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nigam [Selected Talks]
start [2024/02/06 12:02] (current)
nigam
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-We analyze multiple data types (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models at the [[http://bmir.stanford.edu/ Stanford Center for Biomedical Informatics Research]]. We use machine learning, text-mining, and prior knowledge in medical ontologies to enable the learning health system.+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** 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]].
-{{youtube>Njphqhju5Fo?size=840x470}} +
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-====== Group information ======+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.
  
-Current Group[[Lab members]] \\ +We **develop methods** to analyze multiple datatypes for generating insights. Such as:
-On Boarding: [[int:Onboarding|New Lab members]], [[onboarding_affiliates|For Collaborators]] \\ +
-Internal (log in required): [[int:Lab information]], [[int:Lab communication]], [[int:Projects]], [[int:rotation_projects|Rotations]], [[archive:|Archived pages]] \\ +
-Contact: [[Nigam Shah]]+
  
-====== Teaching ======+   * 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.
  
-  * [[BIOMEDIN215|BIOMEDIN 215 Data Driven Medicine]] Autumn quarter of each year +**About us**: [[:lab_members|Lab members]], [[:jobs| Open positions]] \\ 
-  * [[AIHC BootcampAI in Healthcare Bootcamp]]+**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]]
  
-====== Research ======+==== Teaching ====
  
-==== Answering clinical questions ====+  * [[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]]
  
- 
-  * [[http://content.healthaffairs.org/content/33/7/1229.abstract | A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care]]: We suggest a “green button” function within EHRs for clinicians to use aggregate patient data for real time decision making at the [[http://stanmed.stanford.edu/2016winter/on-the-button.html | point of care]]. Check out the [[greenbutton|idea]], or our recently launched [[ http://tinyurl.com/inf-consult-2017 | Informatics Consult Service]] that puts this idea in action. 
-  * [[http://www.ped-rheum.com/content/11/1/45| Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis]]: We report a new association between allergic conditions and chronic uveitis. Covered in Stanford Medicine Mag -- [[http://stanmed.stanford.edu/2014spring/article9b.html | Great Medical Mines]] and in the [[http://online.wsj.com/news/articles/SB10001424052702304536104579557851593416622 | Wall Street Journal]] 
-  * [[http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124653 | Proton Pump Inhibitor Usage and the Risk of Myocardial Infarction in the General Population]]: This study provides an example of how a combination of experimental studies and data-mining approaches can be applied to prioritize drug safety signals for further investigation.See coverage in [[http://www.npr.org/sections/health-shots/2015/06/11/413433350/data-dive-suggests-link-between-heartburn-drugs-and-heart-attacks |NPR]], [[http://www.washingtonpost.com/news/to-your-health/wp/2015/06/10/common-heartburn-medications-linked-to-greater-risk-of-heart-attack/ |Washington Post]], [[http://well.blogs.nytimes.com/2015/06/10/gastric-reflux-drugs-linked-to-heart-attacks/?_r=0 |NY Times]], [[http://www.forbes.com/sites/robertglatter/2015/06/11/common-acid-reflux-drugs-associated-with-increased-risk-for-heart-attacks/ | Forbes]], [[ http://ww2.kqed.org/stateofhealth/2015/06/10/stanford-big-data-study-links-common-heartburn-drugs-with-heart-attack-risk/|KQED]], [[http://www.foxnews.com/health/2015/06/10/study-links-common-heartburn-drug-to-increased-heart-attack-risk/ |Fox News]], and [[http://www.scientificamerican.com/article/new-software-and-genetic-analyses-aim-to-reduce-problems-with-multiple-drug-combinations/ | Scientific American]]. 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/26644522 | Androgen Deprivation Therapy and Future Alzheimer's Disease Risk]]: This study found an association between the use of ADT in the treatment of prostate cancer and an increased risk of Alzheimer's disease in a general population cohort. 
- 
-==== Insights from data ==== 
- 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| Pharmacovigilance using clinical notes]]:  Uses textual clinical notes for detecting single drug–adverse event associations (AUC of 80.4%) and for detecting drug–drug interactions (AUC of 81.5%). Press in [[http://www.forbes.com/sites/zinamoukheiber/2013/04/10/mining-electronic-health-records-reveals-clues-of-harmful-drug-reactions/|Forbes]], [[http://gigaom.com/2013/04/10/stanford-team-shows-how-doctors-notes-can-spot-problem-drugs/|GigaOM]]. Our efforts were the focus of an [[http://www.nature.com/clpt/journal/v93/n6/full/clpt201360a.html| editorial commentary]] titled //Advancing the Science of Pharmacovigilance//. 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/26293444 | Information Seeking and Drug-Safety Alert Response by Health Care Professionals]]: The information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/26707631 | An unsupervised learning method to identify reference intervals from a clinical database]]: We show that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses. 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/26988586 | Postmarket Surveillance of Point-of-Care Glucose Meters through Analysis of Electronic Medical Records]]: We show that it is possible to assess device accuracy using coincident testing of point-of-care and central laboratory blood glucose measurements in a large cohort of critically ill patients. [[http://www.clinchem.org/content/62/5/668.extract | Editorial]] in Clinical Chemistry. 
- 
-==== Predictive Modeling ====  
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/24988898|Predicting Diagnoses of Depression]]: We developed a model that uses electronic medical record (EMR) data for predicting the diagnosis of depression up to 12 months before the diagnosis of depression. 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/26606167 | Rapid identification of slow healing wounds]]: We demonstrate that it is possible to build a model for identifying delayed healing wounds with an Area Under the Curve (AUC) of 0.842 that works across all wound types. 
-  * [[http://www.ncbi.nlm.nih.gov/pubmed/26483171 | Implications of non-stationarity on predictive modeling using EHRs]]: Under the non-stationarity in the underlying dataset, the performance advantage of complex methods such as stacking relative to the best simple classifier disappears. Ignoring non-stationarity can thus lead to sub-optimal model selection in predictive modeling tasks. 
-  * [[http://bmjopen.bmj.com/cgi/content/full/bmjopen-2016-011580?ijkey=oCxNIjOhCzOdmR8&keytype=ref | Predicting patient ‘cost blooms’]]: We develop models that identify new entrants to the upper decile of per capita healthcare expenditures in the next year. 
- 
-====== Selected Talks ===== 
-{{youtube>xW3drA3ijRc?small | Building a Machine Learning Healthcare System, at XLDB 2018}} Building a Machine Learning Healthcare System, at XLDB 2018 
 ---- ----
  
-{{youtube>2ERCBBQOMlg?small&start=460 | Performing an Informatics Consult, Grand rounds in Medicine at Stanford, Feb 1 2017}} Performing an Informatics Consult, Grand rounds in Medicine at Stanford, Feb 1 2017 +<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>//
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-[[Other talks]] 
-====== Seminars on campus ===== 
  
-[[Seminars]] 
start.1532538256.txt.gz · Last modified: 2018/07/25 10:04 by nigam