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nigam
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nigam
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-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 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_ideaidea]] in action and led to the creation of [[https://www.atroposhealth.com/Atropos Health]].
-  * We **make predictions**  that allow taking mitigating actions, and also study 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 [[:paihc| Program for AI in Healthcare]] +
-  * We develop methods to analyze multiple datatypes for **generating insights**. Such as, 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 reference intervals of laboratory tests from a [[http://www.ncbi.nlm.nih.gov/pubmed/26707631|clinical data warehouse]]. 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]]. Finding drug adverse events, and drug-drug interactions using [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| using clinical notes]]. Mining user [[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]]. Assessing [[https://www.ncbi.nlm.nih.gov/pubmed/29557976| impact of informatics tools]] and databases, and profiling [[https://jamanetwork.com/journals/jama/fullarticle/2595514research on gun violence]].+
  
-**About us**[[:lab_members|Lab members]] \\ +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 [[:aihcProgram for AI in Healthcare]] conducts the research which the [[:datascienceApplied Data Science team]] puts into practice.
-**Internal**  (log in required): [[:int:onboarding|New Lab members]][[:int:lab_information|Lab information]][[:int:lab_communication|Lab communication]], [[:int:projects|Projects]], [[:int:rotation_projects|Rotations]], [[:onboarding_affiliates|For Collaborators]], [[:archive:start|Archived pages]] \\ +
-**Teaching**: [[:biomedin215|BIOMEDIN 215]] Autumn quarter of each year, [[:aihc_bootcamp| AI in Healthcare Bootcamp]] \\ +
-**Selected talks and videos**: \\ —-+
  
-{{youtube>Njphqhju5Fo?small | Supporting clinical decision making at the bedside}}+We **develop methods** to analyze multiple datatypes for generating insights. Such as:
  
-2 min overview of our work in supporting clinical decision making at the bedside.+   * 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]]. 
 +  * 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]] \\ 
- +**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]]
-{{youtube>fuyVriaq5Vk?small One minute video on the Informatics Consult Service}} +
- +
-1 min video on the Informatics Consult Service. +
-----+
  
-{{vimeo>375462436?small | NIH Collaboratory}}+==== Teaching ====
  
-45 min Grand Rounds at the NIH Collaboratory on Nov 222019+  * [[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. 
 +  * [[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]], 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/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]]
  
 ---- ----
  
-[[:other_talks|Other talks]][[:seminars|Seminars]]+<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="autoplayfullscreen" style="margin0px auto; display: block;"></iframe> </html> —- //
  
  
start.1581449959.txt.gz · Last modified: 2020/02/11 11:39 by nigam