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Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.

JMIR medical informatics

Reps JM, Kim C, Williams RD, Markus AF, Yang C, Duarte-Salles T, Falconer T, Jonnagaddala J, Williams A, Fernández-Bertolín S, DuVall SL, Kostka K, Rao G, Shoaibi A, Ostropolets A, Spotnitz ME, Zhang L, Casajust P, Steyerberg EW, Nyberg F, Kaas-Hansen BS, Choi YH, Morales D, Liaw ST, Abrahão MTF, Areia C, Matheny ME, Lynch KE, Aragón M, Park RW, Hripcsak G, Reich CG, Suchard MA, You SC, Ryan PB, Prieto-Alhambra D, Rijnbeek PR.
PMID: 33661754
JMIR Med Inform. 2021 Apr 05;9(4):e21547. doi: 10.2196/21547.

BACKGROUND: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index,...

Cite
Reps JM, Kim C, Williams RD, et al. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR Med Inform. 2021;9(4):e21547doi: 10.2196/21547.
Reps, J. M., Kim, C., Williams, R. D., Markus, A. F., Yang, C., Duarte-Salles, T., Falconer, T., Jonnagaddala, J., Williams, A., Fernández-Bertolín, S., DuVall, S. L., Kostka, K., Rao, G., Shoaibi, A., Ostropolets, A., Spotnitz, M. E., Zhang, L., Casajust, P., Steyerberg, E. W., Nyberg, F., Kaas-Hansen, B. S., Choi, Y. H., Morales, D., Liaw, S. T., Abrahão, M. T. F., Areia, C., Matheny, M. E., Lynch, K. E., Aragón, M., Park, R. W., Hripcsak, G., Reich, C. G., Suchard, M. A., You, S. C., Ryan, P. B., Prieto-Alhambra, D., & Rijnbeek, P. R. (2021). Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR medical informatics, 9(4), e21547. https://doi.org/10.2196/21547
Reps, Jenna M, et al. "Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study." JMIR medical informatics vol. 9,4 (2021): e21547. doi: https://doi.org/10.2196/21547
Reps JM, Kim C, Williams RD, Markus AF, Yang C, Duarte-Salles T, Falconer T, Jonnagaddala J, Williams A, Fernández-Bertolín S, DuVall SL, Kostka K, Rao G, Shoaibi A, Ostropolets A, Spotnitz ME, Zhang L, Casajust P, Steyerberg EW, Nyberg F, Kaas-Hansen BS, Choi YH, Morales D, Liaw ST, Abrahão MTF, Areia C, Matheny ME, Lynch KE, Aragón M, Park RW, Hripcsak G, Reich CG, Suchard MA, You SC, Ryan PB, Prieto-Alhambra D, Rijnbeek PR. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR Med Inform. 2021 Apr 05;9(4):e21547. doi: 10.2196/21547. PMID: 33661754; PMCID: PMC8023380.
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Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.

JMIR medical informatics

Reps JM, Kim C, Williams RD, Markus AF, Yang C, Duarte-Salles T, Falconer T, Jonnagaddala J, Williams A, Fernández-Bertolín S, DuVall SL, Kostka K, Rao G, Shoaibi A, Ostropolets A, Spotnitz ME, Zhang L, Casajust P, Steyerberg EW, Nyberg F, Kaas-Hansen BS, Choi YH, Morales D, Liaw ST, Abrahão MTF, Areia C, Matheny ME, Lynch KE, Aragón M, Park RW, Hripcsak G, Reich CG, Suchard MA, You SC, Ryan PB, Prieto-Alhambra D, Rijnbeek PR.
PMID: 33661754
JMIR Med Inform. 2021 Apr 05;9(4):e21547. doi: 10.2196/21547.

BACKGROUND: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index,...

