From a4078a045c51295099c72990412e5894727c121c Mon Sep 17 00:00:00 2001 From: marinkaz Date: Fri, 29 Nov 2024 15:55:47 -0500 Subject: [PATCH] Unified clinical vocabulary embeddings --- data/index.html | 39 + feed.xml | 2 +- img/Clinical-knowledge-embeddings-1.png | Bin 0 -> 1349850 bytes img/Clinical-knowledge-embeddings-2.png | Bin 0 -> 1743171 bytes img/Clinical-knowledge-embeddings-3.png | Bin 0 -> 981447 bytes img/Clinical-knowledge-embeddings-4.png | Bin 0 -> 908928 bytes img/Clinical-knowledge-embeddings-5.png | Bin 0 -> 1076572 bytes img/inaki_arango.png | Bin 0 -> 224931 bytes img/kexin_chen.png | Bin 0 -> 121474 bytes people/index.html | 115 ++- products/ada_fang/index.html | 4 +- products/alejandro_velez_arce/index.html | 4 +- products/andrew_shen/index.html | 4 +- products/ayush_noori/index.html | 4 +- products/grey_kuling/index.html | 4 +- products/inaki_arango/index.html | 195 ++++ products/intae_moon/index.html | 4 +- products/kevin_li/index.html | 4 +- products/kexin_chen/index.html | 195 ++++ products/marinka_zitnik/index.html | 4 +- products/michelle_dai/index.html | 4 +- products/michelle_li/index.html | 4 +- products/pengwei_sui/index.html | 4 +- products/richard_zhu/index.html | 4 +- products/robert_calef/index.html | 4 +- products/ruth_johnson/index.html | 4 +- products/shanghua_gao/index.html | 4 +- products/valentina_giunchiglia/index.html | 4 +- products/wanxiang_shen/index.html | 4 +- products/xiang_lin/index.html | 4 +- products/xiaorui_su/index.html | 4 +- products/yasha_ektefaie/index.html | 4 +- products/yepeng_huang/index.html | 4 +- products/ying_jin/index.html | 4 +- products/zhenglun_kong/index.html | 4 +- .../Clinical-knowledge-embeddings/index.html | 857 ++++++++++++++++++ projects/PocketFlow/index.html | 2 +- projects/UniTS/index.html | 2 +- publications/thumbnails/AIAgents.png | Bin 10450 -> 67743 bytes publications/thumbnails/InvariantCrystals.png | Bin 0 -> 55478 bytes publications/thumbnails/PocketGen.png | Bin 12089 -> 59404 bytes publications/thumbnails/tdc2.png | Bin 0 -> 57098 bytes publications/thumbnails/txgnn.png | Bin 0 -> 56472 bytes pubs.json | 27 +- research/index.html | 29 +- sitemap.xml | 59 +- software/index.html | 39 + 47 files changed, 1514 insertions(+), 139 deletions(-) create mode 100644 img/Clinical-knowledge-embeddings-1.png create mode 100644 img/Clinical-knowledge-embeddings-2.png create mode 100644 img/Clinical-knowledge-embeddings-3.png create mode 100644 img/Clinical-knowledge-embeddings-4.png create mode 100644 img/Clinical-knowledge-embeddings-5.png create mode 100644 img/inaki_arango.png create mode 100644 img/kexin_chen.png create mode 100644 products/inaki_arango/index.html create mode 100644 products/kexin_chen/index.html create mode 100644 projects/Clinical-knowledge-embeddings/index.html create mode 100644 publications/thumbnails/InvariantCrystals.png create mode 100644 publications/thumbnails/tdc2.png create mode 100644 publications/thumbnails/txgnn.png diff --git a/data/index.html b/data/index.html index 5d075236..47cfaddb 100644 --- a/data/index.html +++ b/data/index.html @@ -146,6 +146,45 @@ +
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Unified Clinical Vocabulary Embeddings

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Unified Clinical Vocabulary Embeddings

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Integrating clinical knowledge into AI remains challenging despite standardized medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability.

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Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge unified across seven medical vocabularies. We validated these embeddings through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, demonstrating their ability to stratify individuals by survival outcomes. Inter-institutional panels of clinicians evaluated the alignment of embedding with established clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability.

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This resource addresses the gap in integrating clinical vocabularies in AI models and training datasets and supports population and patient modelsin precision medicine.

