(suppl\u20132), 117\u2013123 (2006)","journal-title":"Human Mol. Genet."},{"issue":"W1","key":"724_CR4","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1093\/nar\/gkz389","volume":"47","author":"C-H Wei","year":"2019","unstructured":"Wei, C.-H., Allot, A., Leaman, R., Lu, Z.: Pubtator central: automated concept annotation for biomedical full text articles. Nucleic Acids Res. 47(W1), 587\u2013593 (2019)","journal-title":"Nucleic Acids Res."},{"issue":"11","key":"724_CR5","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S1359-6446(02)02286-9","volume":"7","author":"R Mack","year":"2002","unstructured":"Mack, R., Hehenberger, M.: Text-based knowledge discovery: search and mining of life-sciences documents. Drug Discovery Today 7(11), 89\u201398 (2002)","journal-title":"Drug Discovery Today"},{"issue":"2","key":"724_CR6","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.jbi.2012.10.007","volume":"46","author":"F Zhu","year":"2013","unstructured":"Zhu, F., Patumcharoenpol, P., Zhang, C., Yang, Y., Chan, J., Meechai, A., Vongsangnak, W., Shen, B.: Biomedical text mining and its applications in cancer research. J. Biomed. Inform. 46(2), 200\u2013211 (2013)","journal-title":"J. Biomed. Inform."},{"issue":"3","key":"724_CR7","doi-asserted-by":"publisher","first-page":"057","DOI":"10.1093\/bib\/bbaa057","volume":"22","author":"S Zhao","year":"2021","unstructured":"Zhao, S., Su, C., Lu, Z., Wang, F.: Recent advances in biomedical literature mining. Brief. Bioinform. 22(3), 057 (2021)","journal-title":"Brief. Bioinform."},{"key":"724_CR8","doi-asserted-by":"crossref","unstructured":"Sankhavara, J.: Biomedical document retrieval for clinical decision support system. In: Proceedings of ACL 2018, Student Research Workshop, pp. 84\u201390 (2018)","DOI":"10.18653\/v1\/P18-3012"},{"key":"724_CR9","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s12859-019-2607-x","volume":"19","author":"C Simon","year":"2019","unstructured":"Simon, C., Davidsen, K., Hansen, C., Seymour, E., Barnkob, M.B., Olsen, L.R.: Bioreader: a text mining tool for performing classification of biomedical literature. BMC Bioinform. 19, 165\u2013170 (2019)","journal-title":"BMC Bioinform."},{"key":"724_CR10","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.cmpb.2018.03.012","volume":"159","author":"T Alves","year":"2018","unstructured":"Alves, T., Rodrigues, R., Costa, H., Rocha, M.: Development of an information retrieval tool for biomedical patents. Comput. Methods Programs Biomed. 159, 125\u2013134 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"724_CR11","doi-asserted-by":"crossref","unstructured":"Mohammadi, M., Roshandel, G., Ghazimirsaeid, S.J., Zarinbal, M., Beheshti, M.H., Sheikhshoaei, F.: Scientometric study of research in information retrieval in medical sciences. Med. J. Islamic Republic Iran 36 (2022)","DOI":"10.47176\/mjiri.36.65"},{"key":"724_CR12","unstructured":"Dervakos, E., Filandrianos, G., Thomas, K., Mandalios, A., Zerva, C., Stamou, G.: Semantic enrichment of pretrained embedding output for unsupervised IR. In: AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, vol. 2846 (2021)"},{"key":"724_CR13","doi-asserted-by":"crossref","unstructured":"Moskovitch, R., Wang, F., Pei, J., Friedman, C.: JASIST special issue on biomedical information retrieval. Wiley Online Library (2017)","DOI":"10.1002\/asi.23972"},{"issue":"10","key":"724_CR14","doi-asserted-by":"publisher","first-page":"3804","DOI":"10.1109\/JBHI.2021.3099755","volume":"25","author":"SA Moqurrab","year":"2021","unstructured":"Moqurrab, S.A., Ayub, U., Anjum, A., Asghar, S., Srivastava, G.: An accurate deep learning model for clinical entity recognition from clinical notes. IEEE J. Biomed. Health Inform. 