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more_details [2018/12/05 16:13] (current)
nigam created
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 +==== Answering clinical questions ====
 +  * In [[http://​​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://​​2016winter/​on-the-button.html | point of care]]. Check out our recently launched [[ http://​ | Informatics Consult Service]] that puts this idea in action.
 +  * [[http://​​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://​​2014spring/​article9b.html | Great Medical Mines]] and in the [[http://​​news/​articles/​SB10001424052702304536104579557851593416622 | Wall Street Journal]]
 +  * [[http://​​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://​​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://​​sites/​zinamoukheiber/​2013/​04/​10/​mining-electronic-health-records-reveals-clues-of-harmful-drug-reactions/​|Forbes]],​ [[http://​​2013/​04/​10/​stanford-team-shows-how-doctors-notes-can-spot-problem-drugs/​|GigaOM]]. Our efforts were the focus of an [[http://​​clpt/​journal/​v93/​n6/​full/​clpt201360a.html| editorial commentary]] titled //Advancing the Science of Pharmacovigilance//​.
 +  * [[http://​​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://​​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://​​content/​62/​5/​668.extract | Editorial]] in Clinical Chemistry.
 +  * [[https://​​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://​ | open collaborative research network]]
 +  * [[https://​​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://​​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://​​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://​​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.
more_details.txt · Last modified: 2018/12/05 16:13 by nigam