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diff --git a/feed.xml b/feed.xml index c46b8b53..e7c61d9f 100644 --- a/feed.xml +++ b/feed.xml @@ -1 +1 @@ -Jekyll2024-11-17T13:35:16-05:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikAyush Noori Selected as a Rhodes Scholar2024-11-17T00:00:00-05:002024-11-17T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar<p>Congratulations to <a href="https://www.thecrimson.com/article/2024/11/18/rhodes-scholars-announced-harvard-students/">Ayush Noori on being named a Rhodes Scholar</a>! Such an incredible achievement!</p>Marinka ZitnikCongratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!PocketGen in Nature Machine Intelligence2024-11-15T00:00:00-05:002024-11-15T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen<p>PocketGen is a <a href="https://www.nature.com/articles/s42256-024-00920-9">multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</a></p>Marinka ZitnikPocketGen is a multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.Biomedical AI Agents in Cell2024-11-01T00:00:00-04:002024-11-01T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist<p>We envision “AI scientists” as <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5">AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</a></p>Marinka ZitnikWe envision “AI scientists” as AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.Activity Cliffs in Molecular Property Prediction2024-10-19T00:00:00-04:002024-10-19T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet<p>New paper on <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6470c963be16ad5c57f5526c">activity-cliff informed contrastive learning for molecular property prediction.</a></p>Marinka ZitnikNew paper on activity-cliff informed contrastive learning for molecular property prediction.Knowledge Graph Agent for Medical Reasoning2024-10-09T00:00:00-04:002024-10-09T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion<p>New paper introducing a <a href="https://arxiv.org/abs/2410.04660">knowledge graph agent for complex, knowledge-intensive medical reasoning.</a></p>Marinka ZitnikNew paper introducing a knowledge graph agent for complex, knowledge-intensive medical reasoning.Three Papers Accepted to NeurIPS2024-09-27T00:00:00-04:002024-09-27T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers<p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p>Marinka ZitnikExciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.TxGNN Published in Nature Medicine2024-09-25T00:00:00-04:002024-09-25T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine<p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a> <a href="https://developer.nvidia.com/blog/ai-uses-zero-shot-learning-to-find-existing-drugs-for-treating-rare-diseases/">[NVIDIA]</a> <a href="https://kempnerinstitute.harvard.edu/news/txgnn-ai-dr-house-for-disease-treatment/">[Kempner Institute]</a> <a href="https://www.thecrimson.com/article/2024/10/9/drug-repurposing-ai-model/">[Harvard Crimson]</a></p>Marinka ZitnikGraph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] [NVIDIA] [Kempner Institute] [Harvard Crimson]Graph AI in Medicine2024-08-28T00:00:00-04:002024-08-28T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/08/28/GraphAI<p>Excited to share a new perspective on <a href="https://go.shr.lc/4g0KpLV">Graph Artificial Intelligence in Medicine</a> in Annual Reviews.</p>Marinka ZitnikExcited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.How Proteins Behave in Context2024-08-15T00:00:00-04:002024-08-15T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/08/15/PINNACLENews<p><a href="https://hms.harvard.edu/news/new-ai-tool-captures-how-proteins-behave-context">Harvard Medicine News</a> on our new AI tool that captures how proteins behave in context. <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/context-matters-for-foundation-models-in-biology/">Kempner Institute</a> on how context matters for foundation models in biology.</p>Marinka ZitnikHarvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.PINNACLE in Nature Methods2024-07-27T00:00:00-04:002024-07-27T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods<p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>Marinka ZitnikPINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website. \ No newline at end of file +Jekyll2024-12-02T21:40:57-05:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikAyush Noori Selected as a Rhodes Scholar2024-11-17T00:00:00-05:002024-11-17T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar<p>Congratulations to <a href="https://www.thecrimson.com/article/2024/11/18/rhodes-scholars-announced-harvard-students/">Ayush Noori on being named a Rhodes Scholar</a>! Such an incredible achievement!</p>Marinka ZitnikCongratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!PocketGen in Nature Machine Intelligence2024-11-15T00:00:00-05:002024-11-15T00:00:00-05:00https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen<p>PocketGen is a <a href="https://www.nature.com/articles/s42256-024-00920-9">multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</a></p>Marinka ZitnikPocketGen is a multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.Biomedical AI Agents in Cell2024-11-01T00:00:00-04:002024-11-01T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist<p>We envision “AI scientists” as <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5">AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</a></p>Marinka ZitnikWe envision “AI scientists” as AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.Activity Cliffs in Molecular Property Prediction2024-10-19T00:00:00-04:002024-10-19T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet<p>New paper on <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6470c963be16ad5c57f5526c">activity-cliff informed contrastive learning for molecular property prediction.</a></p>Marinka ZitnikNew paper on activity-cliff informed contrastive learning for molecular property prediction.Knowledge Graph Agent for Medical Reasoning2024-10-09T00:00:00-04:002024-10-09T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion<p>New paper introducing a <a href="https://arxiv.org/abs/2410.04660">knowledge graph agent for complex, knowledge-intensive medical reasoning.</a></p>Marinka ZitnikNew paper introducing a knowledge graph agent for complex, knowledge-intensive medical reasoning.Three Papers Accepted to NeurIPS2024-09-27T00:00:00-04:002024-09-27T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers<p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p>Marinka ZitnikExciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.TxGNN Published in Nature Medicine2024-09-25T00:00:00-04:002024-09-25T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine<p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a> <a href="https://developer.nvidia.com/blog/ai-uses-zero-shot-learning-to-find-existing-drugs-for-treating-rare-diseases/">[NVIDIA]</a> <a href="https://kempnerinstitute.harvard.edu/news/txgnn-ai-dr-house-for-disease-treatment/">[Kempner Institute]</a> <a href="https://www.thecrimson.com/article/2024/10/9/drug-repurposing-ai-model/">[Harvard Crimson]</a></p>Marinka ZitnikGraph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] [NVIDIA] [Kempner Institute] [Harvard Crimson]Graph AI in Medicine2024-08-28T00:00:00-04:002024-08-28T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/08/28/GraphAI<p>Excited to share a new perspective on <a href="https://go.shr.lc/4g0KpLV">Graph Artificial Intelligence in Medicine</a> in Annual Reviews.</p>Marinka ZitnikExcited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.How Proteins Behave in Context2024-08-15T00:00:00-04:002024-08-15T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/08/15/PINNACLENews<p><a href="https://hms.harvard.edu/news/new-ai-tool-captures-how-proteins-behave-context">Harvard Medicine News</a> on our new AI tool that captures how proteins behave in context. <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/context-matters-for-foundation-models-in-biology/">Kempner Institute</a> on how context matters for foundation models in biology.</p>Marinka ZitnikHarvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.PINNACLE in Nature Methods2024-07-27T00:00:00-04:002024-07-27T00:00:00-04:00https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods<p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>Marinka ZitnikPINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. 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