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Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing.

CITE Title : Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing. Personal Author(s) : Wheeler, Matthew W. Published Date : August 06 2018;3-2019; Source : Biometrics. 75(1):193-201 URL : https://stacks.cdc.gov/view/cdc/75828 Wheeler, Matthew W. (2018). Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing.. 75(1). Wheeler, Matthew W. "Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing." 75, no. 1 (2018) Wheeler, Matthew W. "Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing." vol. 75, no. 1, 2018 Export RIS Citation Information.

:

Wheeler, Matthew W.

August 06 2018 | Biometrics. 75(1):193-201

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Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are e...
to Health Care Resource Utilization: an Analytic Approach

CITE Title : to Health Care Resource Utilization: an Analytic Approach Personal Author(s) : Schiltz, Nicholas K.;Warner, David F.;Sun, Jiayang;Bakaki, Paul M.;Dor, Avi;Given, Charles W.;Stange, Kurt C.;Koroukian, Siran M.; Published Date : Mar 2017 Source : Med Care. 55(3):276-284. URL : https://stacks.cdc.gov/view/cdc/44253 Schiltz, Nicholas K. et al. (2017). to Health Care Resource Utilization: an Analytic Approach. 55(3). Schiltz, Nicholas K. et al. "to Health Care Resource Utilization: an Analytic Approach" 55, no. 3 (2017) Schiltz, Nicholas K. et al. "to Health Care Resource Utilization: an Analytic Approach" vol. 55, no. 3, 2017 Export RIS Citation Information.

:

Schiltz, Nicholas K. ;

Warner, David F.

Mar 2017 | Med Care. 55(3):276-284.

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BackgroundMultimorbidity affects the majority of elderly adults and is associated with higher health costs and utilization, but how specific patterns ...
Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles

CITE Title : Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles Personal Author(s) : Xu, Rong;Wang, QuanQiu; Published Date : Mar 27 2015 Source : J Biomed Inform. 55:64-72. URL : https://stacks.cdc.gov/view/cdc/34602 Xu, Rong and Wang, QuanQiu (2015). Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles. 55 Xu, Rong and Wang, QuanQiu "Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles" 55 (2015) Xu, Rong and Wang, QuanQiu "Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles" vol. 55, 2015 Export RIS Citation Information.

:

Xu, Rong ;

Wang, QuanQiu

Mar 27 2015 | J Biomed Inform. 55:64-72.

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Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these inno...
Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies

CITE Title : Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies Personal Author(s) : Lombard, Melissa A.;Bryan, Molly Scannell;Jones, Daniel K.;Bulka, Catherine;Bradley, Paul M.;Backer, Lorraine C.;Focazio, Michael J.;Silverman, Debra T.;Toccalino, Patricia;Argos, Maria;Gribble, Matthew O.;Ayotte, Joseph D.; Published Date : 4 20 2021 Source : Environ Sci Technol. 55(8):5012-5023 URL : https://stacks.cdc.gov/view/cdc/114592 Lombard, Melissa A. et al. (2021). Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies. 55(8). Lombard, Melissa A. et al. "Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies" 55, no. 8 (2021) Lombard, Melissa A. et al. "Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies" vol. 55, no. 8, 2021 Export RIS Citation Information.

:

Lombard, Melissa A. ;

Bryan, Molly Scannell

4 20 2021 | Environ Sci Technol. 55(8):5012-5023

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Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations excee...
Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer

CITE Title : Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer Personal Author(s) : McGarry, Sean D.;Hurrell, Sarah L.;Iczkowski, Kenneth A.;Hall, William;Kaczmarowski, Amy L.;Banerjee, Anjishnu;Keuter, Tucker;Jacobsohn, Kenneth;Bukowy, John D.;Nevalainen, Marja T.;Hohenwalter, Mark D.;See, William A.;LaViolette, Peter S.; Published Date : 8 01 2018 Source : Int J Radiat Oncol Biol Phys. 101(5):1179-1187 URL : https://stacks.cdc.gov/view/cdc/119161 McGarry, Sean D. et al. (2018). Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer. 101(5). McGarry, Sean D. et al. "Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer" 101, no. 5 (2018) McGarry, Sean D. et al. "Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer" vol. 101, no. 5, 2018 Export RIS Citation Information.

