{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:27:10Z","timestamp":1715646430159},"reference-count":29,"publisher":"Tohoku University Medical Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tohoku J. Exp. Med."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1620\/tjem.2023.j090","type":"journal-article","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T22:13:07Z","timestamp":1698876787000},"page":"33-41","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of Statistical Approaches in Developing a Predictive Model of Severe COVID-19 during Early Phase of Pandemic with Limited Data Resources"],"prefix":"10.1620","volume":"262","author":[{"given":"Tetsuya","family":"Akaishi","sequence":"first","affiliation":[{"name":"Department of Education and Support for Regional Medicine, Tohoku University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasunori","family":"Tadano","sequence":"additional","affiliation":[{"name":"Department of Education and Support for Regional Medicine, Tohoku University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshitaka","family":"Kimura","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tohoku University Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nobuo","family":"Yaegashi","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tohoku University Hospital"},{"name":"Tohoku Medical Megabank Organization, Tohoku University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tadashi","family":"Ishii","sequence":"additional","affiliation":[{"name":"Department of Education and Support for Regional Medicine, Tohoku University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"622","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Akashi, H., Kodoi, H., Noda, S., Tamura, T., Baba, H., Chinda, E., Thandar, M.M., Naito, K., Watanabe, Y., Suzuki, Y., Narita, T. &amp; Shimazu, T. (2022)  Reporting on the implementation to set up a \u201ccare and isolation facility\u201d for mild COVID-19 cases in Tokyo.  <i>Glob. Health Med.<\/i>, 4, 71-77.","DOI":"10.35772\/ghm.2022.01022"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Andrade, C. (2021)  Z scores, standard scores, and composite test scores explained.  <i>Indian J. Psychol. Med.<\/i>, 43, 555-557.","DOI":"10.1177\/02537176211046525"},{"key":"3","doi-asserted-by":"crossref","unstructured":"Awan, F.M., Saleem, Y., Minerva, R. &amp; Crespi, N. (2020)  A comparative analysis of machine\/deep learning models for parking space availability prediction.  <i>Sensors <\/i>(<i>Basel<\/i>), 20, 322.","DOI":"10.3390\/s20010322"},{"key":"4","doi-asserted-by":"crossref","unstructured":"DeLong, E.R., DeLong, D.M. &amp; Clarke-Pearson, D.L. (1988)  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.  <i>Biometrics<\/i>, 44, 837-845.","DOI":"10.2307\/2531595"},{"key":"5","doi-asserted-by":"crossref","unstructured":"Gallo Marin, B., Aghagoli, G., Lavine, K., Yang, L., Siff, E.J., Chiang, S.S., Salazar-Mather, T.P., Dumenco, L., Savaria, M.C., Aung, S.N., Flanigan, T. &amp; Michelow, I.C. (2021)  Predictors of COVID-19 severity: a literature review.  <i>Rev. Med. Virol.<\/i>, 31, 1-10.","DOI":"10.1002\/rmv.2146"},{"key":"6","doi-asserted-by":"crossref","unstructured":"Golpour, P., Ghayour-Mobarhan, M., Saki, A., Esmaily, H., Taghipour, A., Tajfard, M., Ghazizadeh, H., Moohebati, M. &amp; Ferns, G.A. (2020)  Comparison of support vector machine, na\u00efve Bayes and logistic regression for assessing the necessity for coronary angiography.  <i>Int. J. Environ. Res. Public Health<\/i>, 17, 6449.","DOI":"10.3390\/ijerph17186449"},{"key":"7","doi-asserted-by":"crossref","unstructured":"Gude-Sampedro, F., Fern\u00e1ndez-Merino, C., Ferreiro, L., Lado-Baleato, \u00d3., Espasand\u00edn-Dom\u00ednguez, J., Hervada, X., Cadarso, C.M. &amp; Vald\u00e9s, L. (2021)  Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study.  <i>Int. J. Epidemiol.