uef.fi
PUBLICATIONS OF
THE UNIVERSITY OF EASTERN FINLAND Dissertations in Forestry and Natural Sciences
Dissertations in Forestry and Natural Sciences
DISSERTATIONS | ADEKUNLE OLUSEYI AFOLABI | REAL-TIME RECOMMENDATION SYSTEMS IN HEALTHCAR
ADEKUNLE OLUSEYI AFOLABI
REAL-TIME RECOMMENDATION SYSTEMS IN HEALTHCARE PUBLICATIONS OF
THE UNIVERSITY OF EASTERN FINLAND
Current recommendation systems in healthcare are useful in providing recommendations on various care issues such
as general health and remote monitoring.
However, meeting the modern care needs of the elderly and people living with chronic diseases requires implementing designs that will allow corrective, preventive and predictive
recommendations using modern technologies.
This thesis presents designs that will enhance effectiveness of such systems and usefulness of
their recommendations.
ADEKUNLE OLUSEYI AFOLABI
REAL-TIME RECOMMENDATION SYSTEMS IN HEALTHCARE
DESIGNING FOR AGED AND CHRONICALLY ILL PEOPLE
Adekunle Oluseyi Afolabi
REAL-TIME RECOMMENDATION SYSTEMS IN HEALTHCARE
DESIGNING FOR AGED AND CHRONICALLY ILL PEOPLE
Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences
No 347
University of Eastern Finland Kuopio
2019
Academic dissertation
To be presented by permission of the Faculty of Science and Forestry for public examination in the Auditorium SN201 in the Snellmania Building at the University of Eastern Finland, Kuopio, on September 6,
2019, at 12 o’clock noon
Grano Oy Jyväskylä, 2019 Editor: Matti Tedre
Distribution: University of Eastern Finland / Sales of publications www.uef.fi/kirjasto
ISBN: 978-952-61-3144-3 (Print) ISBN: 978-952-61-3145-0 (PDF)
ISSNL: 1798-5668 ISSN: 1798-5668 ISSN: 1798-5676 (PDF)
Author’s address:
Supervisors:
Reviewers:
Opponent:
Adekunle Oluseyi Afolabi University of Eastern Finland School of Computing
P.O. Box 1627
70211 KUOPIO, FINLAND email: adekunle.afolabi@uef.fi
Professor Pekka Toivanen, D.Sc (Tech.) University of Eastern Finland
School of Computing P.O. Box 1627
70211 KUOPIO, FINLAND email: pekka.toivanen@uef.fi Docent Juha Mykkänen, Ph.D.
National Institute for Health and Welfare Information Services, Operational Management P.O. Box 95
70701 KUOPIO, FINLAND Email: juha.mykkanen@thl.fi Professor Katrien Verbert, Ph.D.
Catholic University of Leuven Department of Computer Science Celestijnenlaan 200A - box 2402 B-3001 LEUVEN, BELGIUM
email: katrien.verbert@cs.kuleuven.be Professor Christoph Trattner, Ph.D.
University of Bergen
Depart. of Information Science and Media Studies P.O. Box 7800
5020 BERGEN, NORWAY email: christoph.trattner@uib.no Professor Minna Pikkarainen, Ph.D.
University of Oulu Pentti Kaiteran katu 1 FI-90014 OULU, FINLAND email: minna.Pikkarainen@oulu.fi
Afolabi, Adekunle Oluseyi
Real-Time Recommendation Systems in Healthcare Kuopio: University of Eastern Finland, 2019 Publications of the University of Eastern Finland Dissertations in Forestry and Natural Sciences 2019; 347 ISBN: 978-952-61-3144-3 (print)
ISSNL: 1798-5668 ISSN: 1798-5668
ISBN: 978-952-61-3145-0 (PDF) ISSN: 1798-5676
,
(PDF)ABSTRACT
This study presents further ways recommendation systems can be used in healthcare. Traditional recommendation systems in healthcare have proved useful in providing recommendations on various care issues such as general health and life style, quitting addictions, dietary advice among others. However, the primary focus of this study is getting the elderly and people living with chronic diseases to benefit better from recommendation systems because of the peculiarity of their conditions. The number of elderly and infirm people is expected to grow rapidly in developed countries in the years to come and most developing countries are likely to witness the same trend because of globalization. This will give rise to increasing costs of providing care for these valuable individuals in human and material terms.
