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1. Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study

Author

Jabour AM, Varghese J, Damad AH, Ghailan KY, and Mehmood AM

Subjects
influenza, predictive, surveillance, data quality, disease outbreaks, big data, epidemiological monitoring., Medicine (General), R5-920
Abstract

Abdulrahman M Jabour,1 Joe Varghese,1 Ahmed H Damad,2 Khalid Y Ghailan,3 Asim M Mehmood1 1Health Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia; 2Quality & Patient Safety Department, King Fahd Central Hospital - Jazan, Jazan, Saudi Arabia; 3Epidemiology Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi ArabiaCorrespondence: Abdulrahman M JabourHealth Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi ArabiaTel +966-17329500 Ex 5545Email ajabour@jazanu.edu.saIntroduction: Many studies have explored social media and users search activities such as Google Trends to predict and detect influenza activities. Studies that examined Google Trends correlation with the actual hospital influenza cases were conducted in non-tropical regions that have clearly defined seasons. Tropical areas are known for having less-defined seasonality and the extent of Google Trends concordance with actual influenza cases is unknown for these areas. The goal of this study is to compare Google Trends with hospital cases in tropical regions.Methods: We analyzed 48,263 influenza cases in the time period of 2010 to 2019. The cases were retrieved from central hospital medical records in tropical regions using the corresponding codes for influenza ICD-10 AM. Cases from the medical records were compared with Google Trends to determine trends, seasonality, and correlation.Results: Graphically, there were some similar areas of the trend, but cross-correlation analysis did not show any significant correlation between hospital and Google Trends with a maximum correlation rate of 0.300. Seasonality analysis showed a clear pattern that peaked around November in Google Trends while hospital data showed less defined seasonality with a smaller peak occurring at the end of December and beginning of January.Conclusion: Based on the results, there is a weak correlation between Google Trends and hospital data. More innovative methods are emerging to predict influenza activity using social media and user search data and further study is needed to examine the concurrent trends derived using these methods across regions that have different humidity levels and temperatures.Keywords: influenza, predictive, surveillance, data quality, disease outbreaks, big data, epidemiological monitoring

Published
2021

2. Observation of Gravitational Waves from the Coalescence of a $2.5\text{-}4.5~M_\odot$ Compact Object and a Neutron Star

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akçay, S., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentara, I., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Char, P., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chattopadhyay, D., Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnson-McDaniel, N. K., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Kedia, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. M., Knust, N., Kobayashi, K., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kruska, K., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuntimaddi, N., Kuroyanagi, S., Kurth, N. J., Kuwahara, S., Kwak, K., Kwan, K., Kwok, J., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Lalremruati, P. C., Landry, M., Landry, P., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Lawrence, M. N., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Jean, M. Le, Lemaître, A., Lenti, M., Leonardi, M., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levin, S. E., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Li, Z., Lihos, A., Lin, C-Y., Lin, C. -Y., Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Villarreal, F. Llamas, Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Lorenzo-Medina, A., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lu, N., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., Macedo, A., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Makelele, E., Malaquias-Reis, J. A., Mali, U., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markosyan, A. S., Markowitz, A., Maros, E., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Matcovich, T., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McEachin, S., McElhenny, C., McGhee, G. I., McGinn, J., McGowan, K. B. M., McIver, J., McLeod, A., McRae, T., Meacher, D., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mera, F., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Milotti, V., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakagaki, K., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narikawa, T., Narola, H., Naticchioni, L., Nayak, R. K., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Oliveira, A. S., Oliveri, R., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, S., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ota, I., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pal, A., Pal, S., Palaia, M. A., Pálfi, M., Palma, P. P., Palomba, C., Palud, P., Pan, H., Pan, J., Pan, K. C., Panai, R., Panda, P. K., Pandey, S., Panebianco, L., Pang, P. T. H., Pannarale, F., Pannone, K. A., Pant, B. C., Panther, F. H., Paoletti, F., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Paquis, A., Parisi, A., Park, B. -J., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passenger, L., Passuello, D., Patane, O., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. 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A., Prokhorov, L., Prosposito, P., Puecher, A., Pullin, J., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Raab, F. J., Raabith, S. S., Raaijmakers, G., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Ranjan, S., Ransom, K., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Renzini, A. I., Rettegno, P., Revenu, B., Reyes, R., Rezaei, A. S., Ricci, F., Ricci, M., Ricciardone, A., Richardson, J. W., Richardson, M., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sathyaprakash, B. S., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., Stevenson, S., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong, H., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract