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McGarry, Sean D. ;

Hurrell, Sarah L.

8 01 2018 | Int J Radiat Oncol Biol Phys. 101(5):1179-1187

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Purpose:This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predicti...
Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

CITE Title : Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder Personal Author(s) : Maenner, Matthew J.;Yeargin-Allsopp, Marshalyn;Van Naarden Braun, Kim;Christensen, Deborah L.;Schieve, Laura A.; Published Date : Dec 21 2016 Source : PLoS One. 11(12). URL : https://stacks.cdc.gov/view/cdc/43675 Maenner, Matthew J. et al. (2016). Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder. 11(12). Maenner, Matthew J. et al. "Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder" 11, no. 12 (2016) Maenner, Matthew J. et al. "Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder" vol. 11, no. 12, 2016 Export RIS Citation Information.

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Maenner, Matthew J. ;

Yeargin-Allsopp, Marshalyn

Dec 21 2016 | PLoS One. 11(12).

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The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-y...
Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports

CITE Title : Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports Personal Author(s) : Pineda, Arturo López;Ye, Ye;Visweswaran, Shyam;Cooper, Gregory F.;Wagner, Michael M.;Tsui, Fuchiang (Rich); Published Date : 12 2015;12-2015; Source : J Biomed Inform. 58:60-69 URL : https://stacks.cdc.gov/view/cdc/36922 Pineda, Arturo López et al. (2015). Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports. 58 Pineda, Arturo López et al. "Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports" 58 (2015) Pineda, Arturo López et al. "Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports" vol. 58, 2015 Export RIS Citation Information.

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Pineda, Arturo López ;

Ye, Ye

12 2015 | J Biomed Inform. 58:60-69

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Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detecti...
Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records

CITE Title : Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records Personal Author(s) : Li, Qian;Yang, Xi;Xu, Jie;Guo, Yi;He, Xing;Hu, Hui;Lyu, Tianchen;Marra, David;Miller, Amber;Smith, Glenn;DeKosky, Steven;Boyce, Richard D.;Schliep, Karen;Shenkman, Elizabeth;Maraganore, Demetrius;Wu, Yonghui;Bian, Jiang; Published Date : 8 2023;8-2023; Source : Alzheimers Dement. 19(8):3506-3518 URL : https://stacks.cdc.gov/view/cdc/153596 Li, Qian et al. (2023). Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records. 19(8). Li, Qian et al. "Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records" 19, no. 8 (2023) Li, Qian et al. "Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records" vol. 19, no. 8, 2023 Export RIS Citation Information.

:

Li, Qian ;

Yang, Xi

8 2023 | Alzheimers Dement. 19(8):3506-3518

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Introduction:This study aims to explore machine learning (ML) methods for early prediction of Alzheimer’s disease (AD) and related dementias (ADRD) ...
Can Machine Learning Help Identify Patients at Risk for Recurrent Sexually Transmitted Infections?

CITE Title : Can Machine Learning Help Identify Patients at Risk for Recurrent Sexually Transmitted Infections? Personal Author(s) : Elder, Heather R.;Gruber, Susan;Willis, Sarah J.;Cocoros, Noelle;Callahan, Myfanwy;Flagg, Elaine W.;Klompas, Michael;Hsu, Katherine K.; Published Date : 1 2021;1-2021; Source : Sex Transm Dis. 48(1):56-62 URL : https://stacks.cdc.gov/view/cdc/151768 Elder, Heather R. et al. (2021). Can Machine Learning Help Identify Patients at Risk for Recurrent Sexually Transmitted Infections?. 48(1). Elder, Heather R. et al. "Can Machine Learning Help Identify Patients at Risk for Recurrent Sexually Transmitted Infections?" 48, no. 1 (2021) Elder, Heather R. et al. "Can Machine Learning Help Identify Patients at Risk for Recurrent Sexually Transmitted Infections?" vol. 48, no. 1, 2021 Export RIS Citation Information.