The possibility of reducing these costs while still providing effective care can be achieved by implementing a design that will allow corrective, preventive and predictive recommendations in a digitally structured home. By means of this, a minimum level of independence can be achieved by the elderly and ailing individuals while there will be a drastic reduction in the costs of care both in the short and long terms. In addition, there will be wise use of limited resources and improved care-giving. For people living with chronic diseases, the requirements gathered indicated the desirable need for a design that will allow a shared- environment for interactions and recommendations interchange. In view of this, a prototype mobile app called Recommendations Sharing Community for the Aged and Chronically Ill People (ReSCAP) was designed, developed, implemented and tested. In addition, as a result of lack of harmonized set of metrics and criteria for evaluation of recommendation systems in healthcare, this study sought to produce one. Therefore, sets of metrics and criteria have been produced, harmonized, and categorized. Two harmonization perspectives are presented while four categorization sets are developed. The categorization framework is flexible and easy to use. Hopefully this effort serves as a leap towards a universally acceptable sets of harmonized metrics and criteria for evaluation of recommendation systems.
8
Universal Decimal Classification: 004.89, 004.5, 004.382.73, 004.382.77 National Library of Medicine Classification: W 26.5, WT 26.5
Library of Congress Subject Headings: Recommender systems (Information filtering); Mo- bile apps; Mobile computing; Application software; Intelligent agents (Computer software);
Personal communication service systems; Self-help devices for older people; Assistive com- puter technology; Older people; Chronically ill; Medical care; Self-care, Health; Real-time data processing; Evaluation; Software measurement
Yleinen suomalainen ontologia: suosittelujärjestelmät; mobiilisovellukset; sovellusohjelmat;
älykkäät agentit; älytekniikka; tietämysjärjestelmät; terveydenhoito; ikääntyneet; vanhukset;
kroonikot; reaaliaikaisuus; arviointi
ACKNOWLEDGEMENTS
This study was conducted at the University of Esatern Finland, School of Computing between 2014 – 2018. I appreciate my supervisor, Professor Pekka Toivanen, who was always there in good and difficult times and for giving me a great opportunity for self-realisation and dynamism of research. Thanks to Dr.
Mikko Korpela for playing a very crucial role at the start of this programme. He made me come and made me stay. Thanks to Dr. Juha Mykannen for inspiring me technically. Thanks to Dr. Keijo Haataja, Dr. Tuija Tiihonen and Vilma Vainikainen for their immense help and contributions. Big thanks to Professor Adesola Aderoumu, Professor Kehinde Taiwo, Professor Hettie Soriyan and all members of Staff of Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife Nigeria for their support and encouragement. Thanks to the TETFUND AST&D which provided funds for this programme and Obafemi Awolowo University, Ile-Ife for making it available. I appreciate University of Eastern Finland through the School of computing and Faculty of Forestry and Science for making available finalization grant towards the completion of this program. I have a lot of friends who made my stay in Finland enjoyable and remarkable, Bidemi Orebiyi, Henri Kolka, Nathaniel and Margaret Otitoju, Victor Awakan, Pheneas, Eunice and Nana Fosu, Henry Sordelund among others.
Aderemi Ajibola, Ayodeji and Mary Fayese, Mr. Paradise Akpobome, Segun and Bimbo Ojo, Benjamin Akinlalure, Samuel and Folake Ikotun, Kehinde Opele,
‘Nnem Oma’ Mrs. Eugenia Nwaogu and my mum were always there for me back home. All thanks to my precious jewel, Lilian Onyenachi and my ever adorable daughters Amanda and Norah for their supports and sacrifcice. We walked the path from beginning to the end together. Thanks to all the people I have encountered in one way or the other through this journey.
Kuopio, 7th February 2019 Adekunle Oluseyi Afolabi
10
LIST OF ABBREVIATIONS
HCIS Healthcare Information System IoTs Internet of Things
ReSCAP Recommendations Sharing Community for Aged and Chronically Ill People
ReTReSCAP Real-Time Recommendations Sharing Community for Aged and Chronically Ill People
RS Recommendation System/Recommender System
HRS Healthcare Recommendation System/Healthcare Recommender System
AI Artificial Inteligence TLC Training Logic Concept TGT-M Trainee-Goal-Tutor and Means EHR Electronic Health Record
ICT Information and Communication Technology HRS Health Recommendation System
12
LIST OF ORIGINAL PUBLICATIONS
This thesis is based on data presented in the following articles, referrred to by the Roman Numerals I-V.
I Afolabi, A. O., Toivanen, P., Haataja, K., & Mykkänen, J. (2015). Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender Systems: Lessons Learned and a Novel Proposal.
