References

Below is a selected bibliography of works relevant to clinical Natural Language Processing, clinical text analytics and AI in healthcare.

  1. Alfano, M., Lenzitti, B., Lo Bosco, G., Muriana, C., Piazza, T., & Vizzini, G. (2020). Design, development and validation of a system for automatic help to medical text understanding. International Journal of Medical Informatics, 138, 104109.
    🔗 https://doi.org/10.1016/j.ijmedinf.2020.104109

  2. Attrey, R., & Levit, A. (2019). The promise of natural language processing in healthcare. University of Western Ontario Medical Journal, 87(2), 21–23.
    🔗 https://doi.org/10.5206/uwomj.v87i2.1152

  3. Comelli, A., Agnello, L., & Vitabile, S. (2015). An ontology-based retrieval system for mammographic reports. In 2015 IEEE Symposium on Computers and Communication (ISCC) (pp. 1001–1006).
    🔗 https://doi.org/10.1109/ISCC.2015.7405644

  4. Elbattah, M., Arnaud, É., Gignon, M., & Dequen, G. (2021). The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications. HEALTHINF.
    🔗 https://doi.org/10.5220/0010414508250832

  5. Ford, E., Curlewis, K., Squires, E., Griffiths, L. J., Stewart, R., & Jones, K. H. (2021). The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature. Frontiers in Digital Health, 3, 606599.
    🔗 https://doi.org/10.3389/fdgth.2021.606599

  6. Gao, Y., et al. (2022). A scoping review of publicly available language tasks in clinical natural language processing. Journal of the American Medical Informatics Association, 29(10), 1797–1806.
    🔗 https://doi.org/10.1093/jamia/ocac127

  7. Huang, K., Altosaar, J., & Ranganath, R. (2019). ClinicalBERT: Modeling clinical notes and predicting hospital readmission. arXiv preprint.
    🔗 https://doi.org/10.48550/arXiv.1904.05342

  8. Jensen, K., et al. (2017). Analysis of free text in electronic health records for identification of cancer patient trajectories. Scientific Reports, 7(1).
    🔗 https://doi.org/10.1038/srep46226

  9. Khattak, F. K., et al. (2019). A survey of word embeddings for clinical text. Journal of Biomedical Informatics, 100.
    🔗 https://doi.org/10.1016/j.jbi.2019.103200

  10. Leaman, R., Khare, R., & Lu, Z. (2015). Challenges in clinical natural language processing for automated disorder normalization. Journal of Biomedical Informatics, 57, 28–37.
    🔗 https://doi.org/10.1016/j.jbi.2015.07.010

  11. Liddell, K., Simon, D. A., & Lucassen, A. (2021). Patient data ownership: who owns your health? Journal of Law and the Biosciences, 8(2).
    🔗 https://doi.org/10.1093/jlb/lsab023

  12. Mujtaba, G., et al. (2019). Clinical text classification research trends: systematic literature review and open issues. Expert Systems with Applications, 116, 494–520.
    🔗 https://doi.org/10.1016/j.eswa.2018.09.034

  13. Percha, B. (2021). Modern Clinical Text Mining: A Guide and Review. Annual Review of Biomedical Data Science, 4(1), 165–187.
    🔗 https://doi.org/10.1146/annurev-biodatasci-030421-030931

  14. Rani, G. J. J., Gladis, D., & Mammen, J. (2015). Classification and Prediction of Breast Cancer Data derived Using Natural Language Processing. WCI ‘15.
    🔗 https://doi.org/10.1145/2791405.2791489

  15. Rogers, P., Wang, D., & Lu, Z. (2021). Medical Information Mart for Intensive Care: A Foundation for the Fusion of Artificial Intelligence and Real-World Data. Frontiers in Artificial Intelligence, 4, 76.
    🔗 https://doi.org/10.3389/frai.2021.691626

  16. Schrempf, P., et al. (2021). Templated text synthesis for expert-guided multi-label extraction from radiology reports. Machine Learning and Knowledge Extraction, 3(2), 299–317.
    🔗 https://doi.org/10.3390/make3020015

  17. Secinaro, S., Calandra, D., Secinaro, A., et al. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21, 125.
    🔗 https://doi.org/10.1186/s12911-021-01488-9

  18. Spasic, I., & Nenadic, G. (2020). Clinical text data in machine learning: systematic review. JMIR Medical Informatics, 8(3), e17984.
    🔗 https://doi.org/10.2196/17984

  19. Zia, A., Aziz, M., Popa, I., Khan, S. A., Hamedani, A. F., & Asif, A. R. (2022). Artificial Intelligence-Based Medical Data Mining.