Shah Lab

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==== Answering clinical questions ====


  * In [[http://content.healthaffairs.org/content/33/7/1229.abstract | A ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care]] we envision 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 our recently launched [[ http://greenbutton.stanford.edu | 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://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/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.
  * [[https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2698083 | Learning Effective Treatment Pathways in Patients With Type 2 Diabetes Treated With Metformin]]: Where we examine the effectiveness of second-line treatment of type 2 diabetes after initial therapy with metformin via an [[ http://www.ohdsi.org | open collaborative research network]]
  * [[https://jamanetwork.com/journals/jamaoncology/fullarticle/2673831 | Detecting Chemotherapeutic Skin Adverse Reactions in Social Health Networks Using Deep Learning]]: Where we identify a cutaneous adverse event of a chemotherapeutic agent by analyzing content in a health social network.

==== 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://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.
Shah Lab

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