International Journal of Healthcare Information Systems and Informatics (IJHISI), 10(4): 1-21
II Afolabi, A. O., Toivanen, P. (2018). Recommender Systems in Healthcare:
Towards Practical implementation of Real-Time Recommendations to Meet the Needs of Modern Caregiving. In Handbook of Research on Emerging
Perspectives on Healthcare Information Systems and Informatics (pp. 323-346). IGI Global
III Afolabi, A. O., Toivanen, P. (2020). Harmonization and Categorization of metrics and criteria for evaluation of Recommender Systems in Healthcare from dual perspectives. International Journal of E-Health and Medical Communications (IJEHMC) Vol. 11, No. 1, Article 5 (in print)
IV Afolabi A.O., Toivanen P. (2019). Designing an Integrated Platform for Real-
V
Time Recommendations Sharing among the Aged and People Living with Cancer. In proceedings of 21st International Conference on Information Systems Development, London UK, April 24-25, Part XV, pp. 1463-1467 Afolabi, A. O., Toivanen, P. (2019). Improving the Design of a
Recommendation System Using Evaluation Criteria and Metrics as a Guide.
Journal of Systems and Information Technology. (Accepted- DOI: 10.1108/
JSIT-01-2019-0019)
14
AUTHOR’S CONTRIBUTION
I) Author conducted the literature search and did the systematic review of the literature on recommender systems in Healthcare. The author also did the analysis, observed the gaps and proposed the solutions.
II) Expanding on the issues arising from the initial review of literature on recommender systems in healtcare, the author did the design of both the simplified and expanded architecture for integration of real-time
recommendation system into home care system.
III) The author set up and implemented the process for identifying, harmonizing and categorizing metrics and criteria commonly used in the evaluation of recommendation systems in healthcare. Different views and categorization framework presented were done by the author.
IV) The author conducted the survey-style requirements gathering and analysis.
The design of the entire architecture and the mobile app, Recommendations Sharing Community for the Aged and the Chronically Ill People (ReSCAP), were done by the author.
V) The idea on the use of evaluation criteria and metrics for strengthening the design of recommendation systems, in order to enhance their usefulness and acceptability, was conceived by the author. The procedure and the design for testing the viability of this idea were also done by the author.
16
CONTENTS
ABSTRACT ... 7
ACKNOWLEDGEMENTS ... 9
1 INTRODUCTION ...19
2 HEALTHCARE INFORMATION SYSTEM ...23
2.1 Introduction ...23
2.2 Health Information Sharing ...24
2.3 Health Recommendations Sharing Community ...25
2.4 Challenges of Data Sharing in Healthcare ...27
2.5 The Internet of Things ...29
2.5.1 The Internet of Things and Healthcare ...29
2.5.2 The Internet of Things and Homecare Technologies ...31
3 RECOMMENDATION SYSTEMS IN HEALTHCARE ...33
3.1
I
ntroduction ...333.2 Overview of Health Recommendation Systems ...33
3.2.1 The Future Direction ...35
3.3 ISSUES ARISING FROM HRS ...35
3.3.1 Functional Categorization of Recommendation Systems ...36
3.3.2 Evaluation Metrics and Criteria ...36
3.3.3 Meeting Modern Care Needs with HRS ...37
3.4. Privacy and confidentiality Issues ...39
3.4.1. Confidentiality and Privacy-Ensuring Mechanism ...43
3.4.2 Consideration for Security Mechanism ...43
4 EVALUATION METRICS AND CRITERIA ...45
4.1
I
ntroduction ...454.2
E
valuation Issues ...474.3 Choice of Evaluation Mertics and Criteria ...48
4.3.1 Metrics used for Evaluation ...49
5 NOVEL PROPOSALS AND SOLUTIONS ...51
5.1 Introduction ...51
5.2 Real-Time Recommendattions in Healthcare ...51
5.2.1 IoTs as a Platform for Real-Time RS in Healthcare ...52
5.3. The Overall Architecture ...55
5.3.1 The Home ...55
5.3.2 Home Organization ...56
5.3.3 The Community ...58
5.4
R
ecommendations Sharing App (ReSCAP) ...605.4.1 Types of Real-Time Recommendations...63
5.4.2. Benefits of the System ...64
5.5
E
valuation Metrics and Criteria ...655.5.1 Relevant Metrics for Evaluation ...69
5.5.2 Relevant Criteria for Evaluation ...71
5.5.3 Harmonizing the Metrics ...72
5.5.4 Harmonizing the Criteria ...73
5.5.5 Harmonization Logical Perspectives: Layman Versus Statistics ...77
18
5.5.6 Categorization of Metrics and Criteria ... 80
5.5.7 Advantages of Harmonization and Categorization ... 83
5.6 Training Logic Requirements Analysis Concept ... 85
5.6.1 Application of TGT-M to our Research... 86
5.7 Limitations of Our Proposals and Solutions ... 89
6 CONCLUSION AND FUTURE WORK ... 91
7 BIBLIOGRAPHY ... 95
1 INTRODUCTION
In recent years, the world has experienced phenomenal growth in information available on the Internet with accompanied growth in the number of people search- ing for useful and helpful information. Producing high-quality recommendations and performing as many recommendations as possible per second for millions of users and items simultaneously are parts of intimidating challenges (Sarwar et al.
