Published on in Vol 7, No 2 (2019): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12596, first published .
Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Journals

  1. Rubinstein Y, Robinson P, Gahl W, Avillach P, Baynam G, Cederroth H, Goodwin R, Groft S, Hansson M, Harris N, Huser V, Mascalzoni D, McMurry J, Might M, Nellaker C, Mons B, Paltoo D, Pevsner J, Posada M, Rockett-Frase A, Roos M, Rubinstein T, Taruscio D, van Enckevort E, Haendel M. The case for open science: rare diseases. JAMIA Open 2020;3(3):472 View
  2. Bruckert S, Finzel B, Schmid U. The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions. Frontiers in Artificial Intelligence 2020;3 View
  3. Agrawal A, Qazi K. Detecting modeling inconsistencies in SNOMED CT using a machine learning technique. Methods 2020;179:111 View
  4. Robinson P, Haendel M. Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions. Yearbook of Medical Informatics 2020;29(01):159 View
  5. Semenov I, Osenev R, Gerasimov S, Kopanitsa G, Denisov D, Andreychuk Y. Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support. International Journal of Environmental Research and Public Health 2019;17(1):73 View
  6. Hu B, Bajracharya A, Yu H. Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation. JMIR Medical Informatics 2020;8(1):e14971 View
  7. Luo L, Yan S, Lai P, Veltri D, Oler A, Xirasagar S, Ghosh R, Similuk M, Robinson P, Lu Z, Wren J. PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology. Bioinformatics 2021;37(13):1884 View
  8. Köhler S, Gargano M, Matentzoglu N, Carmody L, Lewis-Smith D, Vasilevsky N, Danis D, Balagura G, Baynam G, Brower A, Callahan T, Chute C, Est J, Galer P, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris N, Hartnett M, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry J, Miller J, Munoz-Torres M, Peters R, Rapp C, Rath A, Rind S, Rosenberg A, Segal M, Seidel M, Smedley D, Talmy T, Thomas Y, Wiafe S, Xian J, Yüksel Z, Helbig I, Mungall C, Haendel M, Robinson P. The Human Phenotype Ontology in 2021. Nucleic Acids Research 2021;49(D1):D1207 View
  9. Haimel M, Pazmandi J, Heredia R, Dmytrus J, Bal S, Zoghi S, van Daele P, Briggs T, Wouters C, Bader-Meunier B, Aeschlimann F, Caorsi R, Eleftheriou D, Hoppenreijs E, Salzer E, Bakhtiar S, Derfalvi B, Saettini F, Kusters M, Elfeky R, Trück J, Rivière J, van der Burg M, Gattorno M, Seidel M, Burns S, Warnatz K, Hauck F, Brogan P, Gilmour K, Schuetz C, Simon A, Bock C, Hambleton S, de Vries E, Robinson P, van Gijn M, Boztug K. Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity. Journal of Allergy and Clinical Immunology 2022;149(1):369 View
  10. Landolsi M, Hlaoua L, Romdhane L. Hybrid method to automatically extract medical document tree structure. Engineering Applications of Artificial Intelligence 2023;120:105922 View
  11. Skreta M, Arbabi A, Wang J, Drysdale E, Kelly J, Singh D, Brudno M. Automatically disambiguating medical acronyms with ontology-aware deep learning. Nature Communications 2021;12(1) View
  12. Zhang J, Bolanos Trujillo L, Tanwar A, Ive J, Gupta V, Guo Y. Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use. BMJ Health & Care Informatics 2022;29(1):e100519 View
  13. Lenivtceva I, Kopanitsa G. The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource. Methods of Information in Medicine 2021;60(03/04):095 View
  14. Azizi S, Hier D, Wunsch II D. Enhanced neurologic concept recognition using a named entity recognition model based on transformers. Frontiers in Digital Health 2022;4 View
  15. Landolsi M, Hlaoua L, Ben Romdhane L. Information extraction from electronic medical documents: state of the art and future research directions. Knowledge and Information Systems 2023;65(2):463 View
  16. Filice R, Kahn C. Biomedical Ontologies to Guide AI Development in Radiology. Journal of Digital Imaging 2021;34(6):1331 View
  17. Tanwar A, Zhang J, Ive J, Gupta V, Guo Y. Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification. Experimental Biology and Medicine 2022;247(22):2038 View
  18. Wang J, Yang J, Zhang H, Lu H, Skreta M, Husić M, Arbabi A, Sultanum N, Brudno M. PhenoPad: Building AI enabled note-taking interfaces for patient encounters. npj Digital Medicine 2022;5(1) View
  19. Feng Y, Qi L, Tian W. PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023;20(2):1269 View
  20. Stinton G, Lieviant J, Kam S, Lim J, Goh J, Lim W, Baynam G, Tan T, Pham D, Jamuar S. Clinical free text to HPO codes. Rare 2023;1:100007 View
  21. Oommen C, Howlett-Prieto Q, Carrithers M, Hier D. Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records. Frontiers in Digital Health 2023;5 View
  22. Landolsi M, Hlaoua L, Romdhane L. Extracting and structuring information from the electronic medical text: state of the art and trendy directions. Multimedia Tools and Applications 2023;83(7):21229 View
  23. Yang J, Liu C, Deng W, Wu D, Weng C, Zhou Y, Wang K. Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT. Patterns 2024;5(1):100887 View
  24. Groza T, Wu H, Dinger M, Danis D, Hilton C, Bagley A, Davids J, Luo L, Lu Z, Robinson P, Wren J. Term-BLAST-like alignment tool for concept recognition in noisy clinical texts. Bioinformatics 2023;39(12) View
  25. Groza T, Caufield H, Gration D, Baynam G, Haendel M, Robinson P, Mungall C, Reese J. An evaluation of GPT models for phenotype concept recognition. BMC Medical Informatics and Decision Making 2024;24(1) View
  26. Groza T, Gration D, Baynam G, Robinson P, Wren J. FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology. Bioinformatics 2024;40(7) View
  27. Wu J, Dong H, Li Z, Wang H, Li R, Patra A, Dai C, Ali W, Scordis P, Wu H. A hybrid framework with large language models for rare disease phenotyping. BMC Medical Informatics and Decision Making 2024;24(1) View
  28. Soysal E, Roberts K. PheNormGPT: a framework for extraction and normalization of key medical findings. Database 2024;2024 View

Books/Policy Documents

  1. O’Leary M. Translational Bioinformatics for Therapeutic Development. View
  2. Galitsky B, Ilvovsky D. Artificial Intelligence for Healthcare Applications and Management. View
  3. Sowinski P, Wasielewska-Michniewska K, Ganzha M, Paprzycki M. Sustainable Technology and Advanced Computing in Electrical Engineering. View
  4. Galitsky B, Ilvovsky D, Goncharova E. Artificial Intelligence. View
  5. Ojha R, Deepak G. Knowledge Graphs and Semantic Web. View
  6. Tanwar A, Zhang J, Ive J, Gupta V, Guo Y. Multimodal AI in Healthcare. View