We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779

Published
2024
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3. CDEGenerator: an online platform to learn from existing data models to build model registries

Author

Varghese J, Fujarski M, Hegselmann S, Neuhaus P, and Dugas M

Subjects
Common data elements, Semantic Interoperability, Metadata Repositories, Unified Medical Language System, Infectious and parasitic diseases, RC109-216
Abstract

Julian Varghese,1 Michael Fujarski,2 Stefan Hegselmann,1 Philipp Neuhaus,1 Martin Dugas1,3 1Institute of Medical Informatics, University of Münster, 2Faculty of Mathematics and Computer Sciences, University of Münster, 3Institute of Medical Informatics, European Research Center for Information Systems (ERCIS), Münster, Germany Objective: Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases. Methods: Based on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale. Results: The analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded “good usability” (mean 75.0, range 65.0–92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies. Conclusion: The online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database. Keywords: common data elements, semantic interoperability, metadata repositories, Unified Medical Language System

Published
2018

7. Association of Histologic Chorioamnionitis with Perinatal Brain Injury and Early Childhood Neurodevelopmental Outcomes among Preterm Neonates

Author

Bierstone, D, Wagenaar, N, Gano, DL, Guo, T, Georgio, G, Groenendaal, F, De Vries, LS, Varghese, J, Glass, HC, Chung, C, Terry, J, Rijpert, M, Grunau, RE, Synnes, A, Barkovich, AJ, Ferriero, DM, Benders, M, Chau, V, and Miller, SP

Subjects
Pediatric, Infant Mortality, Perinatal Period - Conditions Originating in Perinatal Period, Preterm, Low Birth Weight and Health of the Newborn, Infectious Diseases, Reproductive Health and Childbirth, Paediatrics And Reproductive Medicine, Obstetrics & Reproductive Medicine, Paediatrics and Reproductive Medicine
Abstract

Acute chorioamnionitis refers to the neutrophilic inflammation of the placental tissues thought to result from an ascending bacterial infection. It is considered a major factor associated with pretermbirth and has been estimated to occur in 40%to 80% of preterm deliveries. Chorioamnionitis is associated with several adverse neonatal outcomes, including respiratory distress syndrome, sepsis, bronchopulmonary dysplasia, and death. Clinical studies examining brain injury and neurodevelopmental outcomes among infants with chorioamnionitis have yielded inconsistent results. Most of these studies have focused on intraventricular hemorrhage (IVH) and cystic periventricular leukomalacia. Because the incidence of cystic periventricular leukomalacia has greatly decreased during the past decades concurrent with improvements in neonatal intensive care, punctate white matter injury (WMI) is increasingly recognized as the most prevalent pattern of brain injury among preterm neonates. The researchers performed a prospective cohort study conducted across 3 academic centers in Canada, the Netherlands, and the United States. Children who were born preterm (24-32 weeks' gestation) and who had undergone a placental pathologic evaluation, magnetic resonance imaging (MRI) as soon as clinically stable, and Bayley Scales of Infant and Toddler Development, 3rd Edition (Bayley III) assessments between 18 and 24 months' corrected age (CA) were included. Magnetic resonance imaging scans were assessed for grade of IVH and volume of punctate WMI. Data analysis occurred between December 2016 and January 2018. Final multivariable analyses examining the association of chorioamnionitis with motor and cognitive outcomes accounted for academic center and perinatal and postnatal factors. PunctateWMI volume and IVH detected on neonatalMRI scans were used tomeasure the results,withmotor and cognitive outcomes defined using Bayley III assessments conducted among these children between 18 and 24 months' CA. There were 448 preterm infants (24-32 weeks' gestation) in the total cohort. Infants were included in the analysis if they had undergone placental pathologic assessments, early brainMRI, and 18 to 24 months of follow-up.Among the cohorts fromthe 3 academic centers, infants with evidence of a congenital infection, genetic syndrome, or large parenchymal hemorrhagic infarction (>2 cm) were excluded. Each placenta was sent fresh for macroscopic and microscopic analyses, which were conducted using the same clinical protocols at each center. Histologic chorioamnionitis was defined by clinical pathologists using strict criteria and the degree of placental inflammation was scored.

