User Tools

Site Tools


start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
start [2022/09/05 16:53]
nigam
start [2024/02/06 12:02] (current)
nigam
Line 1: Line 1:
-We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system[[:more_details|Read more ...]]+We analyze multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), in service of the learning health system ([[:more_details|see examples]]).
  
-We **answer clinical questions** to enable better medical decisions using EHR and Claims data, via a bedside consult service that enables the use of aggregate patient data at the [[http://stanmed.stanford.edu/2016winter/on-the-button.html|point of care]]. Check out our [[:greenbutton| Informatics Consult Service]] that puts this [[https://shahlab.stanford.edu/greenbutton_idea| idea]] in action.+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]].
  
-We **make predictions** that allow taking mitigating actions. We [[https://arxiv.org/abs/2007.10306|characterize the fairness]] and examine the [[https://www.nejm.org/doi/full/10.1056/NEJMp1714229|ethical implications]] of using machine learning in clinical care. We have built models for predicting [[http://bmjopen.bmj.com/cgi/content/full/bmjopen-2016-011580?ijkey=oCxNIjOhCzOdmR8&keytype=ref| future increases in cost]], identifying [[http://www.ncbi.nlm.nih.gov/pubmed/26606167| slow healing wounds]], [[http://www.ncbi.nlm.nih.gov/pubmed/24988898|missed diagnoses of depression]] and for [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.htmlimproving palliative care]]. Check out our [[:aihc| Program for AI in Healthcare]]+We **make predictions** that allow taking mitigating actions[[https://stanmed.stanford.edu/artificial-intelligence-puts-humanity-health-care/|keeping the human in the loop]]. The [[:aihc| Program for AI in Healthcare]] conducts the research which the [[:datascience| Applied Data Science team]] puts into practice.
  
-We develop methods to analyze multiple datatypes for **generating insights**. Such as:+We **develop methods** to analyze multiple datatypes for generating insights. Such as:
  
    * 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.    * 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.
Line 11: Line 11:
   * 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.
  
- +**About us**: [[:lab_members|Lab members]], [[:jobs| Open positions]] \\
-**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]] **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 ==== ==== 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. +  * [[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. 
-  * [[: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://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://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]], 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://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://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]], [[:seminars|Seminars]]   * Miscellaneous [[:other_talks|Talks]], [[:seminars|Seminars]]
  
start.1662422028.txt.gz · Last modified: 2022/09/05 16:53 by nigam