25(10), 3804\u20133811 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"724_CR15","doi-asserted-by":"publisher","first-page":"2880","DOI":"10.1007\/s11606-020-06110-8","volume":"36","author":"AC Flint","year":"2021","unstructured":"Flint, A.C., Melles, R.B., Klingman, J.G., Chan, S.L., Rao, V.A., Avins, A.L.: Automated extraction of structured data from text notes in the electronic medical record. J. Gen. Internal Med. 36, 2880\u20132882 (2021)","journal-title":"J. Gen. Internal Med."},{"key":"724_CR16","doi-asserted-by":"crossref","unstructured":"Hussain, M., Choi, D.-J., Lee, S.: Semantic based clinical notes mining for factual information extraction. In: 2020 International Conference on Information Networking (ICOIN), pp. 46\u201348. IEEE (2020)","DOI":"10.1109\/ICOIN48656.2020.9016559"},{"key":"724_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103276","volume":"98","author":"E Yehia","year":"2019","unstructured":"Yehia, E., Boshnak, H., AbdelGaber, S., Abdo, A., Elzanfaly, D.S.: Ontology-based clinical information extraction from physician\u2019s free-text notes. J. Biomed. Inform. 98, 103276 (2019)","journal-title":"J. Biomed. Inform."},{"issue":"4","key":"724_CR18","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1561\/1500000019","volume":"3","author":"S Robertson","year":"2009","unstructured":"Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends\u00ae Inf. Retrieval 3(4), 333\u2013389 (2009)","journal-title":"Found. Trends\u00ae Inf. Retrieval"},{"issue":"3","key":"724_CR19","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1561\/1500000016","volume":"3","author":"T-Y Liu","year":"2009","unstructured":"Liu, T.-Y.: Learning to rank for information retrieval. Found. Trends\u00ae Inform. Retrieval 3(3), 225\u2013331 (2009)","journal-title":"Found. Trends\u00ae Inform. Retrieval"},{"issue":"7","key":"724_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3462476","volume":"54","author":"L Tamine","year":"2021","unstructured":"Tamine, L., Goeuriot, L.: Semantic information retrieval on medical texts: research challenges, survey, and open issues. ACM Comput. Surv. (CSUR) 54(7), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"724_CR21","unstructured":"Gutierrez, B.J., McNeal, N., Washington, C., Chen, Y., Li, L., Sun, H., Su, Y.: Thinking about gpt-3 in-context learning for biomedical IE? think again. arXiv preprint arXiv:2203.08410 (2022)"},{"key":"724_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, R., Tu, X., Huang, J.X.: Utilizing BERT for biomedical and clinical text mining. In: Data Analytics in Biomedical Engineering and Healthcare, pp. 73\u2013103. Elsevier, New York (2021)","DOI":"10.1016\/B978-0-12-819314-3.00005-7"},{"key":"724_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103530","volume":"109","author":"M Rybinski","year":"2020","unstructured":"Rybinski, M., Xu, J., Karimi, S.: Clinical trial search: using biomedical language understanding models for re-ranking. J. Biomed. Inform. 109, 103530 (2020)","journal-title":"J. Biomed. Inform."},{"key":"724_CR24","doi-asserted-by":"crossref","unstructured":"Fan, L., Li, L., Ma, Z., Lee, S., Yu, H., Hemphill, L.: A bibliometric review of large language models research from 2017 to 2023. arXiv preprint arXiv:2304.02020 (2023)","DOI":"10.1145\/3664930"},{"key":"724_CR25","doi-asserted-by":"crossref","unstructured":"Abu-Jeyyab, M., Alrosan, S., Alkhawaldeh, I.: Harnessing large language models in medical research and scientific writing: A closer look to the future: LIMS in medical research and scientific writing. High Yield Med. Rev. 1(2) (2023)","DOI":"10.59707\/hymrFBYA5348"},{"key":"724_CR26","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1145\/1394251.1394262","volume":"42","author":"O Alonso","year":"2008","unstructured":"Alonso, O., Zaragoza, H.: Exploiting semantic annotations in information retrieval: Esair \u201908. SIGIR Forum 42, 55\u201358 (2008). https:\/\/doi.org\/10.1145\/1394251.1394262","journal-title":"SIGIR