<\/i>, 50, 64-74.","DOI":"10.1093\/ije\/dyaa209"},{"key":"8","doi-asserted-by":"crossref","unstructured":"Hu, Z., Huang, X., Zhang, J., Fu, S., Ding, D. &amp; Tao, Z. (2022)  Differences in clinical characteristics between delta variant and wild-type SARS-CoV-2 infected patients.  <i>Front. Med. <\/i>(<i>Lausanne<\/i>), 8, 792135.","DOI":"10.3389\/fmed.2021.792135"},{"key":"9","doi-asserted-by":"crossref","unstructured":"Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., et al.  (2020)  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.  <i>Lancet<\/i>, 395, 497-506.","DOI":"10.1016\/S0140-6736(20)30183-5"},{"key":"10","doi-asserted-by":"crossref","unstructured":"Lauer, S.A., Grantz, K.H., Bi, Q., Jones, F.K., Zheng, Q., Meredith, H.R., Azman, A.S., Reich, N.G. &amp; Lessler, J. (2020)  The incubation period of coronavirus disease 2019 (COVID-19)  from publicly reported confirmed cases: estimation and application.  <i>Ann. Intern. Med.<\/i>, 172, 577-582.","DOI":"10.7326\/M20-0504"},{"key":"11","doi-asserted-by":"crossref","unstructured":"Li, X., Marmar, T., Xu, Q., Tu, J., Yin, Y., Tao, Q., Chen, H., Shen, T. &amp; Xu, D. (2020)  Predictive indicators of severe COVID-19 independent of comorbidities and advanced age: a nested case-control study.  <i>Epidemiol. Infect.<\/i>, 148, e255.","DOI":"10.1017\/S0950268820002502"},{"key":"12","doi-asserted-by":"crossref","unstructured":"Machida, M. &amp; Wada, K. (2022)  Public health responses to COVID-19 in Japan.  <i>Glob. Health Med.<\/i>, 4, 78-82.","DOI":"10.35772\/ghm.2022.01025"},{"key":"13","doi-asserted-by":"crossref","unstructured":"Meng, Z., Wang, M., Zhao, Z., Zhou, Y., Wu, Y., Guo, S., Li, M., Zhou, Y., Yang, S., Li, W. &amp; Ying, B. (2021)  Development and validation of a predictive model for severe COVID-19: a case-control study in China.  <i>Front. Med. <\/i>(<i>Lausanne<\/i>), 8, 663145.","DOI":"10.3389\/fmed.2021.663145"},{"key":"14","doi-asserted-by":"crossref","unstructured":"Metz, C.E. (1978)  Basic principles of ROC analysis.  <i>Semin. Nucl. Med.<\/i>, 8, 283-298.","DOI":"10.1016\/S0001-2998(78)80014-2"},{"key":"15","doi-asserted-by":"crossref","unstructured":"Mfateneza, E., Rutayisire, P.C., Biracyaza, E., Musafiri, S. &amp; Mpabuka, W.G. (2022)  Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset.  <i>BMC Pregnancy Childbirth<\/i>, 22, 388.","DOI":"10.1186\/s12884-022-04699-8"},{"key":"16","doi-asserted-by":"crossref","unstructured":"Ng, O.T., Marimuthu, K., Lim, N., Lim, Z.Q., Thevasagayam, N.M., Koh, V., Chiew, C.J., Ma, S., Koh, M., Low, P.Y., Tan, S.B., Ho, J., Maurer-Stroh, S., Lee, V.J.M., Leo, Y.S., et al.  (2022)  Analysis of COVID-19 incidence and severity among adults vaccinated with 2-dose mRNA COVID-19 or inactivated SARS-CoV-2 vaccines with and without boosters in Singapore.  <i>JAMA Netw. Open<\/i>, 5, e2228900.","DOI":"10.1001\/jamanetworkopen.2022.28900"},{"key":"17","doi-asserted-by":"crossref","unstructured":"Ong, S.W.X., Chiew, C.J., Ang, L.W., Mak, T.M., Cui, L., Toh, M., Lim, Y.D., Lee, P.H., Lee, T.H., Chia, P.Y., Maurer-Stroh, S., Lin, R.T.P., Leo, Y.S., Lee, V.J., Lye, D.C., et al.  (2022)  Clinical and virological features of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)  variants of concern: a retrospective cohort study comparing B.1.1.7 (Alpha), B.1.351 (Beta), and B.1.617.2 (Delta).  <i>Clin. Infect. Dis.<\/i>, 75, e1128-e1136.","DOI":"10.1093\/cid\/ciab721"},{"key":"18","doi-asserted-by":"crossref","unstructured":"Sardesai, A.U., Tanak, A.S., Krishnan, S., Striegel, D.A., Schully, K.L., Clark, D.V., Muthukumar, S. &amp; Prasad, S. (2021)  An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.  <i>Sci. Rep.<\/i>, 11, 16905.","DOI":"10.1038\/s41598-021-96081-5"},{"key":"19","doi-asserted-by":"crossref","unstructured":"Sun, L., Song, F., Shi, N., Liu, F., Li, S., Li, P., Zhang, W., Jiang, X., Zhang, Y., Sun, L., Chen, X. &amp; Shi, Y. (2020)  Combination of four clinical indicators predicts the severe\/critical symptom of patients infected COVID-19.  <i>J. Clin. Virol.<\/i>, 128, 104431.","DOI":"10.1016\/j.jcv.2020.104431"},{"key":"20","doi-asserted-by":"crossref","unstructured":"Tadano, Y., Akaishi, T., Suzuki, S., Ono, R., Saito, N., Arita, R., Kanno, T., Tanaka, J., Kikuchi, A., Ohsawa, M., Takayama, S., Abe, M., Onodera, K. &amp; Ishii, T. (2023)  Predictors for the development of hypoxia or prolonged acute symptoms among non-hospitalized mild-to-moderate patients with coronavirus disease 2019.  <i>Tohoku J. Exp. Med.<\/i>, 260, 231-244.","DOI":"10.1620\/tjem.2023.J038"},{"key":"21","doi-asserted-by":"crossref","unstructured":"Tanaka, T., Nambu, I., Maruyama, Y. &amp; Wada, Y. (2022)  Sliding-window normalization to improve the performance of machine-learning models for real-time motion prediction using electromyography.  <i>Sensors <\/i>(<i>Basel<\/i>), 22, 5005.","DOI":"10.3390\/s22135005"},{"key":"22","doi-asserted-by":"crossref","unstructured":"Uddin, S., Khan, A., Hossain, M.E. &amp; Moni, M.A. (2019)  Comparing different supervised machine learning algorithms for disease prediction.  <i>BMC Med. Inform. Decis. Mak.<\/i>, 19, 281.","DOI":"10.1186\/s12911-019-1004-8"},{"key":"23","doi-asserted-by":"crossref","unstructured":"Ustebay, S., Sarmis, A., Kaya, G.K. &amp; Sujan, M. (2023)  A comparison of machine learning algorithms in predicting COVID-19 prognostics.  <i>Intern. Emerg. Med.<\/i>, 18, 229-239.","DOI":"10.1007\/s11739-022-03101-x"},{"key":"24","doi-asserted-by":"crossref","unstructured":"Wang, F., Hou, H., Wang, T., Luo, Y., Tang, G., Wu, S., Zhou, H. &amp; Sun, Z. (2020)  Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests.  <i>Travel Med. Infect. Dis.<\/i>, 36, 101782.","DOI":"10.1016\/j.tmaid.2020.101782"},{"key":"25","doi-asserted-by":"crossref","unstructured":"World Medical Association  (2013)  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.  <i>JAMA<\/i>, 310, 2191-2194.","DOI":"10.1001\/jama.2013.281053"},{"key":"26","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Ma, Y., Ruan, L., Li, D., Lu, C. &amp;Huang, L.;  National Traditional Chinese Medicine Medical Team  (2022)  Comparing different machine learning techniques for predicting COVID-19 severity.  <i>Infect. Dis. Poverty<\/i>, 11, 19.","DOI":"10.1186\/s40249-022-00946-4"},{"key":"27","doi-asserted-by":"crossref","unstructured":"Yagis, E., Atnafu, S.W., Garc\u00eda Seco de Herrera, A., Marzi, C., Scheda, R., Giannelli, M., Tessa, C., Citi, L. &amp; Diciotti, S. (2021)  Effect of data leakage in brain MRI classification using 2D convolutional neural networks.  <i>Sci. Rep.<\/i>, 11, 22544.","DOI":"10.1038\/s41598-021-01681-w"},{"key":"28","doi-asserted-by":"crossref","unstructured":"Yamada, M., Jitkrittum, W., Sigal, L., Xing, E.P. &amp; Sugiyama, M. (2014)  High-dimensional feature selection by feature-wise kernelized Lasso.  <i>Neural Comput.<\/i>, 26, 185-207.","DOI":"10.1162\/NECO_a_00537"},{"key":"29","doi-asserted-by":"crossref","unstructured":"Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., et al.  (2020)  A novel coronavirus from patients with pneumonia in China, 2019.  <i>N. Engl. J. Med.<\/i>, 382, 727-733.","DOI":"10.1056\/NEJMoa2001017"}],"container-title":["The Tohoku Journal of Experimental Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/tjem\/262\/1\/262_2023.J090\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T04:32:22Z","timestamp":1715574742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/tjem\/262\/1\/262_2023.J090\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1620\/tjem.2023.j090","relation":{},"ISSN":["0040-8727","1349-3329"],"issn-type":[{"value":"0040-8727","type":"print"},{"value":"1349-3329","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"article-number":"2023.J090"}}