Published
2018

10. Comparative analysis of self‐cure and dual cure‐dental composites on their physico‐mechanical behaviour.

Author

Thadathil Varghese, J, Raju, R, Farrar, P, Prentice, L, and Prusty, BG

Subjects
SCIENTIFIC knowledge, DENTAL materials, DENTAL resins, FLEXURAL modulus, FILLER materials
Abstract

Background: Clinical practitioners may have become familiar with the rapid transformation of dental composites. However, they may not scientifically understand the factors influencing the mechanical and physical properties. Scientific knowledge of filler‐resin interaction can significantly improve clinical understanding of resin composites. Several independent studies have examined the mechanical and physico‐mechanical properties of dental resin composites; however, no comprehensive study has examined the influence of fillers and resin materials on the physico‐mechanical properties of both self‐cure and dual‐cure composites. Methods: This study performed investigations on the physico‐mechanical behaviour of four commercially available dual‐cure dental composites (Bioactive, Fill Up!, Surefil One, Cention N) and two commercially available self‐cure dental composites (Stela Capsule and Stela Automix). Test specimens for flexural and compressive strength, microhardness, fracture toughness, and hydrolytic behaviour were prepared and tested as per respective standards. The data sets were statistically analysed using one‐way ANOVA and Tukey's post‐hoc comparison. Results: There was a substantial variation in flexural strength and modulus values in this study, ranging from 32.0 to 113.4 MPa and 2.36 to 12.07 GPa, respectively. Similarly, there were significant differences in compressive strength between the materials in this study, ranging from 119.3 to 223.5 MPa. The highest fracture toughness value was found to be 1.41 MPa.m0.5, while the lowest value was 0.43 MPa.m0.5. Variations in surface microhardness were significant (24.11–68.0 N/mm2), which correlated with the filler content. Water sorption and solubility demonstrated high variations among materials, with Surefil One exceeding ISO 4049 thresholds significantly. Conclusions: A linear correlation can be established between surface microhardness (HV) and flexural and compressive moduli, as well as filler content (wt.%). However, both flexural and compressive strengths are impacted by the resin's constituent monomers and the resin‐filler matrix's cross‐linking capability. Additionally, factors such as filler size, shape, and the cross‐linking ability of the resin‐filler matrix play a crucial role in fracture toughness and the propagation of cracks within the restoration. Also, resin monomers and filler particle size affect the hydrolytic degradation characteristics of composites, which can also affect their mechanical properties. © 2023 Australian Dental Association. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Empfehlungen zur Umsetzung des NKLM 2.x durch Epidemiologie, medizinische Biometrie und Medizininformatik, mit Bioinformatik und verwandten Fächern

Author

Timmer, A, Weberschock, T, Rothenbacher, D, Varghese, J, Berger, U, Schlattmann, P, Dugas, M, Kopp-Schneider, A, Winter, A, and Binder, H

Subjects
Epidemiologie, Digitalisierung, education, Ausbildung, Evidenzbasierte Medizin, biometry, medical curriculum, Medizininformatik, digitalization, Medizincurriculum, NKLM, ddc: 610, recommendations, medical informatics, epidemiology, Wissenschaftsausbildung, science education, evidence-based medicine, Biometrie, Empfehlungen
Abstract