Cite
Reps JM, Kim C, Williams RD, et al. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR Med Inform. 2021;9(4):e21547doi: 10.2196/21547.
Reps, J. M., Kim, C., Williams, R. D., Markus, A. F., Yang, C., Duarte-Salles, T., Falconer, T., Jonnagaddala, J., Williams, A., Fernández-Bertolín, S., DuVall, S. L., Kostka, K., Rao, G., Shoaibi, A., Ostropolets, A., Spotnitz, M. E., Zhang, L., Casajust, P., Steyerberg, E. W., Nyberg, F., Kaas-Hansen, B. S., Choi, Y. H., Morales, D., Liaw, S. T., Abrahão, M. T. F., Areia, C., Matheny, M. E., Lynch, K. E., Aragón, M., Park, R. W., Hripcsak, G., Reich, C. G., Suchard, M. A., You, S. C., Ryan, P. B., Prieto-Alhambra, D., & Rijnbeek, P. R. (2021). Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR medical informatics, 9(4), e21547. https://doi.org/10.2196/21547
Reps, Jenna M, et al. "Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study." JMIR medical informatics vol. 9,4 (2021): e21547. doi: https://doi.org/10.2196/21547
Reps JM, Kim C, Williams RD, Markus AF, Yang C, Duarte-Salles T, Falconer T, Jonnagaddala J, Williams A, Fernández-Bertolín S, DuVall SL, Kostka K, Rao G, Shoaibi A, Ostropolets A, Spotnitz ME, Zhang L, Casajust P, Steyerberg EW, Nyberg F, Kaas-Hansen BS, Choi YH, Morales D, Liaw ST, Abrahão MTF, Areia C, Matheny ME, Lynch KE, Aragón M, Park RW, Hripcsak G, Reich CG, Suchard MA, You SC, Ryan PB, Prieto-Alhambra D, Rijnbeek PR. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR Med Inform. 2021 Apr 05;9(4):e21547. doi: 10.2196/21547. PMID: 33661754; PMCID: PMC8023380.
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Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States.

medRxiv : the preprint server for health sciences

Burn E, Sena AG, Prats-Uribe A, Spotnitz M, DuVall S, Lynch KE, Matheny ME, Nyberg F, Ahmed WU, Alser O, Alghoul H, Alshammari T, Zhang L, Casajust P, Areia C, Shah K, Reich C, Blacketer C, Andryc A, Fortin S, Natarajan K, Gong M, Golozar A, Morales D, Rijnbeek P, Subbian V, Roel E, Recalde M, Lane JCE, Vizcaya D, Posada JD, Shah NH, Jonnagaddala J, Lai LYH, Avilés-Jurado FX, Hripcsak G, Suchard MA, Ranzani OT, Ryan P, Prieto-Alhambra D, Kostka K, Duarte-Salles T.
PMID: 33269356
medRxiv. 2021 Feb 12; doi: 10.1101/2020.11.25.20229088.

OBJECTIVE: To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO).DESIGN: A network cohort study.SETTING: Seven databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Hospital CDM, IQVIA...

