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start [2025/09/06 10:46] (current)
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| We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), in service of the learning health system ([[:examples_of_prior_work|see examples]]). The work can be grouped into three focus areas: | We are a group of doctors, engineers, informatics professionals and students focused on enabling better care using existing health data. We develop novel methods to learn from patient-level health data, answer clinical questions that enable better medical decisions at the point of care, and have an active effort to research safe, ethical, and cost-effective strategies for using predictive models to guide mitigating care actions. Our research group is part of the Department of Medicine at Stanford, the Clinical Excellence Research Center, and the Department of Biomedical Data Science. |
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| (1) We **answer clinical questions** using aggregate patient data at the [[http://stanmed.stanford.edu/2016winter/on-the-button.html|bedside]]. The [[:greenbutton| Informatics Consult Service]] put this [[https://shahlab.stanford.edu/greenbutton_idea| idea]] in action and led to the creation of [[https://www.atroposhealth.com/| Atropos Health]]. | ===== About us ===== |
| | [[:lab_members|Lab members]], [[:jobs| Open positions]], [[:blogs-and-media| Blogs and media]], [[https://news.google.com/topics/CAAqJAgKIh5DQkFTRUFvS0wyMHZNR295WDJoell4SUNaVzRvQUFQAQ?ceid=US:en&oc=3 | News and Press]] \\ |
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| (2) We **build predictive models** that allow taking mitigating actions, [[https://stanmed.stanford.edu/artificial-intelligence-puts-humanity-health-care/|keeping the human in the loop]]. Research on [[:rail| Responsible AI]] happens in the lab which the [[:dsatshc| Data Science team at SHC]] puts into practice. | ===== Research ===== |
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| | We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), in service of the learning health system ([[:examples_of_prior_work|see examples]]). The work can be grouped into three focus areas: |
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| (3) We **develop methods** to analyze multiple datatypes for generating insights. Such as: 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. | - We **develop methods** to analyze multiple datatypes for generating insights such as 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 [[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]]. |
| | - We **answer clinical questions** using aggregate patient data at the [[http://stanmed.stanford.edu/2016winter/on-the-button.html|bedside]]. The [[:greenbutton| green button project]] established the viability of this idea and led to the creation of [[https://www.atroposhealth.com/| Atropos Health]]. |
| | - We **build predictive models** that allow taking mitigating actions, [[https://stanmed.stanford.edu/artificial-intelligence-puts-humanity-health-care/|keeping the human in the loop]]. Research on [[:rail| foundation models]] from our team is put into practice by the [[https://dsatshc.stanford.edu/| Data Science team at SHC]]. |
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| **About us**: [[:lab_members|Lab members]], [[:jobs| Open positions]]\\ | |
| **Internal** (log in required): [[:int:onboarding|On boarding]], [[:int:compute_resources|Compute & Data Resources]], [[:int:lab_communication|Group communication]], [[:int:projects|Projects]], [[:int:rotation_projects|Rotations]] | |
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| ==== Teaching ==== | ===== Teaching ===== |
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| * [[https://biomedin215.stanford.edu/|BIOMEDIN 215 Data Science for Medicine]], taught for the BMI Graduate program is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data. | === On campus === |
| * [[https://explorecourses.stanford.edu/search?q=BIOMEDIN+225|BIOMEDIN 225 Data Science for Medicine]], 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://shahlab.stanford.edu/bmds215/|BMDS 215]], taught for the DBDS Graduate program is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data. |
| * [[https://www.coursera.org/specializations/ai-healthcare/|AI in Healthcare Specialization on Coursera]], which 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://navigator.stanford.edu/classes/1266/17310|CIM 213]], 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://online.stanford.edu/courses/xbiomedin215-machine-learning-projects-healthcare|XBIOMEDIN215 Machine Learning Projects in Healthcare]], where you work through interactive exercises and case studies, attend live webinars, receive ongoing feedback from the course team, and collaborate with your fellow learners to gain the real-world skills you need to run your own machine learning projects. | |
| * [[https://stanfordmlgroup.github.io/projects/aihc/|AI in Healthcare Bootcamp]], provides students an opportunity to do cutting-edge research at the intersection of AI and healthcare. | |
| * Miscellaneous [[:other_talks|Talks]] | |
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| ---- | === Online === |
| | * [[https://online.stanford.edu/programs/artificial-intelligence-healthcare | Artificial Intelligence in Healthcare]], which 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://online.stanford.edu/programs/applications-machine-learning-medicine-program|Applications of Machine Learning in Medicine Program]], where you work through interactive exercises and case studies, attend live webinars, receive ongoing feedback from the course team, and collaborate with your fellow learners to gain the real-world skills doing machine learning projects. |
| | * [[https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program#program-courses | Generative AI: Technology, Business, and Society Program]], which covers technical fundamentals, business implications, and societal considerations, all with a focus on putting people first. |
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| <html> <iframe src="https://slideslive.com/embed/presentation/38931909?auto_play=&zoom_ratio=&disable_fullscreen=&locale=en&demo=&vertical_enabled=true&vertical_enabled_on_mobile=&vertical_when_width_lte=500&allow_hidden_controls_when_paused=true&user_uuid=3760fd95-4c65-4d33-af8f-14b581de0e6c" width="1094" height="685" scrolling="no" frameborder="0" allowfullscreen=_ckgedit_QUOT__ckgedit> webkitallowfullscreen=_ckgedit_QUOT__ckgedit> mozallowfullscreen="" sandbox="allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-top-navigation allow-storage-access-by-user-activation" allow="autoplay, fullscreen" style="margin: 0px auto; display: block;"></iframe> </html> —- // | ===== Public Talks ===== |
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