Medical data sciences receive attention as digitalization and artificial intelligence (AI) pose new challenges and opportunities in health care. Specific methods and skills derived from the data sciences have been driving essential developments in almost if not all areas of health care and medical research over many years. For example, evidence-based medicine is now a pillar of medical practice, combined with a stronger focus on reproducible and valid research and an improved understanding of scientific methods. The increased role of the data sciences in medical research and practice is reflected in the revised national competency-based learning objectives catalog for medicine (NKLM 2.0). Digitalization, research skills, evidence medicine and health promotion and prevention are integral parts of the curriculum from start to end. They relate to all subjects and topics in an interprofessional manner. This increase in relevance of the data sciences clearly calls for improved competencies in the clinico-theoretical disciplines previously combined as interdisciplinary domain 1 (Q1). Epidemiology, medical biometry and medical informatics will now contribute expertise throughout the whole course of studying medicine. These disciplines deal with digitalization, medical research competence, evidence-based medicine, and prevention. In addition, disease-related learning and many aspects of therapy, diagnostics, communication, and management benefit from cooperation with dedicated instruction in the medical data sciences by didactically trained experts. This article aims to support faculties and subject representatives during the implementation period of the NKLM 2.0 and beyond regarding data science skills. Tables provide an overview of essential learning objectives in epidemiology, biometry, and medical informatics across the different phases of the curriculum. In addition, we give recommendations for cooperation with other subject representatives. By this we wish to contribute to improving the medical curriculum based on graduate profile-oriented interdisciplinary-integrative teaching. Medizinische Datenwissenschaften erhalten über Themen wie Digitalisierung und künstliche Intelligenz (KI) große Aufmerksamkeit. Aber auch in anderen Bereichen entwickeln sich Gesundheitsversorgung und Forschung kontinuierlich weiter unter Rückgriff auf Methoden und Kompetenzen der in den Datenwissenschaften gebündelten Fächer. So ist die Evidenzbasierte Medizin inzwischen als Pfeiler ärztlichen Handelns etabliert, verbunden mit einem stärkeren Fokus auf reproduzierbarer und valider Forschung und verbessertem wissenschaftlichen Methodenverständnis. Diese wichtigen Entwicklungen medizinischer Forschung und Praxis spiegeln sich auch im überarbeiteten nationalen kompetenzbasierten Lernzielkatalog Medizin (NKLM 2.0) wider. Themenkomplexe zu Digitalisierung, medizinisch-wissenschaftlichen Kompetenzen, und Gesundheitsförderung und Prävention prägen das geplante Curriculum über das gesamte Studium hinweg fächerübergreifend und interprofessionell. Dies korrespondiert mit einer deutlichen Aufwertung der Fächer, die bisher als klinisch-theoretische Fächer im Querschnittsbereich 1 (Q1) zusammengefasst waren. Epidemiologie, Biometrie und Medizininformatik zuzüglich der Bioinformatik bringen nun über den gesamten Verlauf des Studiums Expertise ein. Diese Expertise betrifft schwerpunktmäßig die genannten Bereiche Digitalisierung, wissenschaftliche Methodenkompetenz, Evidenzbasierte Medizin und Prävention. Aber auch inhaltlich-krankheitsbezogene Themen und viele Aspekte aus Therapie, Diagnostik, Kommunikation und Management profitieren von Kooperationen mit Lehrenden der Datenwissenschaften. Dieser Artikel soll medizinische Fakultäten und Fachvertreter bei der Umsetzung des NKLM 2.0 dort unterstützen, wo Kompetenzen der Datenwissenschaften benötigt werden. An den Kapiteln des NKLM orientierte Tabellen bieten eine Übersicht der wesentlichen Lernziele der Epidemiologie, Biometrie und Medizininformatik über die verschiedenen Phasen des Studiums. Darüber hinaus geben wir konkrete Empfehlungen für Kooperationen mit anderen Fächern. Wir möchten damit zur Verbesserung medizinischer Curricula im Sinne absolventenprofilorientierter interdisziplinär-integrativer Lehre beitragen.

Published
2022

24. MRI Brain Image Segmentation with Machine Learning for Mice and Rats: A Preclinical Application

Author

Plagwitz, L, Pradier, B, van Alen, C, and Varghese, J

Subjects
biomedical image segmentation, ddc: 610, Medicine and health, deep learning, artificial intelligence
Abstract

Introduction: In radiology, the preparation of magnetic resonance imaging (MRI) data is an essential step for subsequent analyses. Although it has been shown that modern machine learning algorithms can assist radiologists in MRI-specific preprocessing tasks [ref:1], they have often not yet [for full text, please go to the a.m. URL]

Published
2022
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27. Abstract No. 184 Pre-operative portal vein recanalization-transjugular intrahepatic portosystemic shunt for chronic, obliterative portal vein thrombosis: outcomes following liver transplantation

Author

Talwar, A., primary, Varghese, J., additional, Knight, G., additional, Katariya, N., additional, Caicedo-Ramirez, J., additional, Dietch, Z., additional, Borja-Cacho, D., additional, Ladner, D., additional, Christopher, D., additional, Baker, T., additional, Abecassis, M., additional, Mouli, S., additional, Desai, K., additional, Riaz, A., additional, Thornburg, B., additional, and Salem, R., additional

Published
2022
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38. Online-Education of Medical Data Scientists by Using the Research Infrastructure MDM Portal

