OntoTox is an ontology designed to represent chemotherapy toxicities, its attributes and provenance. OntoTox integrates toxicities and grading information extracted from three heterogeneous sources:
- EHR questionnaires
- semi-structured tables
- free text
In this repository, you will find examples of OntoTox instanciations in the "owl_files" directory. These instanciations has been generated with false patient data, located in the "data" directory.
PROV-O ontology [1] has been instantiated to encode provenance. OntoTox has been implemented in owl using Owlready2 module [2]. Data has been processed using QuickUMLS [3], Stanza [4] and PyMedExt annotators [5].
[1] LEBO, Timothy, SAHOO, Satya, MCGUINNESS, Deborah, et al. Prov-o: The prov ontology. 2013. https://www.w3.org/TR/prov-o/
[2] LAMY, Jean-Baptiste. Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artificial intelligence in medicine, 2017, vol. 80, p. 11-28. https://owlready2.readthedocs.io/en/v0.31/
[3] SOLDAINI, Luca et GOHARIAN, Nazli. Quickumls: a fast, unsupervised approach for medical concept extraction. In : MedIR workshop, sigir. 2016. p. 1-4. https://github.com/Georgetown-IR-Lab/QuickUMLS
[4] QI, Peng, ZHANG, Yuhao, ZHANG, Yuhui, et al. Stanza: A Python natural language processing toolkit for many human languages. arXiv preprint arXiv:2003.07082, 2020. https://github.com/stanfordnlp/stanza/tree/166e80e00254b1bf97961dc3462d043e0a0590c3
[5] DIGAN, William, NEURAZ, Antoine, et al. PyMedExt, un couteau suisse pour le traitement des textes médicaux. https://github.com/equipe22/pymedext_core