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start [2022/09/05 16:52]
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start [2022/09/05 16:53]
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| We develop methods to analyze multiple datatypes for **generating insights**. Such as: | We develop methods to analyze multiple datatypes for **generating insights**. Such as: |
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| * Identifying [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]], 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]]. | * Identifying [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]], 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]] using routine laboratory testing data. |
| * 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]], 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]]. |
| * Inferring physical function from [[https://www.ncbi.nlm.nih.gov/pubmed/30394876|wearables data]], predicting healthcare utilization from [[https://www.ncbi.nlm.nih.gov/pubmed/27655225|Web search logs]] and understanding [[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]], predicting healthcare utilization from [[https://www.ncbi.nlm.nih.gov/pubmed/27655225|Web search logs]] and understanding [[https://www.ncbi.nlm.nih.gov/pubmed/26293444| information seeking behavior]] of health professionals. |
| * [[: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. | * [[: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://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://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://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 | * [[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 |