Author

Meidt, A, Riepenhausen, S, Neuhaus, P, Rafee, A, Hegselmann, S, Varghese, J, and Dugas, M

Subjects
medical data models, standardization, education, ddc: 610, annotation, Medicine and health
Abstract

Introduction: The Portal of Medical Data Models (MDM Portal) [ref:1] is a recognized German [ref:2] and European [ref:3] research infrastructure for medical research. To our knowledge, it constitutes Europe’s largest open-access platform for medical metadata stored [for full text, please go to the a.m. URL]

Published
2021
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46. Royal academy of medicine in Ireland section of radiology: Proceedings of registrars’ prize meeting held december 1997

Author

Abbas, S., Varghese, J., Power, N., McGrath, F., Murray, F., Osborne, H., Lee, M. J., McCarthy, R. M., McGlone, B., Duggan, C., McCann, S., Daly, P., Molloy, M., McEniff, N., Wheat, D., Maher, M. M., Murray, J. G., McDermott, R. A., McEniff, N., Wilson, G. F., Duggan, C., McCann, S., Mirza, S., McCarthy, P., Schranz, M., Gibney, R. G., Malone, D. E., Doyle, J., Lucey, B., Farrell, M. A., Varghese, J. C., Thornton, J., McGrath, F. P., Lee, M. J., McNamara, A., Bergin, D., Boyle, B., Murray, J. G., MacEneaney, P. M., Skehan, S. J., Curry, M., Gibney, R. G., McCormick, P. A., Malone, D. E., Carr, J. C., Skehan, S. J., O’Donovan, A. N., Malone, D. E., McCormick, P. A., Brannigan, M., Pender, S., Keeling, F., McGrath, F., Varghese, J., and Lee, M. J.

Published
1998
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47. Irish society of gastroenterology: Proceedings of meeting held Friday 12th and Saturday 13th June, 1998

