Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Machine Learning for Health Informatics Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives

verfasst von : André Calero Valdez, Martina Ziefle, Katrien Verbert, Alexander Felfernig, Andreas Holzinger

Erschienen in: Machine Learning for Health Informatics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Recommender systems are a classical example for machine learning applications, however, they have not yet been used extensively in health informatics and medical scenarios. We argue that this is due to the specifics of benchmarking criteria in medical scenarios and the multitude of drastically differing end-user groups and the enormous context-complexity of the medical domain. Here both risk perceptions towards data security and privacy as well as trust in safe technical systems play a central and specific role, particularly in the clinical context. These aspects dominate acceptance of such systems. By using a Doctor-in-the-Loop approach some of these difficulties could be mitigated by combining both human expertise with computer efficiency. We provide a three-part research framework to access health recommender systems, suggesting to incorporate domain understanding, evaluation and specific methodology into the development process.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Convolutional Neural Networks Applied for Parkinson’s Disease Identification
Nächstes Kapitel Machine Learning for In Silico Modeling of Tumor Growth
Fußnoten
1
Netflix is an online movie provider.
 
Literatur
1.
Bruns, S., Valdez, A.C., Greven, C., Ziefle, M., Schroeder, U.: What should i read next? a personalized visual publication recommender system. In: Yamamoto, S. (ed.) HCI 2015. LNCS, vol. 9173, pp. 89–100. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-20618-9_​9 CrossRef
2.
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetCrossRef
3.
Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)CrossRef
4.
Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Springer Brain Inform. (BRIN) 3(2), 119–131 (2016)CrossRef
5.
Holzinger, A., Plass, M., Holzinger, K., Crişan, G.C., Pintea, C.-M., Palade, V.: Towards interactive Machine Learning (iML): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 81–95. Springer, Heidelberg (2016). doi:10.​1007/​978-3-319-45507-5_​6 CrossRef
6.
Puthiya Parambath, S.A., Usunier, N., Grandvalet, Y.: A coverage-based approach to recommendation diversity on similarity graph. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 15–22. ACM (2016)
7.
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRef
8.
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRef
9.
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)CrossRef
10.
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefMATH
11.
12.
Tinghuai, M., Jinjuan, Z., Meili, T., Yuan, T., Abdullah, A.D., Mznah, A.R., Sungyoung, L.: Social network and tag sources based augmenting collaborative recommender system. IEICE Trans. Inf. Syst. 98(4), 902–910 (2015)
13.
O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 167–174. ACM (2005)
14.
Gao, P., Miao, H., Baras, J.S., Golbeck, J.: Star: semiring trust inference for trust-aware social recommenders. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 301–308. ACM (2016)
15.
Bansal, T., Belanger, D., McCallum, A.: Ask the gru: Multi-task learning for deep text recommendations. arXiv preprint arXiv:​1609.​02116 (2016)
16.
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198. ACM (2016)
17.
Koren, Y., Bell, R., Volinsky, C., et al.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRef
18.
Ge, H., Caverlee, J., Lu, H.: Taper: a contextual tensor-based approach for personalized expert recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 261–268. ACM (2016)
19.
Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 233–240. ACM (2016)
20.
Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010)
21.
Koren, Y.: The bellkor solution to the netflix grand prize. Netflix Prize Documentation 81, 1–10 (2009)
22.
Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation (2011)
23.
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 257–260. ACM (2010)
24.
Swearingen, K., Sinha, R.: Beyond algorithms: An hci perspective on recommender systems. In: ACM SIGIR 2001 Workshop on Recommender Systems, vol. 13, Citeseer, pp. 1–11 (2001)
25.
Knijnenburg, B.P., Reijmer, N.J., Willemsen, M.C.: Each to his own: How different users call for different interaction methods in recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 141–148. ACM, New York (2011)
26.
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user?s perspective: survey of the state of the art. User Model. User-Adap. Inter. 22(4–5), 317–355 (2012)CrossRef
27.
He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)CrossRef
28.
Calero Valdez, A., Özdemir, D., Yazdi, M.A., Schaar, A.K., Ziefle, M.: Orchestrating collaboration-using visual collaboration suggestion for steering of research clusters. Procedia Manufact. 3, 363–370 (2015)
