We make use of the Cytology and Histopathology Invasive Fungal Infection Reports (CHIFIR) dataset available at PhysioNet. Please note, the dataset is available under credentialed access and requires the user to sign the PhysioNet Credentialed Health Data Use Agreement.
Our goal was to classify the reports as positive or negative for the evidence of invasive fungal infection (IFI). We combined a dictionary-based named-entity recognition approach with a simple logistic regression classifier. The methods and results are outlined in our paper "Detecting evidence of invasive fungal infections in cytology and histopathology reports enriched with concept-level annotations".
When using the CHIFIR dataset, please cite:
Rozova, V., Khanina, A., Teng, J., Teh, J., Worth, L., Slavin, M., thursky, k., & Verspoor, K. (2023). CHIFIR: Cytology and Histopathology Invasive Fungal Infection Reports (version 1.0.1). PhysioNet. https://doi.org/10.13026/ssk0-1x59.
Additionally, please cite the original publication:
Rozova V, Khanina A, Teng JC, S K Teh J, Worth LJ, Slavin MA, et al. Detecting evidence of invasive fungal infections in cytology and histopathology reports enriched with concept-level annotations. Journal of Biomedical Informatics. 2023:104293. https://doi.org/10.1016/j.jbi.2023.104293