{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:42:19Z","timestamp":1770910939704,"version":"3.50.1"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:p>\n            Healthcare systems around the world are facing the challenge of information overload in caring for patients in an affordable, safe and high-quality manner in a system with limited healthcare resources and increasing costs. To alleviate this problem, we develop an integrative healthcare analytics system called\n            <jats:bold>GEMINI<\/jats:bold>\n            which allows point of care analytics for doctors where real-time usable and relevant information of their patients are required through the questions they asked about the patients they are caring for.\n            <jats:bold>GEMINI<\/jats:bold>\n            extracts data of each patient from various data sources and stores them as information in a\n            <jats:italic>patient profile graph.<\/jats:italic>\n            The data sources are complex and varied consisting of both structured data (such as, patients' demographic data, laboratory results and medications) and unstructured data (such as, doctors' notes). Hence, the patient profile graph provides a holistic and comprehensive information of patients' healthcare profile, from which\n            <jats:bold>GEMINI<\/jats:bold>\n            can infer implicit information useful for administrative and clinical purposes, and extract relevant information for performing predictive analytics. At the core,\n            <jats:bold>GEMINI<\/jats:bold>\n            keeps interacting with the healthcare professionals as part of a feedback loop to gather, infer, ascertain and enhance the self-learning knowledge base. We present a case study on using\n            <jats:bold>GEMINI<\/jats:bold>\n            to predict the risk of unplanned patient readmissions.\n          <\/jats:p>","DOI":"10.14778\/2733004.2733081","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"1766-1771","source":"Crossref","is-referenced-by-count":26,"title":["GEMINI"],"prefix":"10.14778","volume":"7","author":[{"given":"Zheng Jye","family":"Ling","sequence":"first","affiliation":[{"name":"National University Health System"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quoc Trung","family":"Tran","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ju","family":"Fan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerald C. H.","family":"Koh","sequence":"additional","affiliation":[{"name":"National University Health System"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi","family":"Nguyen","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuen Seng","family":"Tan","sequence":"additional","affiliation":[{"name":"National University Health System"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James W. L.","family":"Yip","sequence":"additional","affiliation":[{"name":"National University Health System"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Ibm big data for healthcare. http:\/\/www.ibm.com.  Ibm big data for healthcare. http:\/\/www.ibm.com."},{"key":"e_1_2_1_2_1","unstructured":"Unified medical language system. http:\/\/www.nlm.nih.gov\/research\/umls\/.  Unified medical language system. http:\/\/www.nlm.nih.gov\/research\/umls\/."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-011-1663-3"},{"issue":"2","key":"e_1_2_1_4_1","first-page":"161","article-title":"A general natural-language text processor for clinical radiology","volume":"1","author":"Friedman C.","year":"1994","unstructured":"C. Friedman , P. O. Alderson , J. H. Austin , J. J. Cimino , and S. B. Johnson . A general natural-language text processor for clinical radiology . JAMIA , 1 ( 2 ): 161 -- 174 , 1994 . C. Friedman, P. O. Alderson, J. H. Austin, J. J. Cimino, and S. B. Johnson. A general natural-language text processor for clinical radiology. JAMIA, 1(2):161--174, 1994.","journal-title":"JAMIA"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732286.2732291"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1795555"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2674026.2674032"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2013.2282125"},{"issue":"5","key":"e_1_2_1_10_1","first-page":"507","article-title":"Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications","volume":"17","author":"Savova G. K.","year":"2010","unstructured":"G. K. Savova , J. J. Masanz , P. V. Ogren , J. Zheng , S. Sohn , K. K. Schuler , and C. G. Chute . Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications . JAMIA , 17 ( 5 ): 507 -- 513 , 2010 . G. K. Savova, J. J. Masanz, P. V. Ogren, J. Zheng, S. Sohn, K. K. Schuler, and C. G. Chute. Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. JAMIA, 17(5):507--513, 2010.","journal-title":"JAMIA"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2733004.2733081","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:44:51Z","timestamp":1672220691000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2733004.2733081"}},"subtitle":["an integrative healthcare analytics system"],"short-title":[],"issued":{"date-parts":[[2014,8]]},"references-count":10,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2014,8]]}},"alternative-id":["10.14778\/2733004.2733081"],"URL":"https:\/\/doi.org\/10.14778\/2733004.2733081","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2014,8]]}}}