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An annotated dataset for extracting gene-melanoma relations from scientific literature
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In: J Biomed Semantics (2022)
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Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis LOUHI@EACL 2021
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In: EMNLP 2021 ; https://hal.archives-ouvertes.fr/hal-03483374 ; EMNLP 2021, 2021 ; https://aclanthology.org/2021.louhi-1.0/ (2021)
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Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. ...
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Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish
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In: Shaitarova, Anastassia; Rinaldi, Fabio (2021). Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, Online, 6 June 2021 - 11 June 2021, ACL Anthology. (2021)
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Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, LOUHI@EMNLP 2020, Online, November 20, 2020.
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In: https://halshs.archives-ouvertes.fr/halshs-03102506 ; Association for Computational Linguistics 2020. 2020, 978-1-952148-81-1 ; https://www.aclweb.org/anthology/volumes/2020.louhi-1/ (2020)
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COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media ...
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SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces ...
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Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
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In: https://hal.archives-ouvertes.fr/hal-02992403 ; Association for Computational Linguistics. Nov 2019, Hong Kong, 2019, 978-1-950737-77-2 ; https://www.aclweb.org/anthology/D19-6200/ (2019)
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Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019) ...
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UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition ...
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Approaching SMM4H with Merged Models and Multi-task Learning
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In: Ellendorff, Tilia; Furrer, Lenz; Colic, Nicola; Aepli, Noëmi; Rinaldi, Fabio (2019). Approaching SMM4H with Merged Models and Multi-task Learning. In: Proceedings of the 4th Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, Florence, Italy, 2 August 2019 - 2 August 2019, 58-61. (2019)
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Revisiting the decay of scientific email addresses
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In: Rodriguez-Esteban, Raul; Vishnyakova, Dina; Rinaldi, Fabio (2019). Revisiting the decay of scientific email addresses. bioRxiv 633255, University of Zurich. (2019)
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Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
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In: Sheikhalishahi, Seyedmostafa; Miotto, Riccardo; Dudley, Joel T; Lavelli, Alberto; Rinaldi, Fabio; Osmani, Venet (2019). Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Medical Informatics, 7(2):e12239. (2019)
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Abstract:
Background: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective: The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using ``clinical notes,'' ``natural language processing,'' and ``chronic disease'' and their variations as keywords to maximize coverage of the articles. Results: Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Conclusions: Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
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Keyword:
000 Computer science; 410 Linguistics; cancer; chronic diseases; clinical notes; deep learning; diabetes; Digital Society Initiative; electronic health records; heart disease; Institute of Computational Linguistics; knowledge & systems; lung disease; machine learning; natural language processing; stroke
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URL: https://doi.org/10.2196/12239
https://doi.org/10.5167/uzh-178831
https://www.zora.uzh.ch/id/eprint/178831/1/document.pdf
https://www.zora.uzh.ch/id/eprint/178831/
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Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
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In: Holderness, Eben; Yepes, Antonio Jimeno; Lavelli, Alberto; Minard, Anne-Lyse; Pustejovsky, James; Rinaldi, Fabio (2019). Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019). In: Association for Computational Linguistics, Hong Kong, November, Hong Kong, 2019 - 2019. (2019)
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Improving spaCy dependency annotation and PoS tagging web service using independent NER services
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In: Colic, Nicola; Rinaldi, Fabio (2019). Improving spaCy dependency annotation and PoS tagging web service using independent NER services. Genomics & Informatics, 17(2):e21. (2019)
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UZH@SMM4H: System Descriptions
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In: Ellendorff, Tilia; Cornelius, Joseph; Gordon, Heath; Colic, Nicola; Rinaldi, Fabio (2018). UZH@SMM4H: System Descriptions. In: SMM4H: The 3rd Social Media Mining for Health Applications Workshop and Shared Task, Brussels, 30 November 2018 - 30 November 2018, 56-60. (2018)
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Learning Representations for Biomedical Named Entity Recognition
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In: Lauriola, Ivano; Sella, Riccardo; Aiolli, Fabio; Lavelli, Alberto; Rinaldi, Fabio (2018). Learning Representations for Biomedical Named Entity Recognition. In: 2nd workshop on Natural Language for Artificial Intelligence, Trento, 22 November 2018 - 23 November 2018. s.n., 83-94. (2018)
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Learning Preferences for Large Scale Multi-label Problems
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In: Lauriola, Ivano; Polato, Mirko; Lavelli, Alberto; Rinaldi, Fabio; Aiolli, Fabio (2018). Learning Preferences for Large Scale Multi-label Problems. In: International Conference on Artificial Neural Networks, Rhodes, Greece, 4 October 2018 - 7 October 2018. Springer, 546-555. (2018)
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OGER: OntoGene’s Entity Recogniser in the BeCalm TIPS Task
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In: Furrer, Lenz; Rinaldi, Fabio (2017). OGER: OntoGene’s Entity Recogniser in the BeCalm TIPS Task. In: BioCreative V.5 Challenge Evaluation Workshop, Barcelona, Spain, 26 April 2017 - 27 April 2017, 175-182. (2017)
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