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Elder, Heather R. ;

Gruber, Susan

1 2021 | Sex Transm Dis. 48(1):56-62

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Background:A substantial fraction of sexually transmitted infections (STIs) occur in patients who have previously been treated for an STI. We assessed...
Predicting Probability of Perirectal Colonization with Carbapenem-Resistant Enterobacteriaceae (CRE) and other Carbapenem-Resistant Organisms (CROs) at Hospital Unit Admission

CITE Title : Predicting Probability of Perirectal Colonization with Carbapenem-Resistant Enterobacteriaceae (CRE) and other Carbapenem-Resistant Organisms (CROs) at Hospital Unit Admission Personal Author(s) : Goodman, Katherine E.;Simner, Patricia J.;Klein, Eili Y.;Kazmi, Abida Q.;Gadala, Avinash;Toerper, Matthew;Levin, Scott;Tamma, Pranita D.;Rock, Clare;Cosgrove, Sara E.;Maragakis, Lisa L.;Milstone, Aaron M.; Corporate Authors(s) : CDC Prevention Epicenters Program and the CDC MIND-Healthcare Program Published Date : March 27 2019 Source : Infect Control Hosp Epidemiol. 40(5):541-550 URL : https://stacks.cdc.gov/view/cdc/79705 Goodman, Katherine E. et al. (2019). Predicting Probability of Perirectal Colonization with Carbapenem-Resistant Enterobacteriaceae (CRE) and other Carbapenem-Resistant Organisms (CROs) at Hospital Unit Admission. 40(5). Goodman, Katherine E. et al. "Predicting Probability of Perirectal Colonization with Carbapenem-Resistant Enterobacteriaceae (CRE) and other Carbapenem-Resistant Organisms (CROs) at Hospital Unit Admission" 40, no. 5 (2019) Goodman, Katherine E. et al. "Predicting Probability of Perirectal Colonization with Carbapenem-Resistant Enterobacteriaceae (CRE) and other Carbapenem-Resistant Organisms (CROs) at Hospital Unit Admission" vol. 40, no. 5, 2019 Export RIS Citation Information.

:

Goodman, Katherine E. ;

Simner, Patricia J.

March 27 2019 | Infect Control Hosp Epidemiol. 40(5):541-550

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Background.Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-pro...
Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers

CITE Title : Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers Personal Author(s) : Hoshino, Ayuko;Kim, Han Sang;Bojmar, Linda;Gyan, Kofi Ennu;Cioffi, Michele;Hernandez, Jonathan;Zambirinis, Constantinos P.;Rodrigues, Gonçalo;Molina, Henrik;Heissel, Søren;Mark, Milica Tesic;Steiner, Loïc;Benito-Martin, Alberto;Lucotti, Serena;Giannatale, Angela Di;Offer, Katharine;Nakajima, Miho;Williams, Caitlin;Nogués, Laura;Vatter, Fanny A. Pelissier;Hashimoto, Ayako;Davies, Alexander E.;Freitas, Daniela;Kenific, Candia M.;Ararso, Yonathan;Buehring, Weston;Lauritzen, Pernille;Ogitani, Yusuke;Sugiura, Kei;Takahashi, Naoko;Alečković, Maša;Bailey, Kayleen A.;Jolissaint, Joshua S.;Wang, Huajuan;Harris, Ashton;Schaeffer, L. Miles;García-Santos, Guillermo;Posner, Zoe;Balachandran, Vinod P.;Khakoo, Yasmin;Raju, G. Praveen;Scherz, Avigdor;Sagi, Irit;Scherz-Shouval, Ruth;Yarden, Yosef;Oren, Moshe;Malladi, Mahathi;Petriccione, Mary;De Braganca, Kevin C.;Donzelli, Maria;Fischer, Cheryl;Vitolano, Stephanie;Wright, Geraldine P.;Ganshaw, Lee;Marrano, Mariel;Ahmed, Amina;DeStefano, Joe;Danzer, Enrico;Roehrl, Michael H.A.;Lacayo, Norman J.;Vincent, C. Theresa;Weiser, Martin R.;Brady, Mary S.;Meyers, Paul A.;Wexler, Leonard H.;Ambati, Srikanth R.;Chou, Alexander J.;Slotkin, Emily K.;Modak, Shakeel;Roberts, Stephen S.;Basu, Ellen M.;Diolaiti, Daniel;Krantz, Benjamin A.;Cardoso, Fatima;Simpson, Amber L.;Berger, Michael;Rudin, Charles M.;Simeone, Diane M.;Jain, Maneesh;Ghajar, Cyrus M.;Batra, Surinder K.;Stanger, Ben Z.;Bui, Jack;Brown, Kristy A.;Rajasekhar, Vinagolu K.;Healey, John H.;de Sousa, Maria;Kramer, Kim;Sheth, Sujit;Baisch, Jeanine;Pascual, Virginia;Heaton, Todd E.;Quaglia, Michael P. La;Pisapia, David J.;Schwartz, Robert;Zhang, Haiying;Liu, Yuan;Shukla, Arti;Blavier, Laurence;DeClerck, Yves A.;LaBarge, Mark;Bissell, Mina J.;Caffrey, Thomas C.;Grandgenett, Paul M.;Hollingsworth, Michael A.;Bromberg, Jacqueline;Costa-Silva, Bruno;Peinado, Hector;Kang, Yibin;Garcia, Benjamin A.;O’Reilly, Eileen M.;Kelsen, David;Trippett, Tanya M.;Jones, David R.;Matei, Irina R.;Jarnagin, William R.;Lyden, David; Published Date : 8 20 2020 Source : Cell. 182(4):1044-1061.e18 URL : https://stacks.cdc.gov/view/cdc/94603 Hoshino, Ayuko et al. (2020). Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers. 182(4). Hoshino, Ayuko et al. "Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers" 182, no. 4 (2020) Hoshino, Ayuko et al. "Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers" vol. 182, no. 4, 2020 Export RIS Citation Information.

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Hoshino, Ayuko ;

Kim, Han Sang

8 20 2020 | Cell. 182(4):1044-1061.e18

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There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellul...
Machine Learning in Medicine

CITE Title : Machine Learning in Medicine Personal Author(s) : Deo, Rahul C. Published Date : Nov 17 2015 Source : Circulation. 132(20):1920-1930. URL : https://stacks.cdc.gov/view/cdc/52154 Deo, Rahul C. (2015). Machine Learning in Medicine. 132(20). Deo, Rahul C. "Machine Learning in Medicine" 132, no. 20 (2015) Deo, Rahul C. "Machine Learning in Medicine" vol. 132, no. 20, 2015 Export RIS Citation Information.

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Deo, Rahul C.

Nov 17 2015 | Circulation. 132(20):1920-1930.

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Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly comple...
Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data

CITE Title : Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data Personal Author(s) : Chen, Wei;Wheeler, Krista K;Lin, Simon;Huang, Yungui;Xiang, Huiyun; Published Date : Feb 3 2016 Source : Accid Anal Prev. 89:111-117. URL : https://stacks.cdc.gov/view/cdc/42735 Chen, Wei et al. (2016). Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data. 89 Chen, Wei et al. "Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data" 89 (2016) Chen, Wei et al. "Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data" vol. 89, 2016 Export RIS Citation Information.

:

Chen, Wei ;

Wheeler, Krista K

Feb 3 2016 | Accid Anal Prev. 89:111-117.

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One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. ...
Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.

CITE Title : Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. Personal Author(s) : Reich, Nicholas G.;McGowan, Craig J.;Yamana, Teresa K.;Tushar, Abhinav;Ray, Evan L.;Osthus, Dave;Kandula, Sasikiran;Brooks, Logan C.;Crawford-Crudell, Willow;Gibson, Graham Casey;Moore, Evan;Silva, Rebecca;Biggerstaff, Matthew;Johansson, Michael A.;Rosenfeld, Roni;Shaman, Jeffrey; Published Date : November 22 2019 Source : PLoS Comput Biol. 2019; 15(11) URL : https://stacks.cdc.gov/view/cdc/89377 Reich, Nicholas G. et al. (2019). Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.. 15(11). Reich, Nicholas G. et al. "Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S." 15, no. 11 (2019) Reich, Nicholas G. et al. "Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S." vol. 15, no. 11, 2019 Export RIS Citation Information.

