{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T03:51:40Z","timestamp":1725853900901},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319318561"},{"type":"electronic","value":"9783319318585"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-31858-5_11","type":"book-chapter","created":{"date-parts":[[2016,4,20]],"date-time":"2016-04-20T16:19:23Z","timestamp":1461169163000},"page":"245-264","source":"Crossref","is-referenced-by-count":0,"title":["Relational Learning for Sustainable Health"],"prefix":"10.1007","author":[{"given":"Sriraam","family":"Natarajan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peggy L.","family":"Peissig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Page","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,4,21]]},"reference":[{"key":"11_CR1","series-title":"Information Science and Statistics","volume-title":"Pattern Recognition and Machine Learning","author":"C Bishop","year":"2006","unstructured":"Bishop, C.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, Secaucus (2006)"},{"issue":"1-2","key":"11_CR2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0004-3702(98)00034-4","volume":"101","author":"Hendrik Blockeel","year":"1998","unstructured":"Blockeel, H.: Top-down induction of first order logical decision trees. AI Commun. 12(1\u20132), 119\u2013120 (1999)","journal-title":"Artificial Intelligence"},{"issue":"1","key":"11_CR3","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neuroimage.2004.01.002","volume":"22","author":"K Chen","year":"2004","unstructured":"Chen, K., Reiman, E.M., Alexander, G.E., Bandy, D., Renaut, R., Crum, W.R., Fox, N.C., Rossor, M.N.: An automated algorithm for the computation of brain volume change from sequential mris using an iterative principal component analysis and its evaluation for the assessment of whole-brain atrophy rates in patients with probable Alzheimer\u2019s disease. Neuroimage 22(1), 134\u2013143 (2004)","journal-title":"Neuroimage"},{"unstructured":"Craven, M., Shavlik, J.: Extracting tree-structured representations of trained networks. In: NIPS, pp. 24\u201330 (1996)","key":"11_CR4"},{"key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods. Cambridge University Press, New York (2000)"},{"key":"11_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An Introduction to Support Vector Machines and Other Kernel-based Learning Methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)"},{"issue":"1","key":"11_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"A. P. Dempster","year":"1977","unstructured":"Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B. 39(1), 1\u201338 (1977)","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1056\/NEJMoa072100","volume":"358","author":"R Detrano","year":"2008","unstructured":"Detrano, R., Guerci, A.D., Carr, J.J., et al.: Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N. Engl. J. Med. 358, 1338\u20131345 (2008)","journal-title":"N. Engl. J. Med."},{"doi-asserted-by":"crossref","unstructured":"Dietterich, T.G., Ashenfelter, A., Bulatov, Y.: Training conditional random fields via gradient tree boosting. In: ICML (2004)","key":"11_CR9","DOI":"10.1145\/1015330.1015428"},{"key":"11_CR10","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01549-6","volume-title":"Markov Logic: An Interface Layer for AI","author":"P Domingos","year":"2009","unstructured":"Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for AI. Morgan & Claypool, San Rafael (2009)"},{"unstructured":"Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML, pp. 148\u2013156 (1996)","key":"11_CR11"},{"issue":"5","key":"11_CR12","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"Jerome H. Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 189\u20131232 (2001)","journal-title":"The Annals of Statistics"},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/j.patcog.2011.01.017","volume":"44","author":"M Galar","year":"2011","unstructured":"Galar, M., Fern\u00e1ndez, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44, 1761\u20131776 (2011)","journal-title":"Pattern Recogn."