29.
Calero Valdez, A., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 123–126. ACM, New York (2016)
30.
Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics and challenges. Int. J. Environ. Res. Public Health 11(3), 2580–2607 (2014)CrossRef
31.
Farrell, R.G., Danis, C.M., Ramakrishnan, S., Kellogg, W.A.: Intrapersonal retrospective recommendation: lifestyle change recommendations using stable patterns of personal behavior. In: Proceedings of the First International Workshop on Recommendation Technologies for Lifestyle Change (LIFESTYLE 2012), p. 24. Ireland, Citeseer, Dublin (2012)
32.
Roitman, H., Messika, Y., Tsimerman, Y., Maman, Y.: Increasing patient safety using explanation-driven personalized content recommendation. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 430–434. ACM (2010)
33.
Rokicki, M., Herder, E., Demidova, E.: Whats on my plate: towards recommending recipe variations for diabetes patients. In: Proceedings of UMAP 2015 (2015)
34.
Elsweiler, D., Harvey, M., Ludwig, B., Said, A.: Bringing the healthy into food recommenders. In: DRMS Workshop (2015)
35.
Fernandez-Luque, L., Karlsen, R., Vognild, L.K.: Challenges and opportunities of using recommender systems for personalized health education. In: MIE, pp. 903–907 (2009)
36.
Sezgin, E., Özkan, S.: A systematic literature review on health recommender systems. In: E-Health and Bioengineering Conference (EHB), pp. 1–4. IEEE (2013)
37.
Achananuparp, P., Weber, I.: Extracting food substitutes from food diary via distributional similarity. arXiv preprint arXiv:​1607.​08807 (2016)
38.
Freyne, J., Berkovsky, S.: Recommending food: reasoning on recipes and ingredients. In: Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 381–386. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-13470-8_​36 CrossRef
39.
Ge, M., Ricci, F., Massimo, D.: Health-aware food recommender system. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 333–334. ACM (2015)
40.
Harvey, M., Ludwig, B., Elsweiler, D.: Learning user tastes: a first step to generating healthy meal plans. In: First International Workshop on Recommendation Technologies for Lifestyle Change (lifestyle 2012), Citeseer, p. 18 (2012)
41.
Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 111–118. ACM (2010)
42.
Said, A., Tikk, D., Shi, Y., Larson, M., Stumpf, K., Cremonesi, P.: Recommender systems evaluation: A 3d benchmark. In: ACM RecSys 2012 Workshop on Recommendation Utility Evaluation: Beyond RMSE, Dublin, Ireland, pp. 21–23 (2012)
43.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)CrossRef
44.
Ziefle, M., Rocker, C., Holzinger, A.: Medical technology in smart homes: exploring the user’s perspective on privacy, intimacy and trust. In: 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops (COMPSACW), pp. 410–415. IEEE (2011)
45.
Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10), 4511–4515 (2010)CrossRef
46.
Beale, R.: Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and web browsing. Int. J. Hum. Comput. Stud. 65(5), 421–433 (2007)CrossRef
47.
Hors-Fraile, S., Benjumea, F.J.N., Hernández, L.C., Ruiz, F.O., Fernandez-Luque, L.: Design of two combined health recommender systems for tailoring messages in a smoking cessation app. arXiv preprint arXiv:​1608.​07192 (2016)
48.
Ekstrand, J.D., Ekstrand, M.D.: First do no harm: Considering and minimizing harm in recommender systems designed for engendering health. In: Engendering Health Workshop at the RecSys 2016 Conference (2016)
49.
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. (TOIT) 7(4), 23 (2007)CrossRef
50.
Grasgruber, P., Sebera, M., Hrazdira, E., Hrebickova, S., Cacek, J.: Food consumption and the actual statistics of cardiovascular diseases: an epidemiological comparison of 42 European countries. Food Nutr. Res. 60 (2016). doi:10.​3402/​fnr.​v60.​31694
51.
Spinuzzi, C.: The methodology of participatory design. Tech. Commun. 52(2), 163–174 (2005)
52.
Ekstrand, M.D., Willemsen, M.C.: Behaviorism is not enough: better recommendations through listening to users. In: Proceedings of the 10th ACM Conference on Recommender Systems. RecSys 2016, pp. 221–224. ACM, New York (2016)
53.
Barnes, S.B.: A privacy paradox: Social networking in the united states. First Monday 11(9) (2006)
54.
Martin, R.L.: The design of business: why design thinking is the next competitive advantage. Harvard Business Press (2009)
55.
56.
Aggarwal, C.C., Philip, S.Y.: A general survey of privacy-preserving data mining models and algorithms. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining, pp. 11–52. Springer, US (2008)CrossRef
57.
Ziefle, M., Halbey, J., Kowalewski, S.: Users willingness to share data on the internet: Perceived benefits and caveats. In: Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pp. 255–265 (2016)
58.
Kowalewski, S., Ziefle, M., Ziegeldorf, H., Wehrle, K.: Like us on facebook!-analyzing user preferences regarding privacy settings in germany. Procedia Manuf. 3, 815–822 (2015)CrossRef