Forum"},{"issue":"1","key":"724_CR27","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2964797.2964806","volume":"50","author":"K Balog","year":"2016","unstructured":"Balog, K., Dalton, J., Doucet, A., Ibrahim, Y.: Report on the eighth workshop on exploiting semantic annotations in information retrieval (ESAIR \u201915). SIGIR Forum 50(1), 49\u201357 (2016). https:\/\/doi.org\/10.1145\/2964797.2964806","journal-title":"SIGIR Forum"},{"key":"724_CR28","unstructured":"Zhang, L., F\u00e4rber, M., Tran, T., Rettinger, A.: Exploiting semantic annotations for entity-based information retrieval. In: Horridge, M., Rospocher, M., Ossenbruggen, J. (eds.) Proceedings of the ISWC 2014 Posters & Demonstrations Track a Track Within the 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, October 21, 2014. CEUR Workshop Proceedings, vol. 1272, pp. 429\u2013432. CEUR-WS.org, Dhupguri (2014). https:\/\/ceur-ws.org\/Vol-1272\/paper_134.pdf"},{"key":"724_CR29","doi-asserted-by":"publisher","unstructured":"Garc\u00eda, N.F., Toro, J.M.B., Fern\u00e1ndez, L.S., Centeno, V.L.: Exploiting Wikipedia in integrating semantic annotation with information retrieval. In: Last, M., Szczepaniak, P.S., Volkovich, Z., Kandel, A. (eds.) Advances in Web Intelligence and Data Mining. Studies in Computational Intelligence, vol. 23, pp. 61\u201370. Springer, Berlin (2006). https:\/\/doi.org\/10.1007\/3-540-33880-2_7","DOI":"10.1007\/3-540-33880-2_7"},{"key":"724_CR30","doi-asserted-by":"crossref","unstructured":"Louren\u00e7o, A., Carneiro, S., Ferreira, E.C., Carreira, R., Rocha, L.M., Glez-Pe\u00f1a, D., M\u00e9ndez, J.R., Fdez-Riverola, F., Diaz, F., Rocha, I., : Biomedical text mining applied to document retrieval and semantic indexing. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II 10, pp. 954\u2013963 (2009). Springer","DOI":"10.1007\/978-3-642-02481-8_146"},{"key":"724_CR31","doi-asserted-by":"crossref","unstructured":"Wei, C.-H., Kao, H.-Y., Lu, Z.: Text mining tools for assisting literature curation. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 590\u2013591 (2014)","DOI":"10.1145\/2649387.2660786"},{"key":"724_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-015-0472-9","volume":"16","author":"\u00c0 Bravo","year":"2015","unstructured":"Bravo, \u00c0., Pi\u00f1ero, J., Queralt-Rosinach, N., Rautschka, M., Furlong, L.I.: Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinform. 16, 1\u201317 (2015)","journal-title":"BMC Bioinform."},{"issue":"10","key":"724_CR33","doi-asserted-by":"publisher","first-page":"0164680","DOI":"10.1371\/journal.pone.0164680","volume":"11","author":"S Lee","year":"2016","unstructured":"Lee, S., Kim, D., Lee, K., Choi, J., Kim, S., Jeon, M., Lim, S., Choi, D., Kim, S., Tan, A.-C.: Best: next-generation biomedical entity search tool for knowledge discovery from biomedical literature. PLoS ONE 11(10), 0164680 (2016)","journal-title":"PLoS ONE"},{"issue":"W1","key":"724_CR34","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1093\/nar\/gkac310","volume":"50","author":"P-H Li","year":"2022","unstructured":"Li, P.-H., Chen, T.-F., Yu, J.-Y., Shih, S.-H., Su, C.-H., Lin, Y.-H., Tsai, H.-K., Juan, H.-F., Chen, C.-Y., Huang, J.-H.: pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature. Nucleic Acids Res. 50(W1), 616\u2013622 (2022)","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"724_CR35","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1093\/bib\/bbaa142","volume":"22","author":"K Lee","year":"2021","unstructured":"Lee, K., Wei, C.-H., Lu, Z.: Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature. Brief. Bioinform. 22(3), 142 (2021)","journal-title":"Brief. Bioinform."