Cite
Burn E, Sena AG, Prats-Uribe A, et al. Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States. medRxiv. 2021;doi: 10.1101/2020.11.25.20229088.
Burn, E., Sena, A. G., Prats-Uribe, A., Spotnitz, M., DuVall, S., Lynch, K. E., Matheny, M. E., Nyberg, F., Ahmed, W. U., Alser, O., Alghoul, H., Alshammari, T., Zhang, L., Casajust, P., Areia, C., Shah, K., Reich, C., Blacketer, C., Andryc, A., Fortin, S., Natarajan, K., Gong, M., Golozar, A., Morales, D., Rijnbeek, P., Subbian, V., Roel, E., Recalde, M., Lane, J. C. E., Vizcaya, D., Posada, J. D., Shah, N. H., Jonnagaddala, J., Lai, L. Y. H., Avilés-Jurado, F. X., Hripcsak, G., Suchard, M. A., Ranzani, O. T., Ryan, P., Prieto-Alhambra, D., Kostka, K., & Duarte-Salles, T. (2021). Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.11.25.20229088
Burn, Edward, et al. "Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States." medRxiv : the preprint server for health sciences vol. (2021). doi: https://doi.org/10.1101/2020.11.25.20229088
Burn E, Sena AG, Prats-Uribe A, Spotnitz M, DuVall S, Lynch KE, Matheny ME, Nyberg F, Ahmed WU, Alser O, Alghoul H, Alshammari T, Zhang L, Casajust P, Areia C, Shah K, Reich C, Blacketer C, Andryc A, Fortin S, Natarajan K, Gong M, Golozar A, Morales D, Rijnbeek P, Subbian V, Roel E, Recalde M, Lane JCE, Vizcaya D, Posada JD, Shah NH, Jonnagaddala J, Lai LYH, Avilés-Jurado FX, Hripcsak G, Suchard MA, Ranzani OT, Ryan P, Prieto-Alhambra D, Kostka K, Duarte-Salles T. Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States. medRxiv. 2021 Feb 12; doi: 10.1101/2020.11.25.20229088. PMID: 33269356; PMCID: PMC7709172.
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Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks.

Medical image analysis

Yao J, Zhu X, Jonnagaddala J, Hawkins N, Huang J.
PMID: 32739769
Med Image Anal. 2020 Oct;65:101789. doi: 10.1016/j.media.2020.101789. Epub 2020 Jul 19.

Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are...

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Yao J, Zhu X, Jonnagaddala J, et al. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med Image Anal. 2020;65:101789doi: 10.1016/j.media.2020.101789.
Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., & Huang, J. (2020). Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical image analysis, 65101789. https://doi.org/10.1016/j.media.2020.101789
Yao, Jiawen, et al. "Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks." Medical image analysis vol. 65 (2020): 101789. doi: https://doi.org/10.1016/j.media.2020.101789
Yao J, Zhu X, Jonnagaddala J, Hawkins N, Huang J. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med Image Anal. 2020 Oct;65:101789. doi: 10.1016/j.media.2020.101789. Epub 2020 Jul 19. PMID: 32739769.
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The OpenDeID corpus for patient de-identification.

Scientific reports

Jonnagaddala J, Chen A, Batongbacal S, Nekkantti C.
PMID: 34620985
Sci Rep. 2021 Oct 07;11(1):19973. doi: 10.1038/s41598-021-99554-9.

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health...

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Jonnagaddala J, Chen A, Batongbacal S, et al. The OpenDeID corpus for patient de-identification. Sci Rep. 2021;11(1):19973doi: 10.1038/s41598-021-99554-9.
Jonnagaddala, J., Chen, A., Batongbacal, S., & Nekkantti, C. (2021). The OpenDeID corpus for patient de-identification. Scientific reports, 11(1), 19973. https://doi.org/10.1038/s41598-021-99554-9
Jonnagaddala, Jitendra, et al. "The OpenDeID corpus for patient de-identification." Scientific reports vol. 11,1 (2021): 19973. doi: https://doi.org/10.1038/s41598-021-99554-9
Jonnagaddala J, Chen A, Batongbacal S, Nekkantti C. The OpenDeID corpus for patient de-identification. Sci Rep. 2021 Oct 07;11(1):19973. doi: 10.1038/s41598-021-99554-9. PMID: 34620985; PMCID: PMC8497517.
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The OpenDeID corpus for patient de-identification.

Scientific reports

Jonnagaddala J, Chen A, Batongbacal S, Nekkantti C.
PMID: 34620985
Sci Rep. 2021 Oct 07;11(1):19973. doi: 10.1038/s41598-021-99554-9.

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health...

