{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:35Z","timestamp":1777854515476,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p>\n                    Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya\u2013SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya\u2013SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya\u2013SCA algorithm attained the maximum value of\n                    <jats:italic>F<\/jats:italic>\n                    -measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.\n                  <\/jats:p>","DOI":"10.1177\/01655515211013708","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T15:54:20Z","timestamp":1621871660000},"page":"500-518","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4652-8420","authenticated-orcid":false,"given":"N.R. Gladiss","family":"Merlin","sequence":"first","affiliation":[{"name":"Jeppiaar Institute of Technology, India"}]},{"given":"Vigilson","family":"Prem. M","sequence":"additional","affiliation":[{"name":"RMD Engineering College, India"}]}],"member":"179","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx121"},{"key":"e_1_3_2_3_2","first-page":"9185686","article-title":"A way to understand inpatients based on the electronic medical records in the big data environment","volume":"2017","author":"Mao H","year":"2017","unstructured":"Mao H, Sun Y. A way to understand inpatients based on the electronic medical records in the big data environment. Int J Telemed Appl 2017; 2017: 9185686.","journal-title":"Int J Telemed Appl"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.04.014"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1108\/ITP-08-2013-0155"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2011.2173585"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23884"},{"issue":"4","key":"e_1_3_2_8_2","first-page":"31","article-title":"Non invasive estimation of blood pressure using a linear regression model from the photoplethysmogram (PPG) signal","volume":"22","author":"Valsalan P","year":"2017","unstructured":"Valsalan P, Manimegalai P, Augustine S. Non invasive estimation of blood pressure using a linear regression model from the photoplethysmogram (PPG) signal. Perspect Cienc Inf 2017; 22(4), 31\u201335.","journal-title":"Perspect Cienc Inf"},{"key":"e_1_3_2_9_2","volume-title":"Proceedings of the fourth international conference on computing, communications and networking technologies","author":"George A","unstructured":"George A, Rajakumar BR. On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis. In: Proceedings of the fourth international conference on computing, communications and networking technologies, Tiruchengode, India, July 2013."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijproman.2015.10.003"},{"issue":"3","key":"e_1_3_2_11_2","first-page":"1","article-title":"DIGWO: hybridization of dragonfly algorithm with improved grey wolf optimization algorithm for data clustering","volume":"2","author":"Jadhav AN","year":"2019","unstructured":"Jadhav AN, Gomathi N. DIGWO: hybridization of dragonfly algorithm with improved grey wolf optimization algorithm for data clustering. Multimed Res 2019; 2(3): 1\u201311.","journal-title":"Multimed Res"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-016-0712-4"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2010.2049352"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-015-1928-y"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJBDI.2015.070597"},{"issue":"1","key":"e_1_3_2_16_2","first-page":"166","article-title":"A bibliometric analysis and visualization of medical big data research","volume":"10","author":"Liao H","year":"2018","unstructured":"Liao H, Tang M, Luo L et al. A bibliometric analysis and visualization of medical big data research. Big Data Predict Anal Sustain 2018; 10(1): 166.","journal-title":"Big Data Predict Anal Sustain"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-017-2222-4"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-017-2052-4"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3864"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2015.08.001"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-020-00362-3"},{"key":"e_1_3_2_22_2","unstructured":"https:\/\/deepmind.com\/"},{"key":"e_1_3_2_23_2","first-page":"576","volume-title":"Proceedings of the IEEE 30th international symposium on computer-based medical systems (CBMS)","author":"Meropi P","unstructured":"Meropi P, Billis AS, Hasanagas ND et al. Conditional entropy based retrieval model in patient-carer conversational cases. In: Proceedings of the IEEE 30th international symposium on computer-based medical systems (CBMS), Thessaloniki, 22\u201324 June 2017, pp. 576\u2013581. New York: IEEE."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-015-0830-y"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.12.022"},{"issue":"1","key":"e_1_3_2_26_2","first-page":"19","article-title":"Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems","volume":"7","author":"Rao R","year":"2016","unstructured":"Rao R. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 2016; 7(1): 19\u201334.","journal-title":"Int J Ind Eng Comput"},{"key":"e_1_3_2_27_2","volume-title":"Proceedings of 5th biennial conference on innovative data systems research (No. CONF)","author":"Idreos S","unstructured":"Idreos S, Alagiannis I, Johnson R et al. Here are my data files. Here are my queries. Where are my results? In: Proceedings of 5th biennial conference on innovative data systems research (No. CONF), Asilomar, CA, USA, 9\u201312 January 2011."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2645606"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2014.10.007"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1747-7_63"},{"key":"e_1_3_2_31_2","unstructured":"Breast cancer dataset https:\/\/archive.ics.uci.edu\/ml\/datasets\/breast+cancer (accessed July 2019)"},{"key":"e_1_3_2_32_2","unstructured":"Breast cancer wins dataset https:\/\/archive.ics.uci.edu\/ml\/datasets\/breast+cancer+wisconsin+(original) (accessed July 2019)."},{"key":"e_1_3_2_33_2","unstructured":"Pima Indian diabetes dataset https:\/\/www.kaggle.com\/uciml\/pima-indians-diabetes-database (accessed July 2019)."},{"key":"e_1_3_2_34_2","unstructured":"StatLog heart disease dataset http:\/\/archive.ics.uci.edu\/ml\/datasets\/statlog+(heart) (accessed July 2019)."},{"key":"e_1_3_2_35_2","unstructured":"New thyroid dataset https:\/\/sci2s.ugr.es\/keel\/dataset.php?cod=66#sub1 (accessed July 2019)."}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01655515211013708","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01655515211013708","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01655515211013708","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:09:18Z","timestamp":1777504158000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01655515211013708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10.1177\/01655515211013708"],"URL":"https:\/\/doi.org\/10.1177\/01655515211013708","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"value":"0165-5515","type":"print"},{"value":"1741-6485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,24]]}}}