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Reich, Nicholas G. ;

McGowan, Craig J.

November 22 2019 | PLoS Comput Biol. 2019; 15(11)

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Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of inf...
Exploratory analysis of machine learning approaches for surveillance of Zika-associated birth defects

CITE Title : Exploratory analysis of machine learning approaches for surveillance of Zika-associated birth defects Personal Author(s) : Lusk, Richard;Zimmerman, John;VanMaldeghem, Kelley;Kim, Suzanna;Roth, Nicole M.;Lavinder, James;Fulton, Anna;Raycraft, Meghan;Ellington, Sascha R.;Galang, Romeo R.; Published Date : 11 2020 Source : Birth Defects Res. 112(18):1450-1460 URL : https://stacks.cdc.gov/view/cdc/105376 Lusk, Richard et al. (2020). Exploratory analysis of machine learning approaches for surveillance of Zika-associated birth defects. 112(18). Lusk, Richard et al. "Exploratory analysis of machine learning approaches for surveillance of Zika-associated birth defects" 112, no. 18 (2020) Lusk, Richard et al. "Exploratory analysis of machine learning approaches for surveillance of Zika-associated birth defects" vol. 112, no. 18, 2020 Export RIS Citation Information.

:

Lusk, Richard ;

Zimmerman, John

11 2020 | Birth Defects Res. 112(18):1450-1460

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In 2016, Centers for Disease Control and Prevention (CDC) established surveillance of pregnant women with Zika virus infection and their infants in th...
Role of Artificial Intelligence (AI) and Machine Learning (ML) in NCHS Programs

CITE Title : Role of Artificial Intelligence (AI) and Machine Learning (ML) in NCHS Programs Personal Author(s) : Hoppe, Travis;Rogers, Ben; Corporate Authors(s) : National Center for Health Statistics (U.S.) Conference Author(s) : National Center for Health Statistics (U.S.). Board of Scientific Counselors. Meeting (2022 Sept 14) Published Date : September 14, 2023 Pages in Document : 24 unnumbered slides URL : https://stacks.cdc.gov/view/cdc/133663 Hoppe, Travis and Rogers, Ben (2023). Role of Artificial Intelligence (AI) and Machine Learning (ML) in NCHS Programs. Hoppe, Travis and Rogers, Ben "Role of Artificial Intelligence (AI) and Machine Learning (ML) in NCHS Programs" (2023) Hoppe, Travis and Rogers, Ben "Role of Artificial Intelligence (AI) and Machine Learning (ML) in NCHS Programs" , 2023 Export RIS Citation Information.

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Hoppe, Travis ;

Rogers, Ben

September 14, 2023

:

bsc-pres-Travis-Hoppe-and-Ben-Rogers-9-14-2023.pdf
Redesigning Implementation Measurement for Monitoring and Quality Improvement in Community Delivery Settings

CITE Title : Redesigning Implementation Measurement for Monitoring and Quality Improvement in Community Delivery Settings Personal Author(s) : Berkel, Cady;Gallo, Carlos G.;Sandler, Irwin N.;Mauricio, Anne M.;Smith, Justin D.;Brown, C. Hendricks; Published Date : 2 2019 Source : J Prim Prev. 40(1):111-127 URL : https://stacks.cdc.gov/view/cdc/76381 Berkel, Cady et al. (2019). Redesigning Implementation Measurement for Monitoring and Quality Improvement in Community Delivery Settings. 40(1). Berkel, Cady et al. "Redesigning Implementation Measurement for Monitoring and Quality Improvement in Community Delivery Settings" 40, no. 1 (2019) Berkel, Cady et al. "Redesigning Implementation Measurement for Monitoring and Quality Improvement in Community Delivery Settings" vol. 40, no. 1, 2019 Export RIS Citation Information.

:

Berkel, Cady ;

Gallo, Carlos G.