Cite
Jonnagaddala J, Chen A, Batongbacal S, et al. The OpenDeID corpus for patient de-identification. Sci Rep. 2021;11(1):19973doi: 10.1038/s41598-021-99554-9.
Jonnagaddala, J., Chen, A., Batongbacal, S., & Nekkantti, C. (2021). The OpenDeID corpus for patient de-identification. Scientific reports, 11(1), 19973. https://doi.org/10.1038/s41598-021-99554-9
Jonnagaddala, Jitendra, et al. "The OpenDeID corpus for patient de-identification." Scientific reports vol. 11,1 (2021): 19973. doi: https://doi.org/10.1038/s41598-021-99554-9
Jonnagaddala J, Chen A, Batongbacal S, Nekkantti C. The OpenDeID corpus for patient de-identification. Sci Rep. 2021 Oct 07;11(1):19973. doi: 10.1038/s41598-021-99554-9. PMID: 34620985; PMCID: PMC8497517.
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Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis.

medRxiv : the preprint server for health sciences

Tan EH, Sena AG, Prats-Uribe A, You SC, Ahmed WU, Kostka K, Reich C, Duvall SL, Lynch KE, Matheny ME, Duarte-Salles T, Bertolin SF, Hripcsak G, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Blacketer C, Alshammari TM, Alghoul H, Alser O, Lane JCE, Dawoud DM, Shah K, Yang Y, Zhang L, Areia C, Golozar A, Relcade M, Casajust P, Jonnagaddala J, Subbian V, Vizcaya D, Lai LY, Nyberg F, Morales DR, Posada JD, Shah NH, Gong M, Vivekanantham A, Abend A, Minty EP, Suchard M, Rijnbeek P, Ryan PB, Prieto-Alhambra D.
PMID: 33269355
medRxiv. 2020 Nov 27; doi: 10.1101/2020.11.24.20236802.

OBJECTIVE: Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after...

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Tan EH, Sena AG, Prats-Uribe A, et al. Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis. medRxiv. 2020;doi: 10.1101/2020.11.24.20236802.
Tan, E. H., Sena, A. G., Prats-Uribe, A., You, S. C., Ahmed, W. U., Kostka, K., Reich, C., Duvall, S. L., Lynch, K. E., Matheny, M. E., Duarte-Salles, T., Bertolin, S. F., Hripcsak, G., Natarajan, K., Falconer, T., Spotnitz, M., Ostropolets, A., Blacketer, C., Alshammari, T. M., Alghoul, H., Alser, O., Lane, J. C. E., Dawoud, D. M., Shah, K., Yang, Y., Zhang, L., Areia, C., Golozar, A., Relcade, M., Casajust, P., Jonnagaddala, J., Subbian, V., Vizcaya, D., Lai, L. Y., Nyberg, F., Morales, D. R., Posada, J. D., Shah, N. H., Gong, M., Vivekanantham, A., Abend, A., Minty, E. P., Suchard, M., Rijnbeek, P., Ryan, P. B., & Prieto-Alhambra, D. (2020). Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.11.24.20236802
Tan, Eng Hooi, et al. "Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis." medRxiv : the preprint server for health sciences vol. (2020). doi: https://doi.org/10.1101/2020.11.24.20236802
Tan EH, Sena AG, Prats-Uribe A, You SC, Ahmed WU, Kostka K, Reich C, Duvall SL, Lynch KE, Matheny ME, Duarte-Salles T, Bertolin SF, Hripcsak G, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Blacketer C, Alshammari TM, Alghoul H, Alser O, Lane JCE, Dawoud DM, Shah K, Yang Y, Zhang L, Areia C, Golozar A, Relcade M, Casajust P, Jonnagaddala J, Subbian V, Vizcaya D, Lai LY, Nyberg F, Morales DR, Posada JD, Shah NH, Gong M, Vivekanantham A, Abend A, Minty EP, Suchard M, Rijnbeek P, Ryan PB, Prieto-Alhambra D. Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis. medRxiv. 2020 Nov 27; doi: 10.1101/2020.11.24.20236802. PMID: 33269355; PMCID: PMC7709171.
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Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States.

