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MEME/index.html

@@ -167,14 +167,14 @@ <h2 class="title is-3">Abstract</h2>
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-          <h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">Extracting Affordances Without Extra Annotations</h1>
+          <h1 class="title is-2" style="text-align: center; padding-bottom: 10px;">Transforming Electronic Health Records into text</h1>
           <!-- <h2 class="subtitle is-4" style="text-align: center;">Defining Visual Affordances</h2> -->
 
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-              Robot-centric Affordances: While visual affordances have been studied extensively in computer vision, applying them to robotics needs smart tweaks. As we learn from large-scale human videos but apply to robots, we seek an agent-agnostic affordance to facilitate this transfer. Therefore, we define affordance as:
+            One of our main contributions in our methodology is turning tabular Electronic Health Records (EHR) data into text  
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@@ -645,11 +645,12 @@ <h3 class="title is-3" style="text-align: center; padding-bottom: 10px;"><strong
   <section class="section" id="BibTeX">
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       <h2 class="titile">BibTeX</h2>
-      <pre><code>@inproceedings{bahl2023affordances,
-              title={Affordances from Human Videos as a Versatile Representation for Robotics},
-              author={Bahl, Shikhar and Mendonca, Russell and Chen, Lili and Jain, Unnat and Pathak, Deepak},
-              journal={CVPR},
-              year={2023}
+      <pre><code>@misc{lee2024multimodal,
+        title={Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)},
+        author={Lee, Simon A and Jain, Sujay and Chen, Alex and Biswas, Arabdha and Fang, Jennifer and Rudas, Akos and Chiang, Jeffrey N},
+        journal={arXiv preprint arXiv:2402.00160},
+        year={2024}
+
             }</code></pre>
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@@ -663,8 +664,7 @@ <h2 class="titile">Acknowledgements</h2>
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-          We thank Shivam Duggal, Yufei Ye and Homanga Bharadhwaj for fruitful discussions and are grateful to Shagun Uppal, Ananye Agarwal, Murtaza Dalal and Jason Zhang for comments on early drafts of this paper. RM, LC, and DP are supported by NSF IIS-2024594, ONR MURI N00014-22-1-2773 and ONR N00014-22-1-2096.
-        </p>
+          We thank Arabdha Biswas for helping us run a random forest model.         </p>
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