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 [1, 2, 3], the significance of spatiotemporal interactions on C.diff infections within a hospital [4], and the impact of hospital transfers on C.diff infection rates in hospitals [5].<\/p>\n
A. C. Miller, A. M. Segre, S. V. Pemmaraju, D. K. Sewell, and P. M. Polgreen, \u201cAssociation of Household Exposure to Primary Clostridioides difficile Infection with Secondary Infection in Family Members,\u201d JAMA Network Open, vol. 3, iss. 6, 2020. [PDF]<\/a><\/p>\n<\/li>\n A. C. Miller, L. A. Polgreen, J. E. Cavanaugh, and P. M. Polgreen, \u201cHospital Clostridium difficile infection (CDI) incidence as a risk factor for hospital-associated CDI,\u201d American Journal of Infection Control, vol. 44, iss. 7, pp. 825-829, 2016. [PDF]<\/a><\/p>\n<\/li>\n A. C. Miller, L. A. Polgreen, J. E. Cavanaugh, and P. M. Polgreen, \u201cHospital Clostridium difficile Infection Rates and Prediction of Length of Stay in Patients Without C. difficile Infection,\u201d Infection Control and Hospital Epidemiology, vol. 37, iss. 4, pp. 404-410, 2016. [PDF]<\/a><\/p>\n<\/li>\n S. Pai, P. M. Polgreen, A. M. Segre, D. K. Sewell, and S. V. Pemmaraju, \u201cSpatiotemporal Clustering of In-Hospital Clostridioides difficile Infection (CDI),\u201d Infection Control and Hospital Epidemiology, vol. 41, iss. 4, pp. 418-424, 2020. [PDF]<\/a><\/p>\n<\/li>\n D. K. Sewell, J. E. Simmering, S. Justice, S. V. Pemmaraju, A. M. Segre, and P. M. Polgreen, \u201cEstimating the Attributable Disease Burden and Effects of Inter-Hospital Patient Sharing on Clostridium difficile Infections,\u201d Infection Control and Hospital Epidemiology, vol. 40, pp. 656-661, 2019. [PDF]<\/a><\/p>\n<\/li>\n<\/ol>\n 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 [1], hand hygiene behavior of healthcare personnel [2], and duration of close-contacts between healthcare personnel and patients in hospital-rooms [3].<\/p>\n D. E. Curtis, C. S. Hlady, G. Kanade, S. V. Pemmaraju, P. M. Polgreen, and A. M. Segre, \u201cHealthcare Worker Contact Networks and the Prevention of Hospital-Acquired Infections,\u201d PLOS One, 2013. [PDF]<\/a><\/p>\n<\/li>\n V. Galluzzi, T. Herman, D. J. Shumaker, D. R. Macinga, J. W. Arbogast, E. M. Segre, A. M. Segre, and P. M. Polgreen, \u201cElectronic Recognition of Hand-Hygiene Technique and Duration,\u201d Infection Control and Hospital Epidemiology, vol. 35, iss. 10, pp. 1298-1300, 2014. [PDF]<\/a><\/p>\n<\/li>\n R. Butler, M. N. Monsalve, G. W. Thomas, T. Herman, A. M. Segre, P. M. Polgreen, and M. Suneja, \u201cEstimating Time Physicians and Other Healthcare Workers Spend with Patients in an Intensive Care Unit Using a Sensor Network,\u201d The American Journal of Medicine, 2018. [PDF]<\/a><\/p>\n<\/li>\n<\/ol>\n We build machine learning prediction models using detailed electronic medical record data overlaid with hospital architectural layout for predicting patient risk. In recent work [2] we use a 2-stage prediction model to identify latent C.diff infections (e.g., asymptomatic C.diff carriers). In [1], 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 M. N. Monsalve, S. V. Pemmaraju, S. Johnson, and P. M. Polgreen, \u201cImproving Risk Prediction of Clostridium Difficile Infection Using Temporal Event-Pairs,\u201d in 2015 International Conference on Healthcare Informatics, 2015, pp. 140-149. [PDF]<\/a><\/p>\n<\/li>\n \u201cepiDAMIK Workshop.\u201d Accessed August 24, 2023. [PDF]<\/a><\/p>\n<\/li>\n<\/ol>\n We study the use of social media \u2014 Twitter [1], Wikipedia [2], and Craig\u2019s list [3] \u2014 in helping with disease surveillance. We model the geographic placement of surveillance sites as an optimization problem [4] and propose methods for computing optimal screening rates in [5]. We also build apps for individual-level surveillance [6].<\/p>\n A. Signorini, A. M. Segre, and P. M. Polgreen, \u201cThe Use of Twitter to Track Levels of Disease Activity and Public Concern in the US During the Influenza A H1N1 Pandemic,\u201d PLOS One, 2011. [PDF]<\/a><\/p>\n<\/li>\n G. C. Fairchild, S. DelValle, L. DeSilva, and A. M. Segre, \u201cEliciting Disease Data From Wikipedia Articles,\u201d in ICWSM Workshop on Wikipedia Research Challenges and Opportunities, Stanford, CA, 2015.<\/p>\n<\/li>\n J. A. Fries, P. M. Polgreen, and A. M. Segre, \u201cMining the Demographics of Craigslist Casual Sex Ads to Inform Public Health Policy,\u201d in IEEE International Conference on Healthcare Informatics, Verona, Italy, 2014.<\/p>\n<\/li>\n G. C. Fairchild, P. M. Polgreen, E. Foster, G. Rushton, and A. M. Segre, \u201cHow Many Suffice? A Computational Framework for Sizing Sentinel Surveillance Networks,\u201d International Journal of Health Geographics, vol. 12, iss. 56, 2013. [PDF]<\/a><\/p>\n<\/li>\n A. C. Miller, L. A. Polgreen, and P. M. Polgreen, \u201cOptimal Screening Strategies for Healthcare Associated Infections in a Multi-Institutional Setting,\u201d PLOS Computational Biology, vol. 10, iss. 1, pp. 1-11, 2014. [PDF]<\/a><\/p>\n<\/li>\n A. C. Miller, I. Singh, E. Koehler, and P. M. Polgreen, \u201cA Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance,\u201d Clinical Infectious Diseases, vol. 67, iss. 3, pp. 388-397, 2018. [PDF]<\/a><\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":" 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 [1, 2, 3], the significance of spatiotemporal interactions on C.diff infections within a hospital [4],<\/p>\n
\nInferring agent-behavior in healthcare settings<\/h3>\n
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\nHAI risk prediction<\/h3>\n
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\nDisease-surveillance<\/h3>\n
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Inferring HAI characteristics<\/h3>\n