Author

Ryan, B. M., McKiernan, S., Keeling, P. W. N., Byrne, P. J., Quill, R., McCallion, K., Mitchell, R. M. S., Watson, R. G. P., Collins, J. S. A., Gardiner, K. R., Winter, D. C., O’Sullivan, G. C., Taylor, C. T., Fanning, N. F., Redmond, H. P., O’Donoghue, D. P., Baird, A. W., Harvey, B. J., Brannigan, A. E., O’Connell, P. R., Regan, M. C., Fitzpatrick, J. M., Watson, R. W. G., Lemass, H., Ryan, E., MacMathuna, P., Crowe, J., O’Keane, J. C., Goh, J., MacMathuna, P., Baird, A., Maher, L., Godson, C., Brady, H. R., Petasis, N. A., Fokin, V. V., O’Keane, J. C., Barry, M. K., Grant, D. C., Sheahan, K., O’Donoghue, D. P., Hyland, J. M. P., Sheehan, K. M., Sheahan, K., O’Donoghue, D. P., Fitzgerald, D. J., Murray, F. E., Heaney, A., Collins, J. S. A., Bamford, K. B., Watson, R. G. P., McFarland, R. J., Tham, T. C. K., McNamara, D., Franelli, S., Whelan, H., Hamilton, H., Beattie, S., O’Morain, C., Ryan, B. M., Keeling, P. W. N., Brennan, E. G., O’Hare, N., McDermott, R., Byrne, P. J., McDougall, N. I., Collins, J. S. A., Gieeson, C. M., Russell, S. E. H., Sloan, J. M., Morrisey, D., Murphy, L., Kiely, B., Fitzgerald, G., Daly, C., O’Sullivan, G., Shanahan, F., Collins, J. K., Marteau, P., Johnston, S. D., Watson, R. G. P., Coates, C., Feighery, C., O’Keeffe, J., Whelan, A., Lynch, S., Weir, D. G., Abuzakouk, M., Feighery, C., Barnes, L., O’Gorman, N., McKenna, M., Freaney, R., Young, M., Gaines, S., Brady, D., Drudy, D., O’Farrelly, C., Gilleece, A., Fenelon, L., McPartlin, J., Goh, J., MacMathuna, P., Baird, A., Godson, C., Brady, H. R., Petasis, N. A., Fokin, V. V., Hopkins, A. M., Myers, A., Moynagh, P., Baird, A. W., O’Donoghue, D. P., Kirby, J. M., Tham, T. C. K., Allen, M. J., Best, B., Calvert, H., Kirk, S., McKelvey, S. T. D., Moorehead, R. J., Varghese, J. C., Sookhai, S., Murray, F. E., Walsh, T., Osborne, H., Broe, P., Lee, M. J., Moriarty, D., Baird, A. W., Watson, R. W. G., Brannigan, A. E., Coffey, R., Murphy, E., Fitzpatrick, J. M., Shah, A. A., Murray, F. E., Murray, E., Thjodleifsson, B., Bjarnason, I., Fitzgerald, D. J., Sheehan, K. M., Murray, E., Shah, A. A., Murray, F. E., Thjodleifsson, B., Bjarnason, I., Fitzgerald, D. J., Montague, S., McNamara, D., Forkin, C., O’Morain, C., O’Toole, G. C., Grant, D. C., Barry, M. K., Sheahan, K., O’Donoghue, D. P., Hyland, J. M. P., Gallagher, C. M., Grant, D. C., Connell, P., Barry, M. K., Traynor, O., Hyland, J. M. P., Barry, M. K., Grant, D. C., Sheahan, K., O’Donoghue, D. P., Hyland, J. M. P., Ling, T. C., Johnston, B., Byrne, M. F., Farrell, M. A., Varghese, J. C., Lee, M. J., Murray, F. E., Goulding, C. A., Albloushi, S. S., O’Connell, P., Murray, F. E., Graham, L. E., Robinson, T. J., Graham, L. E., Robinson, T. J., Jabeen, T., Cannon, B., Jenkins, D., Whelton, M. J., Bohra, S., Cannon, B., Whelton, M. J., Keohane, C., Duggan, M., Bohra, S., Cannon, B., Whelton, M. J., Siddheshwar, R. K., Wilson, R. G., Hainsworth, P. J., Campbell, F. C., Kelly, S. B., Kirby, J. M., Tham, T. C. K., Best, B., Calvert, H., Moorehead, R. J., Kirk, S., Heaney, A., Collins, J. S. A., Watson, R. G. P., Heaney, A., Collins, J. S. A., Watson, R. G. P., Bamford, K. B., McFarland, R. J., Tham, T. C. K., Egan, B. M., Grant, D. C., Barry, M. K., Traynor, O., Hyland, J. M. P., Simutowe, C., McNamara, D. A., Collins, N., Walsh, T. N., Mukherjee, A., Scott, M., Pohl, C., Duggan, E., Wasi, M., Sarkar, A., Donnell, L. O., Eustace, P. W., Johnston, J. G., Waldron, R., Barrett, S., Ryan, E., MacMathuna, P., Crowe, J., Callagy, G., Keane, J. C. O., Coughlan, B., Crowe, J., Sheehan, J., Hickey, A., Carr, A., Kell, M. R., Lynch, M., Ryan, D., Winter, D. C., Rajpal, P., Kirwan, W. O., Redmond, H. P., Larkin, C. J., Sloan, J. M., Watson, R. G. P., Ardill, J. E. S., Collins, J. S. A., Buchanan, K. D., Lim, P. L., Gibbons, M., Crawford, E. J., Johnston, B. T., Mitchell, R. M. S., Watson, R. G. P., Gardiner, K. R., Collins, J. S. A., McCallion, K., Mitchell, R. M. S., Collins, J. S. A., Watson, R. G. P., McCallion, K., Gardiner, K. R., McCallion, K., Gardiner, K. R., Mitchell, R. M. S., Watson, R. G. P., Collins, J. S. A., Rodgers, C., Johnston, S., Watson, R. G. P., Crone, B. M., Love, A. H. G., Feighery, L., Feighery, C., Jackson, J., Abuzakouk, M., Lynch, S., Weir, D. G., Yassin, M. M. I., Harkin, D. W., Barros D’sa, A. A. B., Parks, T. G., Curry, M. P., Hegarty, J. E., Golden-Mason, L., O’Farrelly, C., Hannigan, E., and Parfrey, N.