},{"issue":"1","key":"724_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3470562","volume":"40","author":"A Mustar","year":"2021","unstructured":"Mustar, A., Lamprier, S., Piwowarski, B.: On the study of transformers for query suggestion. ACM Trans. Inf. Syst. (TOIS) 40(1), 1\u201327 (2021)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"724_CR37","doi-asserted-by":"crossref","unstructured":"Lin, S.-C., Yang, J.-H., Lin, J.: Contextualized query embeddings for conversational search. arXiv preprint arXiv:2104.08707 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.77"},{"issue":"18","key":"724_CR38","doi-asserted-by":"publisher","first-page":"55305","DOI":"10.1007\/S11042-023-17783-8","volume":"83","author":"S Gupta","year":"2024","unstructured":"Gupta, S., Sharaff, A., Nagwani, N.K.: Query based biomedical document retrieval for clinical information access with the semantic similarity. Multim. Tools Appl. 83(18), 55305\u201355317 (2024). https:\/\/doi.org\/10.1007\/S11042-023-17783-8","journal-title":"Multim. Tools Appl."},{"key":"724_CR39","doi-asserted-by":"crossref","unstructured":"Roberts, K., Demner-Fushman, D., Voorhees, E.M., Hersh, W.R., Bedrick, S., Lazar, A.J., Pant, S.: Overview of the TREC 2017 precision medicine track. In: The 28th Text REtrieval Conference: TREC. Text REtrieval Conference, vol. 26 (2017). NIH Public Access","DOI":"10.6028\/NIST.SP.500-324.pm-overview"},{"key":"724_CR40","doi-asserted-by":"crossref","unstructured":"Rivas, A., Borrajo, L., Iglesias, E.L., Romero, R.: Applying lemur query expansion techniques in biomedical information retrieval. In: Distributed Computing and Artificial Intelligence: 9th International Conference, pp. 403\u2013410. Springer, Berlin (2012)","DOI":"10.1007\/978-3-642-28765-7_48"},{"issue":"6","key":"724_CR41","doi-asserted-by":"publisher","first-page":"28272","DOI":"10.2196\/28272","volume":"9","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Feng, J., Yang, Z., Wang, L.: Document retrieval for precision medicine using a deep learning ensemble method. JMIR Med. Inform. 9(6), 28272 (2021)","journal-title":"JMIR Med. Inform."},{"issue":"5","key":"724_CR42","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jbi.2010.04.005","volume":"43","author":"S Yoo","year":"2010","unstructured":"Yoo, S., Choi, J.: On the query reformulation technique for effective medline document retrieval. J. Biomed. Inform. 43(5), 686\u2013693 (2010)","journal-title":"J. Biomed. Inform."},{"key":"724_CR43","doi-asserted-by":"crossref","unstructured":"Dai, H.-J., Wei, C.-H., Kao, H.-Y., Liu, R.-L., Tsai, R.T.-H., Lu, Z., et al.: Text mining for translational bioinformatics. Hindawi (2015)","DOI":"10.1155\/2015\/368264"},{"issue":"12","key":"724_CR44","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1038\/nrg3337","volume":"13","author":"D Rebholz-Schuhmann","year":"2012","unstructured":"Rebholz-Schuhmann, D., Oellrich, A., Hoehndorf, R.: Text-mining solutions for biomedical research: enabling integrative biology. Nat. Rev. Genet. 13(12), 829\u2013839 (2012)","journal-title":"Nat. Rev. Genet."},{"key":"724_CR45","doi-asserted-by":"crossref","unstructured":"Arabzadeh, N., Yan, X., Clarke, C.L.: Predicting efficiency\/effectiveness trade-offs for dense vs. sparse retrieval strategy selection. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2862\u20132866 (2021)","DOI":"10.1145\/3459637.3482159"},{"key":"724_CR46","doi-asserted-by":"crossref","unstructured":"Nguyen, T., MacAvaney, S., Yates, A.: A unified framework for learned sparse retrieval. In: European Conference on Information Retrieval, pp. 101\u2013116. Springer, Berlin (2023)","DOI":"10.1007\/978-3-031-28241-6_7"},{"issue":"10","key":"724_CR47","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1093\/bioinformatics\/bty871","volume":"35","author":"AJ Soto","year":"2019","unstructured":"Soto, A.J., Przyby\u0142a, P., Ananiadou, S.: Thalia: Semantic search engine for biomedical abstracts. Bioinformatics 35(10), 1799\u20131801 (2019)","journal-title":"Bioinformatics"},{"key":"724_CR48","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, Z., Xiong, C., Liu, Z.: More robust dense retrieval with contrastive dual learning. In: Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 287\u2013296 (2021)","DOI":"10.1145\/3471158.3472245"},{"issue":"4","key":"724_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3486250","volume":"40","author":"J Guo","year":"2022","unstructured":"Guo, J., Cai, Y., Fan, Y., Sun, F., Zhang, R., Cheng, X.: Semantic models for the first-stage retrieval: a comprehensive review. ACM Trans. Inf. Syst. (TOIS) 40(4), 1\u201342 (2022)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"724_CR50","unstructured":"Zhang, Y., Long, D., Xu, G., Xie, P.: HLATR: enhance multi-stage text retrieval with hybrid list aware transformer reranking. arXiv preprint arXiv:2205.10569 (2022)"},{"key":"724_CR51","doi-asserted-by":"crossref","unstructured":"Lin, J., Ma, X., Lin, S.-C., Yang, J.-H., Pradeep, R., Nogueira, R.: Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2356\u20132362 (2021)","DOI":"10.1145\/3404835.3463238"},{"key":"724_CR52","doi-asserted-by":"crossref","unstructured":"Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., Poon, H.: Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing (2020)","DOI":"10.1145\/3458754"},{"issue":"4","key":"724_CR53","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"724_CR54","doi-asserted-by":"crossref","unstructured":"Guo, T., Guo, J., Fan, Y., Lan, Y., Xu, J., Cheng, X.: A comparison between term-based and embedding-based methods for initial retrieval. In: Information Retrieval: 24th China Conference, CCIR 2018, Guilin, China, September 27\u201329, 2018, Proceedings 24, pp. 28\u201340 (2018). Springer, Berlin","DOI":"10.1007\/978-3-030-01012-6_3"},{"key":"724_CR55","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"724_CR56","doi-asserted-by":"crossref","unstructured":"Roberts, K., Demner-Fushman, D., Voorhees, E.M., Hersh, W.R., Bedrick, S., Lazar, A.J.: Overview of the TREC 2018 precision medicine track. In: The...Text REtrieval Conference: TREC. Text REtrieval Conference (2018). NIH Public Access","DOI":"10.6028\/NIST.SP.500-331.pm-overview"},{"key":"724_CR57","doi-asserted-by":"crossref","unstructured":"Roberts, K., Demner-Fushman, D., Voorhees, E.M., Hersh, W.R., Bedrick, S., Lazar, A.J., Pant, S., Meric-Bernstam, F.: Overview of the TREC 2019 precision medicine track. In: The...Text REtrieval Conference: TREC. Text REtrieval Conference, vol. 1250 (2019). NIH Public Access","DOI":"10.6028\/NIST.SP.1250.pm-overview"},{"key":"724_CR58","unstructured":"Medicine, N.I.H.: MEDLINE Overview. [Online; Accessed 30 March 2024] (2024)"},{"key":"724_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103526","volume":"109","author":"S Fu","year":"2020","unstructured":"Fu, S., Chen, D., He, H., Liu, S., Moon, S., Peterson, K.J., Shen, F., Wang, L., Wang, Y., Wen, A.: Clinical concept extraction: a methodology review. J. Biomed. Inform. 109, 103526 (2020)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"724_CR60","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.jbi.2010.10.002","volume":"44","author":"M Bada","year":"2011","unstructured":"Bada, M., Hunter, L.: Desiderata for ontologies to be used in semantic annotation of biomedical documents. J. Biomed. Inform. 44(1), 94\u2013101 (2011)","journal-title":"J. Biomed. Inform."