},{"key":"11_CR14","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/978-3-662-04599-2_13","volume-title":"Relational Data Mining","author":"Lise Getoor","year":"2001","unstructured":"Getoor, L., Friedman, N., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining (2001)"},{"doi-asserted-by":"crossref","unstructured":"Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)","key":"11_CR15","DOI":"10.7551\/mitpress\/7432.001.0001"},{"key":"11_CR16","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/3-540-60112-0_25","volume-title":"Symbolic and Quantitative Approaches to Reasoning and Uncertainty","author":"Sabine Glesner","year":"1995","unstructured":"Glesner, S., Koller, D.: Constructing flexible dynamic belief networks from first-order probabilistic knowledge bases. In: Froidevaux, C., Kohlas, J. (eds.) Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU\u201995), pp. 217\u2013226. Springer, Berlin (1995)"},{"issue":"9","key":"11_CR17","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1001\/jama.289.9.1107","volume":"289","author":"Jerry H. Gurwitz","year":"2003","unstructured":"Gurwitz, J.H., Field, T.S., Harrold, L.R., Rothschild, J., Debellis, K., Seger, A.C., Cadoret, C., Fish, L.S., Garber, L., Kelleher, M., Bates, D.W.: Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA 289, 1107\u20131116 (2003)","journal-title":"JAMA"},{"doi-asserted-by":"crossref","unstructured":"Gutmann, B., Kersting, K.: Tildecrf: conditional random fields for logical sequences. In: ECML (2006)","key":"11_CR18","DOI":"10.1007\/11871842_20"},{"issue":"3","key":"11_CR19","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TKDE.2005.50","volume":"17","author":"Jin Huang","year":"2005","unstructured":"Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299\u2013310 (2005)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"unstructured":"John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338\u2013345. Morgan Kaufmann (1995)","key":"11_CR20"},{"doi-asserted-by":"crossref","unstructured":"Khot, T., Natarajan, S., Kersting, K., Shavlik, J.: Learning markov logic networks via functional gradient boosting. In: ICDM (2011)","key":"11_CR21","DOI":"10.1109\/ICDM.2011.87"},{"doi-asserted-by":"crossref","unstructured":"Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Souli\u00e9, F.F., H\u00e9rault, J. (eds) Neurocomputing: Algorithms, Architectures and Applications, vol. F68, pp. 41\u201350. Springer (1990)","key":"11_CR22","DOI":"10.1007\/978-3-642-76153-9_5"},{"unstructured":"Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI), pp. 302\u2013313, (1997). Winner of the Best Student Paper Award","key":"11_CR23"},{"key":"11_CR24","series-title":"Ellis Horwood Series in Artificial Intelligence","volume-title":"Inductive Logic Programming\u2014Techniques and Applications","author":"N Lavrac","year":"1994","unstructured":"Lavrac, N., Dzeroski, S.: Inductive Logic Programming\u2014Techniques and Applications. Ellis Horwood Series in Artificial Intelligence. Ellis Horwood, New York (1994)"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1001\/jama.279.15.1200","volume":"279","author":"J Lazarou","year":"1998","unstructured":"Lazarou, J., Pomeranz, B.H., Corey, P.N.: Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279, 1200\u20131205 (1998)","journal-title":"JAMA"},{"issue":"5","key":"11_CR26","doi-asserted-by":"publisher","first-page":"529","DOI":"10.2217\/17410541.5.5.529","volume":"5","author":"CA McCarty","year":"2008","unstructured":"McCarty, C.A., Peissig, P., Caldwell, M.D., Wilke, R.A.: The marshfield clinic personalized medicine research project: 2008 scientific update and lessons learned in the first 6 years. Personalized Med. 5(5), 529\u2013542 (2008)","journal-title":"Personalized Med."},{"issue":"1","key":"11_CR27","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1517\/17410541.2.1.49","volume":"2","author":"CA McCarty","year":"2005","unstructured":"McCarty, C.A., Wilke, R.A., Giampietro, P.F., Wesbrook, S.D., Caldwell, M.D.: Marshfield clinic personalized medicine research project (pmrp): design, methods and recruitment for a large population-based biobank. Personalized Med. 2(1), 49\u201379 (2005)","journal-title":"Personalized Med."