Update README.md

README.md

@@ -66,4 +66,29 @@ dataset = load_dataset('thbndi/Mimic4Dataset', task="Mortality", mimic_path="/pa
 dataset = load_dataset('thbndi/Mimic4Dataset', task="Phenotype", mimic_path="/path/to/mimic_data", config_path="/path/to/config_file", encoding="aggreg", generate_cohort=False, val_size=0.2, cache_dir="/path/to/cache_dir")
 ```
 
-Please note that the provided examples are for illustrative purposes only, and you should adjust the paths and settings based on your actual dataset and specific use case.
+Please note that the provided examples are for illustrative purposes only, and you should adjust the paths and settings based on your actual dataset and specific use case.
+
+## Citations
+Please if you use this dataset we would appreciate citations to the following paper : 
+```raw
+@inproceedings{lovon-melgarejo-etal-2024-revisiting,
+    title = "Revisiting the {MIMIC}-{IV} Benchmark: Experiments Using Language Models for Electronic Health Records",
+    author = "Lovon-Melgarejo, Jesus  and
+      Ben-Haddi, Thouria  and
+      Di Scala, Jules  and
+      Moreno, Jose G.  and
+      Tamine, Lynda",
+    editor = "Demner-Fushman, Dina  and
+      Ananiadou, Sophia  and
+      Thompson, Paul  and
+      Ondov, Brian",
+    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.cl4health-1.23/",
+    pages = "189--196",
+    abstract = "The lack of standardized evaluation benchmarks in the medical domain for text inputs can be a barrier to widely adopting and leveraging the potential of natural language models for health-related downstream tasks. This paper revisited an openly available MIMIC-IV benchmark for electronic health records (EHRs) to address this issue. First, we integrate the MIMIC-IV data within the Hugging Face datasets library to allow an easy share and use of this collection. Second, we investigate the application of templates to convert EHR tabular data to text. Experiments using fine-tuned and zero-shot LLMs on the mortality of patients task show that fine-tuned text-based models are competitive against robust tabular classifiers. In contrast, zero-shot LLMs struggle to leverage EHR representations. This study underlines the potential of text-based approaches in the medical field and highlights areas for further improvement."
+}
+```