},{"key":"724_CR61","doi-asserted-by":"publisher","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: IR evaluation methods for retrieving highly relevant documents. In: Yannakoudakis, E.J., Belkin, N.J., Ingwersen, P., Leong, M. (eds.) SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 24\u201328, 2000, Athens, Greece, pp. 41\u201348. ACM, New York (2000). https:\/\/doi.org\/10.1145\/345508.345545","DOI":"10.1145\/345508.345545"},{"key":"724_CR62","doi-asserted-by":"publisher","first-page":"136182","DOI":"10.1109\/ACCESS.2021.3116857","volume":"9","author":"M Ferrante","year":"2021","unstructured":"Ferrante, M., Ferro, N., Fuhr, N.: Towards meaningful statements in IR evaluation: mapping evaluation measures to interval scales. IEEE Access 9, 136182\u2013136216 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3116857","journal-title":"IEEE Access"},{"issue":"422","key":"724_CR63","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","volume":"88","author":"J Shao","year":"1993","unstructured":"Shao, J.: Linear model selection by cross-validation. J. Am. Stat. Assoc. 88(422), 486\u2013494 (1993)","journal-title":"J. Am. Stat. Assoc."},{"issue":"3","key":"724_CR64","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1145\/3190580.3190586","volume":"51","author":"N Fuhr","year":"2017","unstructured":"Fuhr, N.: Some common mistakes in IR evaluation, and how they can be avoided. SIGIR Forum 51(3), 32\u201341 (2017)","journal-title":"SIGIR Forum"},{"issue":"2","key":"724_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605943","volume":"56","author":"B Min","year":"2023","unstructured":"Min, B., Ross, H., Sulem, E., Veyseh, A.P.B., Nguyen, T.H., Sainz, O., Agirre, E., Heintz, I., Roth, D.: Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput. Surv. 56(2), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"724_CR66","doi-asserted-by":"crossref","unstructured":"Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al.: A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. (2023)","DOI":"10.1145\/3641289"},{"issue":"3","key":"724_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102516","volume":"58","author":"AM Shah","year":"2021","unstructured":"Shah, A.M., Yan, X., Tariq, S., Ali, M.: What patients like or dislike in physicians: analyzing drivers of patient satisfaction and dissatisfaction using a digital topic modeling approach. Inf. Process. Manag. 58(3), 102516 (2021). https:\/\/doi.org\/10.1016\/j.ipm.2021.102516","journal-title":"Inf. Process. Manag."},{"key":"724_CR68","doi-asserted-by":"publisher","first-page":"9355","DOI":"10.3390\/s23239355","volume":"23","author":"PN Ahmad","year":"2023","unstructured":"Ahmad, P.N., Liu, Y., Khan, K., Jiang, T., Burhan, U.: BIR: biomedical information retrieval system for cancer treatment in electronic health record using transformers. Sensors 23, 9355 (2023). https:\/\/doi.org\/10.3390\/s23239355","journal-title":"Sensors"},{"key":"724_CR69","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.3390\/healthcare11091268","volume":"11","author":"PN Ahmad","year":"2023","unstructured":"Ahmad, P.N., Shah, A., Lee, K.: A review on electronic health record text-mining for biomedical name entity recognition in healthcare domain. Healthcare 11, 1268 (2023). https:\/\/doi.org\/10.3390\/healthcare11091268","journal-title":"Healthcare"},{"key":"724_CR70","doi-asserted-by":"crossref","unstructured":"Frihat, S., Papenmeier, A., Fuhr, N.: Enhancing biomedical literature retrieval with level of evidence and bio-concepts: A comparative user study. In: 2024 ACM IEEE Joint Conference on Digital Libraries (JCDL) (forthcoming) (2024)","DOI":"10.1145\/3677389.3702558"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00724-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-025-00724-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00724-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:49:06Z","timestamp":1758797346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-025-00724-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,3]]},"references-count":70,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["724"],"URL":"https:\/\/doi.org\/10.1007\/s41060-025-00724-z","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2025,2,3]]},"assertion":[{"value":"26 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}