Update README.md

README.md

@@ -19,7 +19,7 @@ Laboratory data are a rich source of information about a patient's health. They
 
 Labrador is pre-trained on a large corpus of 100 million lab tests from over 260,000 patients. We rigorously evaluate Labrador on intrinsic and extrinsic tasks, including four real-world problems: cancer diagnosis, COVID-19 diagnosis, predicting elevated alcohol consumption and ICU mortality due to sepsis. We find that Labrador is superior to BERT across all evaluations but both are outperformed by XGBoost indicating that transfer learning from continuous EHR data is still an open problem. 
 
-We discuss the limitations of our approach and suggest future directions for research in the corresponding paper, [Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data](). 
+We discuss the limitations of our approach and suggest future directions for research in the corresponding paper, [Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data](https://arxiv.org/abs/2312.11502). 
 
 
 - **Developed by:** David Bellamy
@@ -56,7 +56,7 @@ See the [Get Started instructions](https://github.com/DavidBellamy/labrador?tab=
 
 ## Training & Evaluation Details
 
-See the associated [publication]() and [codebase](https://github.com/DavidBellamy/labrador).
+See the associated [publication](https://arxiv.org/abs/2312.11502) and [codebase](https://github.com/DavidBellamy/labrador).
 
 
 ## Environmental Impact