Updated README.md with essential info

README.md

@@ -124,3 +124,73 @@ configs:
   - split: test
     path: source/test-*
 ---
+
+# HINT: Hierarchical interaction network for clinical-trial-outcome predictions
+
+## Dataset Description
+
+- **Homepage:** [Papers with Code](https://paperswithcode.com/paper/hint-hierarchical-interaction-network-for)
+  
+- **Original Repository:** [Github](https://github.com/futianfan/clinical-trial-outcome-prediction)
+  
+- **Paper:** [arXiv](https://arxiv.org/abs/2102.04252)
+  
+- **Points of Contact:**
+
+	- Main Original Author - Tianfan Fu (futianfan@gmail.com)
+  
+	- Huggingface Curator and Mantainer - [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com)
+  
+### Dataset Summary
+
+`Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. If we were better at predicting the results of clinical trials, we could avoid having to run trials that will inevitably fail more resources could be devoted to trials that are likely to succeed. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions for all diseases based on a comprehensive and diverse set of web data including molecule information of the drugs, target disease information, trial protocol and biomedical knowledge. HINT first encode these multi-modal data into latent embeddings, where an imputation module is designed to handle missing data. Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web. Then the interaction graph module will connect all the embedding via domain knowledge to fully capture various trial components and their complex relations as well as their influences on trial outcomes. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcome. Comprehensive experimental results show that HINT achieves strong predictive performance, obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and indication outcome prediction, respectively. It also consistently outperforms the best baseline method by up to 12.4\% on PR-AUC.`
+
+### Data Instances
+
+```
+{
+  'TO:DO': ...,
+  ...
+}
+```
+
+### Data Fields
+
+TO:DO
+
+## Additional Information
+
+### Dataset Curators
+
+#### Original Paper
+
+- Tianfan Fu - Georgia Institute of Technology
+- Kexin Huang - Harvard University
+- Cao Xiao - Analytics Center of Excellence, IQVIA
+- Lucas M. - Temple University
+- Jimeng Sun - University of Illinois at Urbana-Champaign
+
+#### Huggingface Curator
+
+- [Artur Guimarães](https://araag2.netlify.app/) (artur.guimas@gmail.com) - INESC-ID / University of Lisbon - Instituto Superior Técnico
+
+### Licensing Information
+
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)
+
+### Citation Information
+
+```
+@article{fu2021hint,
+  title={HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data},
+  author={Fu, Tianfan and Huang, Kexin and Xiao, Cao and Glass, Lucas M and Sun, Jimeng},
+  journal={arXiv preprint arXiv:2102.04252},
+  year={2021}
+}
+```
+
+[10.1016/j.patter.2022.100445](https://doi.org/10.1016/j.patter.2022.100445)
+
+### Contributions
+
+Thanks to [araag2](https://github.com/araag2) for adding this dataset.