},{"key":"11_CR28","volume-title":"Machine Learning","author":"T Mitchell","year":"1997","unstructured":"Mitchell, T.: Machine Learning, 1st edn. McGraw-Hill Inc., New York (1997)","edition":"1"},{"unstructured":"Murphy, K.: Machine Learning: A Probabilistic Perspective. MIT Press (2012)","key":"11_CR29"},{"doi-asserted-by":"crossref","unstructured":"Natarajan, S., Joshi, S., Saha, B., Edwards, A., Khot, T., Moody, E., Kersting, K., Whitlow, C., Maldjian, J.: A machine learning pipeline for three-way classification of alzheimer patients from structural magnetic resonance images of the brain. In: IEEE Conference on Machine Learning and Applications (ICMLA) (2012)","key":"11_CR30","DOI":"10.1109\/ICMLA.2012.42"},{"doi-asserted-by":"crossref","unstructured":"Natarajan, S., Joshi, S., Saha, B., Edwards, A., Khot, T., Moody, E., Kersting, K., Whitlow, C., Maldjian, J.: Relational learning helps in three-way classification of alzheimer patients from structural magnetic resonance images of the brain. Int. J. Mach. Learn. Cybern. (2013)","key":"11_CR31","DOI":"10.1007\/s13042-013-0161-9"},{"unstructured":"Natarajan, S., Joshi, S., Tadepalli, P., Kersting, K., Shavlik, J.: Imitation learning in relational domains: a functional-gradient boosting approach. In: IJCAI, pp. 1414\u20131420 (2011)","key":"11_CR32"},{"doi-asserted-by":"crossref","unstructured":"Natarajan, S., Kersting, K., Ip, E., Jacobs, D., Carr, J.: Early prediction of coronary artery calcification levels using machine learning. In: Innovative Appl. AI (2013)","key":"11_CR33","DOI":"10.1609\/aaai.v27i2.19001"},{"doi-asserted-by":"crossref","unstructured":"Natarajan, S., Khot, T., Kersting, K., Gutmann, B., Shavlik, J.: Gradient-based boosting for statistical relational learning. Relational Depend. Netw. Case MLJ (2012)","key":"11_CR34","DOI":"10.1007\/s10994-011-5244-9"},{"unstructured":"Page, D., Natarajan, S., Costa, V.S., Peissig, P., Barnard, A., Caldwell, M.: Identifying adverse drug events from multi-relational healthcare data. In: AAAI (2012)","key":"11_CR35"},{"unstructured":"Quinlan, J.: C4.5: Programs for Machine Learning (1993)","key":"11_CR36"},{"unstructured":"Quinlan, J.R.: Bagging, boosting, and c4.5. In: AAAI\/IAAI, vol. 1, pp. 725\u2013730 (1996)","key":"11_CR37"},{"doi-asserted-by":"crossref","unstructured":"De Raedt, L.: Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies). Springer, New York (2008)","key":"11_CR38","DOI":"10.1007\/978-3-540-68856-3"},{"unstructured":"Roge, V.L., Go, A.S., et al., Lloyd-Jones, D.M.: Heart disease and stroke statistics-2011 update: a report from the american heart association. Circulation 123, e18\u2013e209 (2011)","key":"11_CR39"},{"doi-asserted-by":"crossref","unstructured":"Schapire, R., Freund, Y.: Boosting: Foundations and Algorithms. The MIT Press (2012)","key":"11_CR40","DOI":"10.7551\/mitpress\/8291.001.0001"},{"unstructured":"Srinivasan, A.: The Aleph Manual (2004)","key":"11_CR41"},{"doi-asserted-by":"crossref","unstructured":"Sun, L., Patel, R., Liu, J., Chen, K., Wu, T., Li, J., Reiman, E., Ye, J.: Mining brain region connectivity for alzheimer\u2019s disease study via sparse inverse covariance estimation. In: KDD (2009)","key":"11_CR42","DOI":"10.1145\/1557019.1557162"},{"issue":"6","key":"11_CR43","doi-asserted-by":"publisher","first-page":"e1000100","DOI":"10.1371\/journal.pcbi.1000100","volume":"4","author":"K Supekar","year":"2008","unstructured":"Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M.D.: Network analysis of intrinsic functional brain connectivity in Alzheimer\u2019s disease. PLoS Comput. Biol. 4(6), e1000100 (2008)","journal-title":"PLoS Comput. Biol."},{"doi-asserted-by":"crossref","unstructured":"Weiss, J., Natarajan, S., Peissig, P., McCarty, C., Page, D.: Statistical relational learning to predict primary myocardial infarction from electronic health records. In: Innovative Applications in AI (2012)","key":"11_CR44","DOI":"10.1609\/aimag.v33i4.2438"},{"doi-asserted-by":"crossref","unstructured":"Weiss, J., Natarajan, S., Peissig, P., McCarty, C., Page, D.: Statistical relational learning to predict primary myocardial infarction from electronic health records. In: AI Magazine (2012)","key":"11_CR45","DOI":"10.1609\/aimag.v33i4.2438"},{"unstructured":"Jieping, Y., Gene, A., Eric, R., Kewei, C., Wu, T., Jing, L., Zheng, Z., Rinkal, P., Min, B., Ravi, J., et al.: Heterogeneous data fusion for alzheimer\u2019s disease study. In: KDD, p. 1025 (2008)","key":"11_CR46"}],"container-title":["Studies in Computational Intelligence","Computational Sustainability"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-31858-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T21:45:19Z","timestamp":1718487919000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-31858-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319318561","9783319318585"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-31858-5_11","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2016]]}}}