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 We develop methods to analyze multiple datatypes for **generating insights**. Such as:
-   * Combining molecular data with EHR data to identify [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]]
+   * Identifying [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]], learning effective [[https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2698083| treatment pathways in Type 2 Diabetes]] -- from EHR and Claims data
-  * Learning effective treatment pathways in Type 2 Diabetes with [[http://www.ohdsi.org|OHDSI]] using [[https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2698083| claims data from multiple countries]].
+  * Learning [[http://www.ncbi.nlm.nih.gov/pubmed/26707631| reference intervals of laboratory tests]] and [[http://www.ncbi.nlm.nih.gov/pubmed/26988586| monitoring Point-of-Care glucose meters -- from routine laboratory testing data
-  * Unsupervised learning of reference intervals of laboratory tests from a [[http://www.ncbi.nlm.nih.gov/pubmed/26707631|clinical data warehouse]], and monitoring Point-of-Care glucose meters using [[http://www.ncbi.nlm.nih.gov/pubmed/26988586|coincident testing]] with central laboratory measurements.
+  * Detecting skin adverse reactions by analyzing content in a [[https://jamanetwork.com/journals/jamaoncology/fullarticle/2673831|health social network]], enabling [[https://pubmed.ncbi.nlm.nih.gov/31583282/|medical device surveillance]], discovering drug adverse events as well as drug-drug interactions [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| from clinical notes]] using novel methods for [[https://hai.stanford.edu/news/agile-nlp-clinical-text-covid-19-and-beyond|processing textual documents]].
-  * Detecting skin adverse reactions by analyzing content in a [[https://jamanetwork.com/journals/jamaoncology/fullarticle/2673831|health social network]]. Finding drug adverse events, and drug-drug interactions by analyzing [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| using clinical notes]]; and developing weakly supervised methods for [[https://pubmed.ncbi.nlm.nih.gov/31583282/|medical device surveillance]].
   * Mining Web [[https://www.ncbi.nlm.nih.gov/pubmed/27655225|search logs]] to predict health utilization, and analyzing [[https://www.ncbi.nlm.nih.gov/pubmed/26293444| information seeking behavior]] of health professionals.
   * Inferring physical function from [[https://www.ncbi.nlm.nih.gov/pubmed/30394876|wearables data]], and personalizing ICU [[https://www.ncbi.nlm.nih.gov/pubmed/29218906| alarm thresholds]].
-  * Developing new methods for [[https://hai.stanford.edu/news/agile-nlp-clinical-text-covid-19-and-beyond|processing clinical textual documents]]
-**About us**: [[:lab_members|Lab members]] \\
-**Internal**  (log in required): [[:int:onboarding|On boarding]], [[:int:compute_resources|Compute Resources]], [[:int:lab_communication|Lab communication]], [[:int:projects|Projects]], [[:int:rotation_projects|Rotations]], [[:onboarding_affiliates|For Collaborators]], [[:archive:start|Archived pages]] \\
+**About us**: [[:lab_members|Lab members]], [[:jobs | Open positions]] \\
+**Internal**  (log in required): [[:int:onboarding|On boarding]], [[:int:compute_resources|Compute Resources]], [[:int:lab_communication|Lab communication]], [[:int:projects|Projects]], [[:int:rotation_projects|Rotations]], [[:onboarding_affiliates|For Collaborators]], [[:archive:start|Archived pages]]
 ==== Teaching ====
   * [[:biomedin215|BIOMEDIN 215]], taught for the BMI Graduate program is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data.
-  * [[:biomedin225 | BIOMEDIN 225]], taught for the MCiM program explores how to use electronic health records (EHRs) and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare
+  * [[:biomedin225|BIOMEDIN 225]], taught for the MCiM program explores how to use electronic health records (EHRs) and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare.
-  * [[https://www.coursera.org/specializations/ai-healthcare/| AI in Healthcare Specialization on Coursera]], reviews the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.
+  * [[https://hai.stanford.edu/safe-ethical-and-cost-effective-use-ai-healthcare-critical-topics-senior-leadership|Safe, Ethical, and Cost-Effective Use of AI in Healthcare: Critical Topics for Senior Leadership]], taught in partnership with the Institute for Human-Centered AI (HAI) and the Center for Artificial Intelligence in Medicine & Imaging (AIMI).
-  * [[https://stanfordmlgroup.github.io/programs/aihc-bootcamp/| AI in Healthcare Bootcamp]], provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare
+  * [[https://www.coursera.org/specializations/ai-healthcare/|AI in Healthcare Specialization on Coursera]], created in partnership with the [[https://healtheducation.stanford.edu/|Stanford Center for Health Education]]. The course reviews the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.
+  * [[https://stanfordmlgroup.github.io/programs/aihc-bootcamp/|AI in Healthcare Bootcamp]], provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare
   * Miscellaneous [[:other_talks|Talks]], [[:seminars|Seminars]]

Shah Lab

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