Published
1998
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49. Which competencies in medical informatics are required by physicians? An update of the catalog of learning objectives for medical students

Author

Varghese, J, Röhrig, R, Dugas, M, GMDS-Arbeitsgruppe \\'MI-Lehre in der Medizin\\', and GMDS working group \\'MI-Teaching in Medicine\\'

Subjects
education, ddc: 610, medical student, Medizinische Informatik, Ausbildung, lcsh:R858-859.7, medical informatics, lcsh:RC109-216, Medizinstudent/in, 610 Medical sciences, Medicine, lcsh:Computer applications to medicine. Medical informatics, lcsh:Infectious and parasitic diseases
Abstract

The digital transformation has influenced medical pathways in clinical routine and research as never before. This transformation requires multiple competencies in digital health from health professionals. To address learning objectives for teaching, the national project group „Teaching Medical Informatics in Medicine“ of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS) published a competency-based catalog in 2012. The project group has turned into working group with regular meetings to continuously plan, discuss, develop and harmonize teaching content in medical schools on a national level. After six years have passed, an updated catalog has been proposed, reviewed and consented by the working group. The new catalog specifically covers new aspects as patient empowerment, patient apps, precision medicine and further minor amendments. It is proposed as guidance for teaching contents of medical informatics at medical schools and will be subject to continuous developments and harmonization with ongoing digitization initiatives by the clinical disciplines., Die digitale Transformation hat Einzug in den Alltag der klinischen Routine und – Forschung gehalten. Diese Transformation stellt Gesundheitsberufe vor spezielle Herausforderungen. Vor diesem Hintergrund hatte die Projektgruppe „Medizininformatik-Lehre in der Medizin“ der Gesellschaft für Medizininformatik, Biometrie und Epidemiologie (GMDS) einen kompetenzbasierten Lernzielkatalog 2012 veröffentlicht. Aus der Projektgruppe wurde eine Arbeitsgruppe, die regelmäßig tagt, um kontinuierlich die Inhalte der Medizininformatik in den Medizinischen Fakultäten zu planen, diskutieren, weiterzuentwickeln und zu harmonisieren. Nach sechs Jahren wurde eine Aktualisierung dieses Katalogs vorgeschlagen und im Umlaufverfahren von der Arbeitsgruppe konsentiert. Die neuen Inhalte dieses Katalogs adressieren aktuell aufstrebende Themen wie Patient Empowerment, Patienten-Apps und Präzisionsmedizin sowie weitere kleinere Änderungen. Der Katalog dient als Orientierung der Lehrinhalte für das Fach Medizininformatik in Medizinischen Fakultäten und wird auch in Zukunft in der Arbeitsgruppe diskutiert, und unter Berücksichtigung der Digitalisierungsinitiativen der klinischen Fachgesellschaften weiterentwickelt., GMS Medizinische Informatik, Biometrie und Epidemiologie; 16(1):Doc02

Published
2020
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50. PVDF-SIC composite thick films an effective ESD composition for growing anti-static applications

Author

Joseph, N. (Nina), Varghese, J. (Jobin), and Sebastian, M. T. (Mailadil Thomas)

Subjects
EMI shielding, PVDF, Silicon carbide, composite, films
Abstract

Thin polyvinylidene fluoride-silicon carbide (PVDF-SiC) shielding composite films were prepared by a simple solution casting process. X-ray diffraction (XRD) and scanning electron microscopy (SEM) show the presence of both polymer and filler with no reaction between them. The coefficient of thermal expansion (CTE) and tensile strength of the composite exhibit a decreasing tendency, while that of conductivity and electromagnetic interference (EMI) shielding values increase with filler loading. The εr, and tanδ of the composites were also found to increase with filler loading in the frequency range of 8.2—18 GHz, which also influence the shielding behaviour of the composite. The high filler (60 vol.% of SiC) loaded composite of 1 mm thickness exhibits direct current (DC) conductivity of the order of 10-5 S/m indicating its potential for electrostatic discharge shielding applications. This is further confirmed from its EMI SE value of 12 dB, which corresponds 94% attenuation. The composite exhibits a partially reflecting and absorbing shielding nature, without any dominant shielding mechanism. The alternating current (AC) conductivity and skin depth value of the composites exhibit an inverse relationship with each other and influence the shielding thickness. The attenuating power of the PVDF-SiC composite with 60 volume % (vol.%) silicon carbide (SiC) changes from 84% to 99%, with an increase in sample thickness from 0.5 mm to 2 mm. Hence, thin and flexible paper-like EMI shield of PVDF-SiC composite can be easily prepared by simple, cost-effective approach for various shielding applications like electrostatic discharge shielding as well as EMI shielding.

Published
2020

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