59.
Akhter, H.S.: Privacy concern and online transactions: the impact of internet self-efficacy and internet involvement. J. Consum. Mark. 31(2), 118–125 (2014)MathSciNetCrossRef
60.
Fogel, J., Nehmad, E.: Internet social network communities: risk taking, trust, and privacy concerns. Comput. Hum. Behav. 25(1), 153–160 (2009)CrossRef
61.
Freestone, O., Mitchell, V.: Generation y attitudes towards e-ethics and internet-related misbehaviours. J. Bus. Ethics 54(2), 121–128 (2004)CrossRef
62.
Nissenbaum, H.: A contextual approach to privacy online. Daedalus 140(4), 32–48 (2011)CrossRef
63.
Wilkowska, W., Ziefle, M.: Privacy and data security in e-health: requirements from the users perspective. Health Inf. J. 18(3), 191–201 (2012)CrossRef
64.
Ziefle, M., Himmel, S., Wilkowska, W.: When your living space knows what you do: acceptance of medical home monitoring by different technologies. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 607–624. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-25364-5_​43 CrossRef
65.
66.
Lipkus, I.M., Hollands, J.: The visual communication of risk. J. National Cancer Inst. Monogr. 25, 149–163 (1998)
67.
Seipp, K., Ochoa, X., Gutiérrez, F., Verbert, K.: A research agenda for managing uncertainty in visual analytics. Mensch und Computer 2016-Workshopband (2016)
68.
Gigerenzer, G., Edwards, A.: Simple tools for understanding risks: from innumeracy to insight. BMJ. Brit. Med. J. 327, 741–744 (2003)CrossRef
69.
Sedlmair, M., Meyer, M., Munzner, T.: Design study methodology: reflections from the trenches and the stacks. IEEE Trans. Vis. Comput. Graph. 18(12), 2431–2440 (2012)CrossRef
70.
Calero Valdez, A., Bruns, S., Greven, C., Schroeder, U., Ziefle, M.: What do my colleagues know? dealing with cognitive complexity in organizations through visualizations. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2015. LNCS, vol. 9192, pp. 449–459. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-20609-7_​42 CrossRef
71.
Parra, D., Brusilovsky, P., Trattner, C.: See what you want to see: visual user-driven approach for hybrid recommendation. In: Proceedings of the 19th International Conference on Intelligent User Interfaces, pp. 235–240. ACM (2014)
72.
Calero Valdez, A., Brauner, P., Ziefle, M., Kuhlen, T.W., Sedlmair, M.: Human factors in information visualization and decision support systems. Mensch und Computer 2016-Workshopband (2016)
73.
Holzinger, A.: Interactive machine learning (iml). Informatik-Spektrum 39(1), 64–68 (2016)CrossRef
74.
75.
Malle, B., Kieseberg, P., Weippl, E., Holzinger, A.: The right to be forgotten: towards machine learning on perturbed knowledge bases. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 251–266. Springer, Heidelberg (2016). doi:10.​1007/​978-3-319-45507-5_​17 CrossRef
76.
Kieseberg, P., Weippl, E., Holzinger, A.: Trust for the doctor-in-the-loop. European Research Consortium for Informatics and Mathematics (ERCIM) News: Tackling Big Data in the Life Sciences 104(1), 32–33
77.
Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S.: Toward the next generation of recommender systems: applications and research challenges. In: Tsihrintzis, G.A. (ed.) Multimedia Services in Intelligent Environments, pp. 81–98. Springer, Switzerland (2013)CrossRef
78.
Rossetti, M., Stella, F., Zanker, M.: Contrasting offline and online results when evaluating recommendation algorithms. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 31–34. ACM (2016)
79.
Karumur, R.P., Nguyen, T.T., Konstan, J.A.: Exploring the value of personality in predicting rating behaviors: a study of category preferences on movielens. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 139–142. ACM (2016)
80.
Azaria, A., Hong, J.: Recommender systems with personality. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 207–210. ACM, New York (2016)
81.
Shiv, B., Carmon, Z., Ariely, D.: Placebo effects of marketing actions: consumers may get what they pay for. J. Mark. Res. 42(4), 383–393 (2005)CrossRef
82.
Laufer, P., Wagner, C., Flöck, F., Strohmaier, M.: Mining cross-cultural relations from wikipedia: a study of 31 european food cultures. In: Proceedings of the ACM Web Science Conference, p. 3. ACM (2015)
83.
Babitsch, B., Braun, T., Borde, T., David, M.: Doctor’s perception of doctor-patient relationships in emergency departments: what roles do gender and ethnicity play? BMC Health Serv. Res. 8(1), 1 (2008)CrossRef
84.
Knijnenburg, B.P., Sivakumar, S., Wilkinson, D.: Recommender systems for self-actualization. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 11–14. ACM, New York (2016)
Metadaten
Titel
Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives
verfasst von
André Calero Valdez
Martina Ziefle
Katrien Verbert
Alexander Felfernig
Andreas Holzinger
Copyright-Jahr
2016
Buch
Machine Learning for Health Informatics

Print ISBN: 978-3-319-50477-3

Electronic ISBN: 978-3-319-50478-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50478-0_20