| Both sides previous revision
Previous revision
|
Next revision
Both sides next revision
|
start [2022/09/05 16:40]
nigam |
start [2022/09/05 16:47]
nigam |
| We develop methods to analyze multiple datatypes for **generating insights**. Such as: | We develop methods to analyze multiple datatypes for **generating insights**. Such as: |
| |
| * Combining molecular data with EHR data to identify [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]] | * Identifying [[https://www.sciencedirect.com/science/article/pii/S2213260018305083|biomarkers for poor outcomes in fibrotic diseases]], learning effective [[https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2698083| treatment pathways in Type 2 Diabetes]] -- from EHR and Claims data |
| * 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 [[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 -- from routine laboratory testing data |
| * Unsupervised learning of reference intervals of laboratory tests from a [[http://www.ncbi.nlm.nih.gov/pubmed/26707631|clinical data warehouse]], and 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]], 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]]. |
| * 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 by analyzing [[http://www.ncbi.nlm.nih.gov/pubmed/23571773| using clinical notes]]; and developing weakly supervised methods for [[https://pubmed.ncbi.nlm.nih.gov/31583282/|medical device surveillance]]. | |
| * Mining Web [[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. | * Mining Web [[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]]. | * 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]]. |
| * Developing new methods for [[https://hai.stanford.edu/news/agile-nlp-clinical-text-covid-19-and-beyond|processing clinical textual documents]] | |
| |
| **About us**: [[:lab_members|Lab members]], [[:jobs | Open positions]] \\ | **About us**: [[:lab_members|Lab members]], [[:jobs | Open positions]] \\ |