medRxiv : the preprint server for health sciences

Golozar A, Lai LY, Sena AG, Vizcaya D, Schilling LM, Huser V, Nyberg F, Duvall SL, Morales DR, Alshammari TM, Abedtash H, Ahmed WU, Alser O, Alghoul H, Zhang Y, Gong M, Guan Y, Areia C, Jonnagaddala J, Shah K, Lane JCE, Prats-Uribe A, Posada JD, Shah NH, Subbian V, Zhang L, Fernandes Abrahão MT, Rijnbeek PR, You SC, Casajust P, Roel E, Recalde M, Fernández-Bertolín S, Andryc A, Thomas JA, Wilcox AB, Fortin S, Blacketer C, DeFalco F, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Hripcsak G, Suchard M, Lynch KE, Matheny ME, Williams A, Reich C, Duarte-Salles T, Kostka K, Ryan PB, Prieto-Alhambra D.
PMID: 33140068
medRxiv. 2020 Oct 27; doi: 10.1101/2020.10.25.20218875.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons...

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Golozar A, Lai LY, Sena AG, et al. Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States. medRxiv. 2020;doi: 10.1101/2020.10.25.20218875.
Golozar, A., Lai, L. Y., Sena, A. G., Vizcaya, D., Schilling, L. M., Huser, V., Nyberg, F., Duvall, S. L., Morales, D. R., Alshammari, T. M., Abedtash, H., Ahmed, W. U., Alser, O., Alghoul, H., Zhang, Y., Gong, M., Guan, Y., Areia, C., Jonnagaddala, J., Shah, K., Lane, J. C. E., Prats-Uribe, A., Posada, J. D., Shah, N. H., Subbian, V., Zhang, L., Fernandes Abrahão, M. T., Rijnbeek, P. R., You, S. C., Casajust, P., Roel, E., Recalde, M., Fernández-Bertolín, S., Andryc, A., Thomas, J. A., Wilcox, A. B., Fortin, S., Blacketer, C., DeFalco, F., Natarajan, K., Falconer, T., Spotnitz, M., Ostropolets, A., Hripcsak, G., Suchard, M., Lynch, K. E., Matheny, M. E., Williams, A., Reich, C., Duarte-Salles, T., Kostka, K., Ryan, P. B., & Prieto-Alhambra, D. (2020). Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.10.25.20218875
Golozar, Asieh, et al. "Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States." medRxiv : the preprint server for health sciences vol. (2020). doi: https://doi.org/10.1101/2020.10.25.20218875
Golozar A, Lai LY, Sena AG, Vizcaya D, Schilling LM, Huser V, Nyberg F, Duvall SL, Morales DR, Alshammari TM, Abedtash H, Ahmed WU, Alser O, Alghoul H, Zhang Y, Gong M, Guan Y, Areia C, Jonnagaddala J, Shah K, Lane JCE, Prats-Uribe A, Posada JD, Shah NH, Subbian V, Zhang L, Fernandes Abrahão MT, Rijnbeek PR, You SC, Casajust P, Roel E, Recalde M, Fernández-Bertolín S, Andryc A, Thomas JA, Wilcox AB, Fortin S, Blacketer C, DeFalco F, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Hripcsak G, Suchard M, Lynch KE, Matheny ME, Williams A, Reich C, Duarte-Salles T, Kostka K, Ryan PB, Prieto-Alhambra D. Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States. medRxiv. 2020 Oct 27; doi: 10.1101/2020.10.25.20218875. PMID: 33140068; PMCID: PMC7605581.
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Characterizing database granularity using SNOMED-CT hierarchy.

AMIA ... Annual Symposium proceedings. AMIA Symposium

Ostropolets A, Reich C, Ryan P, Weng C, Molinaro A, DeFalco F, Jonnagaddala J, Liaw ST, Jeon H, Park RW, Spotnitz ME, Natarajan K, Argyriou G, Kostka K, Miller R, Williams A, Minty E, Posada J, Hripcsak G.
PMID: 33936474
AMIA Annu Symp Proc. 2021 Jan 25;2020:983-992. eCollection 2020.

Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers...

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Ostropolets A, Reich C, Ryan P, et al. Characterizing database granularity using SNOMED-CT hierarchy. AMIA Annu Symp Proc. 2021;2020:983-992
Ostropolets, A., Reich, C., Ryan, P., Weng, C., Molinaro, A., DeFalco, F., Jonnagaddala, J., Liaw, S. T., Jeon, H., Park, R. W., Spotnitz, M. E., Natarajan, K., Argyriou, G., Kostka, K., Miller, R., Williams, A., Minty, E., Posada, J., & Hripcsak, G. (2020). Characterizing database granularity using SNOMED-CT hierarchy. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020983-992.
Ostropolets, Anna, et al. "Characterizing database granularity using SNOMED-CT hierarchy." AMIA ... Annual Symposium proceedings. AMIA Symposium vol. 2020 (2020): 983-992.
Ostropolets A, Reich C, Ryan P, Weng C, Molinaro A, DeFalco F, Jonnagaddala J, Liaw ST, Jeon H, Park RW, Spotnitz ME, Natarajan K, Argyriou G, Kostka K, Miller R, Williams A, Minty E, Posada J, Hripcsak G. Characterizing database granularity using SNOMED-CT hierarchy. AMIA Annu Symp Proc. 2021 Jan 25;2020:983-992. eCollection 2020. PMID: 33936474; PMCID: PMC8075504.
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Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

NPJ digital medicine

Sieberts SK, Schaff J, Duda M, Pataki BÁ, Sun M, Snyder P, Daneault JF, Parisi F, Costante G, Rubin U, Banda P, Chae Y, Chaibub Neto E, Dorsey ER, Aydın Z, Chen A, Elo LL, Espino C, Glaab E, Goan E, Golabchi FN, Görmez Y, Jaakkola MK, Jonnagaddala J, Klén R, Li D, McDaniel C, Perrin D, Perumal TM, Rad NM, Rainaldi E, Sapienza S, Schwab P, Shokhirev N, Venäläinen MS, Vergara-Diaz G, Zhang Y, Wang Y, Guan Y, Brunner D, Bonato P, Mangravite LM, Omberg L.
PMID: 33742069
NPJ Digit Med. 2021 Mar 19;4(1):53. doi: 10.1038/s41746-021-00414-7.

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated...

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Sieberts SK, Schaff J, Duda M, et al. Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge. NPJ Digit Med. 2021;4(1):53doi: 10.1038/s41746-021-00414-7.
Sieberts, S. K., Schaff, J., Duda, M., Pataki, B. �. �., Sun, M., Snyder, P., Daneault, J. F., Parisi, F., Costante, G., Rubin, U., Banda, P., Chae, Y., Chaibub Neto, E., Dorsey, E. R., Aydın, Z., Chen, A., Elo, L. L., Espino, C., Glaab, E., Goan, E., Golabchi, F. N., Görmez, Y., Jaakkola, M. K., Jonnagaddala, J., Klén, R., Li, D., McDaniel, C., Perrin, D., Perumal, T. M., Rad, N. M., Rainaldi, E., Sapienza, S., Schwab, P., Shokhirev, N., Venäläinen, M. S., Vergara-Diaz, G., Zhang, Y., Wang, Y., Guan, Y., Brunner, D., Bonato, P., Mangravite, L. M., & Omberg, L. (2021). Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge. NPJ digital medicine, 4(1), 53. https://doi.org/10.1038/s41746-021-00414-7
Sieberts, Solveig K, et al. "Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge." NPJ digital medicine vol. 4,1 (2021): 53. doi: https://doi.org/10.1038/s41746-021-00414-7
Sieberts SK, Schaff J, Duda M, Pataki BÁ, Sun M, Snyder P, Daneault JF, Parisi F, Costante G, Rubin U, Banda P, Chae Y, Chaibub Neto E, Dorsey ER, Aydın Z, Chen A, Elo LL, Espino C, Glaab E, Goan E, Golabchi FN, Görmez Y, Jaakkola MK, Jonnagaddala J, Klén R, Li D, McDaniel C, Perrin D, Perumal TM, Rad NM, Rainaldi E, Sapienza S, Schwab P, Shokhirev N, Venäläinen MS, Vergara-Diaz G, Zhang Y, Wang Y, Guan Y, Brunner D, Bonato P, Mangravite LM, Omberg L. Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge. NPJ Digit Med. 2021 Mar 19;4(1):53. doi: 10.1038/s41746-021-00414-7. PMID: 33742069; PMCID: PMC7979931.
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MET network in PubMed: a text-mined network visualization and curation system.