2 2019 | J Prim Prev. 40(1):111-127

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The field of prevention has established the potential to promote child adjustment across a wide array of outcomes. However, when evidence-based preven...
Estimating Occupation-Related Crashes in Light and Medium Size Vehicles in Kentucky: A Text Mining and Data Linkage Approach

CITE Title : Estimating Occupation-Related Crashes in Light and Medium Size Vehicles in Kentucky: A Text Mining and Data Linkage Approach Personal Author(s) : Northcutt, Caitlin A.;Stamatiadis, Nikiforos;Fields, Michael A.;Souleyrette, Reginald; Published Date : 11 2024;11-2024; Source : Accid Anal Prev. 207:107749 URL : https://stacks.cdc.gov/view/cdc/174557 Northcutt, Caitlin A. et al. (2024). Estimating Occupation-Related Crashes in Light and Medium Size Vehicles in Kentucky: A Text Mining and Data Linkage Approach. 207 Northcutt, Caitlin A. et al. "Estimating Occupation-Related Crashes in Light and Medium Size Vehicles in Kentucky: A Text Mining and Data Linkage Approach" 207 (2024) Northcutt, Caitlin A. et al. "Estimating Occupation-Related Crashes in Light and Medium Size Vehicles in Kentucky: A Text Mining and Data Linkage Approach" vol. 207, 2024 Export RIS Citation Information.

:

Northcutt, Caitlin A. ;

Stamatiadis, Nikiforos

11 2024 | Accid Anal Prev. 207:107749

:

Occupational motor vehicle (OMV) crashes are a leading cause of occupation-related injury and fatality in the United States. Statewide crash databases...

Public Access Available on:

November 01, 2025
Disparities in Adherence and Emergency Department Utilization Among People with Epilepsy: A Machine Learning Approach

CITE Title : Disparities in Adherence and Emergency Department Utilization Among People with Epilepsy: A Machine Learning Approach Personal Author(s) : Bensken, Wyatt P.;Vaca, Guadalupe Fernandez-Baca;Williams, Scott M.;Khan, Omar I.;Jobst, Barbara C.;Stange, Kurt C.;Sajatovic, Martha;Koroukian, Siran M.; Published Date : 8 2023;8-2023; Source : Seizure. 110:169-176 URL : https://stacks.cdc.gov/view/cdc/133515 Bensken, Wyatt P. et al. (2023). Disparities in Adherence and Emergency Department Utilization Among People with Epilepsy: A Machine Learning Approach. 110 Bensken, Wyatt P. et al. "Disparities in Adherence and Emergency Department Utilization Among People with Epilepsy: A Machine Learning Approach" 110 (2023) Bensken, Wyatt P. et al. "Disparities in Adherence and Emergency Department Utilization Among People with Epilepsy: A Machine Learning Approach" vol. 110, 2023 Export RIS Citation Information.

:

Bensken, Wyatt P. ;

Vaca, Guadalupe Fernandez-Baca

8 2023 | Seizure. 110:169-176

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Purpose:We used a machine learning approach to identify the combinations of factors that contribute to lower adherence and high emergency department (...
Modeling transmission of pathogens in healthcare settings

CITE Title : Modeling transmission of pathogens in healthcare settings Personal Author(s) : Stachel, Anna;Keegan, Lindsay T.;Blumberg, Seth; Published Date : 8 01 2021;8-01-; Source : Curr Opin Infect Dis. 34(4):333-338 URL : https://stacks.cdc.gov/view/cdc/120528 Stachel, Anna et al. (2021). Modeling transmission of pathogens in healthcare settings. 34(4). Stachel, Anna and Keegan, Lindsay T. and Blumberg, Seth "Modeling transmission of pathogens in healthcare settings" 34, no. 4 (2021) Stachel, Anna et al. "Modeling transmission of pathogens in healthcare settings" vol. 34, no. 4, 2021 Export RIS Citation Information.

:

Stachel, Anna ;

Keegan, Lindsay T.

8 01 2021 | Curr Opin Infect Dis. 34(4):333-338

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Purpose of reviewMathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcar...

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