{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:28:09Z","timestamp":1781018889791,"version":"3.54.1"},"reference-count":42,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2011,5,19]],"date-time":"2011-05-19T00:00:00Z","timestamp":1305763200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Prim Health Care Res Dev"],"published-print":{"date-parts":[[2011,10]]},"abstract":"<jats:sec id=\"S1463423611000089_abs1\" sec-type=\"general\">\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To introduce systems biology as a conceptual framework for research\n                            in family medicine, based on empirical data from a case study on the\n                            prediction of influenza vaccination outcomes. This concept is primarily\n                            oriented towards planning preventive interventions and includes\n                            systematic data recording, a multi-step research protocol and predictive\n                            modelling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1463423611000089_abs2\" sec-type=\"general\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Factors known to affect responses to influenza vaccination include\n                            older age, past exposure to influenza viruses, and chronic diseases;\n                            however, constructing useful prediction models remains a challenge,\n                            because of the need to identify health parameters that are appropriate\n                            for general use in modelling patients\u2019 responses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1463423611000089_abs3\" sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The sample consisted of 93 patients aged 50\u201389 years (median 69),\n                            with multiple medical conditions, who were vaccinated against influenza.\n                            Literature searches identified potentially predictive health-related\n                            parameters, including age, gender, diagnoses of the main chronic ageing\n                            diseases, anthropometric measures, and haematological and biochemical\n                            tests. By applying data mining algorithms, patterns were identified in\n                            the data set. Candidate health parameters, selected in this way, were\n                            then combined with information on past influenza virus exposure to build\n                            the prediction model using logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1463423611000089_abs4\" sec-type=\"general\">\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>A highly significant prediction model was obtained, indicating that\n                            by using a systems biology approach it is possible to answer unresolved\n                            complex medical uncertainties. Adopting this systems biology approach\n                            can be expected to be useful in identifying the most appropriate target\n                            groups for other preventive programmes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s1463423611000089","type":"journal-article","created":{"date-parts":[[2011,5,19]],"date-time":"2011-05-19T11:18:55Z","timestamp":1305803935000},"page":"310-321","source":"Crossref","is-referenced-by-count":6,"title":["Systems biology as a conceptual framework for research in family\n                        medicine; use in predicting response to influenza vaccination"],"prefix":"10.1017","volume":"12","author":[{"given":"Ljiljana","family":"Majnari\u0107-Trtica","sequence":"first","affiliation":[{"name":"University of Osijek"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Branko","family":"Vitale","sequence":"additional","affiliation":[{"name":"Institute Ru\u0111er Bo\u0161kovi\u0107"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2011,5,19]]},"reference":[{"key":"S1463423611000089_manual_ref-6","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afg109"},{"key":"S1463423611000089_manual_ref-15","doi-asserted-by":"publisher","DOI":"10.1016\/j.virusres.2004.02.025"},{"key":"S1463423611000089_manual_ref-34","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(97)06316-2"},{"key":"S1463423611000089_manual_ref-3","doi-asserted-by":"publisher","DOI":"10.1002\/9780470755839"},{"key":"S1463423611000089_manual_ref-21","doi-asserted-by":"publisher","DOI":"10.1016\/0047-6374(84)90152-0"},{"key":"S1463423611000089_manual_ref-4","doi-asserted-by":"publisher","DOI":"10.1086\/313947"},{"key":"S1463423611000089_manual_ref-14","doi-asserted-by":"publisher","DOI":"10.1016\/0264-410X(89)90190-4"},{"key":"S1463423611000089_manual_ref-10","volume-title":"Data minig server","author":"Gamberger","year":"2001"},{"key":"S1463423611000089_manual_ref-37","doi-asserted-by":"publisher","DOI":"10.1016\/S0264-410X(03)00309-8"},{"key":"S1463423611000089_manual_ref-23","first-page":"5","article-title":"Trends and challenges in preventive medicine in European Union countries. 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