Database : the journal of biological databases and curation

Dai HJ, Su CH, Lai PT, Huang MS, Jonnagaddala J, Rose Jue T, Rao S, Chou HJ, Milacic M, Singh O, Syed-Abdul S, Hsu WL.
PMID: 27242035
Database (Oxford). 2016 May 30;2016. doi: 10.1093/database/baw090. Print 2016.

Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis...

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Dai HJ, Su CH, Lai PT, et al. MET network in PubMed: a text-mined network visualization and curation system. Database (Oxford). 2016;2016doi: 10.1093/database/baw090.
Dai, H. J., Su, C. H., Lai, P. T., Huang, M. S., Jonnagaddala, J., Rose Jue, T., Rao, S., Chou, H. J., Milacic, M., Singh, O., Syed-Abdul, S., & Hsu, W. L. (2016). MET network in PubMed: a text-mined network visualization and curation system. Database : the journal of biological databases and curation, 2016. https://doi.org/10.1093/database/baw090
Dai, Hong-Jie, et al. "MET network in PubMed: a text-mined network visualization and curation system." Database : the journal of biological databases and curation vol. 2016 (2016). doi: https://doi.org/10.1093/database/baw090
Dai HJ, Su CH, Lai PT, Huang MS, Jonnagaddala J, Rose Jue T, Rao S, Chou HJ, Milacic M, Singh O, Syed-Abdul S, Hsu WL. MET network in PubMed: a text-mined network visualization and curation system. Database (Oxford). 2016 May 30;2016. doi: 10.1093/database/baw090. Print 2016. PMID: 27242035; PMCID: PMC4885604.
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Assessing video games to improve driving skills: a literature review and observational study.

JMIR serious games

Sue D, Ray P, Talaei-Khoei A, Jonnagaddala J, Vichitvanichphong S.
PMID: 25654355
JMIR Serious Games. 2014 Aug 07;2(2):e5. doi: 10.2196/games.3274.

BACKGROUND: For individuals, especially older adults, playing video games is a promising tool for improving their driving skills. The ease of use, wide availability, and interactivity of gaming consoles make them an attractive simulation tool.OBJECTIVE: The objective of this...

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Sue D, Ray P, Talaei-Khoei A, et al. Assessing video games to improve driving skills: a literature review and observational study. JMIR Serious Games. 2014;2(2):e5doi: 10.2196/games.3274.
Sue, D., Ray, P., Talaei-Khoei, A., Jonnagaddala, J., & Vichitvanichphong, S. (2014). Assessing video games to improve driving skills: a literature review and observational study. JMIR serious games, 2(2), e5. https://doi.org/10.2196/games.3274
Sue, Damian, et al. "Assessing video games to improve driving skills: a literature review and observational study." JMIR serious games vol. 2,2 (2014): e5. doi: https://doi.org/10.2196/games.3274
Sue D, Ray P, Talaei-Khoei A, Jonnagaddala J, Vichitvanichphong S. Assessing video games to improve driving skills: a literature review and observational study. JMIR Serious Games. 2014 Aug 07;2(2):e5. doi: 10.2196/games.3274. PMID: 25654355; PMCID: PMC4307814.
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