{"id":552,"date":"2019-08-01T20:35:08","date_gmt":"2019-08-01T20:35:08","guid":{"rendered":"https:\/\/vinci.cs.uiowa.edu\/compepi\/?page_id=552"},"modified":"2020-07-28T23:52:22","modified_gmt":"2020-07-28T23:52:22","slug":"projects","status":"publish","type":"page","link":"https:\/\/vinci.cs.uiowa.edu\/compepi\/?page_id=552","title":{"rendered":"Projects"},"content":{"rendered":"<p><strong>Inferring HAI characteristics: <\/strong>Using fine-grained spatiotemporal data, we aim to improve our understanding of characteristics of HAIs such as C.diff. Our recent work studies the impact of exposure to family members with C. diff infection and other risk factors on the likelihood of acquiring C.diff infection , the significance of spatiotemporal interactions on C.diff infections within a hospital , and the impact of hospital transfers on C.diff infection rates in hospitals .<\/p>\n<hr \/>\n<p><strong>Inferring agent-behavior in healthcare settings: <\/strong>Using data, sometimes gathered using novel technology, we aim to infer behavior of healthcare personnel and patients in hospital settings. We use electronic medical records, sensor network instrumentation, kinect cameras, etc. to estimate contact networks of healthcare personnel and patients , hand hygiene behavior of healthcare personnel , and duration of close-contacts between healthcare personnel and patients in hospital-rooms .<\/p>\n<hr \/>\n<p><strong>HAI risk prediction: <\/strong>We build machine learning prediction models using detailed electronic medical record data overlaid with hospital architectural layout for predicting patient risk. In recent work [bibcite\u00a0 key=epidamik20] we use a 2-stage prediction model to identify latent C.diff infections (e.g., asymptomatic C.diff carriers). In , we predict the daily risk of a patient acquiring a C.diff infection by taking the temporal ordering of events into account as features.<\/p>\n<hr \/>\n<p><strong>Disease-surveillance: <\/strong>We study the use of social media &#8212; Twitter , Wikipedia , and Craig&#8217;s list  &#8212; in helping with disease surveillance. We model the geographic placement of surveillance sites as an optimization problem  and propose methods for computing optimal screening rates in . We also build apps for individual-level surveillance .<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Inferring HAI characteristics: Using fine-grained spatiotemporal data, we aim to improve our understanding of characteristics of HAIs such as C.diff. Our recent work studies the impact of exposure to family members with C. diff infection and other risk factors on the likelihood of acquiring C.diff infection , the significance of spatiotemporal interactions on C.diff infections [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=\/wp\/v2\/pages\/552"}],"collection":[{"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=552"}],"version-history":[{"count":11,"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=\/wp\/v2\/pages\/552\/revisions"}],"predecessor-version":[{"id":651,"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=\/wp\/v2\/pages\/552\/revisions\/651"}],"wp:attachment":[{"href":"https:\/\/vinci.cs.uiowa.edu\/compepi\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=552"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}