2001), for developers and users of recommendation systems in healthcare. Besides, the relevance of recommendations which often are user-defined is very important (Moreau et al. 2002) and can change when user’s interests, activities, and prefer- ences change. Recommendation systems are nowadays more than just systems for matching and predicting user preferences. They are used for producing individual- ized recommendations as output or guide the users in a personalized and useful way towards meeting their aims (Burke 2002). This is especially true for those hav- ing health challenges and the elderly. Key attributes of a recommendation system therefore are individualization, interesting, and usefulness (Burke 2002, Olmo &
Gaudioso 2008). That is the reason user-centered evaluation of recommendation systems has gained popularity, to determine to what extent a recommendation system is useful to the users (Pu et al. 2011, Knijnenburg et al. 2012). All these are seen from user’s perspective.
Traditional recommendation systems use the input data to predict potential preferences and interests of its users. Past evaluations of the user can be used as input data as well (Lu et al. 2012). A recommendation system collects information on the preferences of its users for a set of items using different sources of infor- mation for providing users with predictions and recommendations of items (Boba- dilla et al. 2013). A recommendation system is as important in healthcare as it is in commerce and other sectors. It has brought about many changes and created new possibilities in healthcare domain. The typical implementation of a recommenda- tion system known to many people is the search engine such as Google. The main characteristic of a recommendation system is that it can personalize interactions for each user. Personalization involves the design of a system that can infer the needs of each person and then satisfy those needs (Riecken 2000). In enhancing the use- fulness of recommendation systems, they have been developed to meet the needs of users in different domains by using different methods and algorithms (Herlocker et al. 1999, Schafer et al. 2017). However, identifying the best possible algorithm for each domain or use case has been a challenge; since there is no general agreement among researchers neither on the required attributes nor on the metrics for these attributes (Herlocker et al. 2004). Addressing this challenge is one of the thrusts of this study.
20
Research questions answered in this study are
RQ1. What are the current and expected future trends in healthcare recommen- dation systems?
RQ2. How can healthcare recommendation systems meet the need of modern care giving?
RQ3. In what ways can the aged and the chronically ill benefit from the fusion of modern technologies and recommendation systems?
In this study, three important objectives are in the focus, namely
(1) Designing a system that will provide adequate care for the elderly while liv- ing in the comfort of their homes allowing corrective, preventive and predictive recommendations,
(2) Designing a platform where people living with chronic diseases can share recommendations and receive personalized attention, and
(3) Producing a set of metrics and criteria that can be used to determine useful- ness of recommendation systems in healthcare.
With the expected growth in the number of old people in the coming decades and the attendant costs of providing health care along with required personnel (UNFPA 2018), the need for an alternative way of meeting the care needs of these people is urgent. Many researchers have pointed in the direction of home care driven by modern technologies (Taylor et al. 2008, Ten Haken et al. 2018, Tran et al. 2005, Andò et al. 2015). The implication is that most old people can live at home and still receive good care. In achieving this, most efforts have focused on medication reminders and use of sensors to monitor these people remotely. However, there is the need to go beyond these, come up with a design that will provide preventive, corrective, and predictive recommendations in real-time, and connect various stakeholders to enhance effective care giving. Although the design presented herein is heavily hardware-dependent and the initial costs of implementation may be high, the long- term benefits far outweigh the initial costs.
One good thing that a technological tool deployed in health domain can do is to meet the health needs of people for which it is designed. In doing so, the health needs of people living with chronic diseases were ascertained. The survey-style requirements gathering brought out the need for a community that will include patients with similar ailments along with their physicians and caregivers; up-to- date information about current treatment methods, drug discoveries, dietary ad- vice, fitness and lifestyle, future expectations about the diseases are the essential features a system that will cater to their needs must have. These informed the de- signed implemented in a prototype mobile app called Recommendation Sharing Community for the Aged and Chronically Ill People (ReSCAP).
Based on the findings from this study that most recommendation systems in healthcare were not evaluated because there are no standard set of metrics and criteria to do so besides lack of harmonization in the metrics used for evaluation, the need to develop one became urgent. As a result, a set of metrics and criteria for
evaluation of recommendation systems in healthcare has been developed, harmo- nized, and categorized. Nine metrics and eleven criteria were harmonized. In har- monizing them, two logical perspectives namely Layman and Statistics were pro- posed, with none better than the other. The choice of which to use depends on indi- viduals. However, for a better result, it is advisable that the two perspectives are considered. Besides, in categorizing metrics and criteria four priority sets have been proposed namely high priority, low priority, weak priority, and conditional high priority. The harmonized metrics and criteria can be used as a checklist during pre- requirements analysis and design. This has also been experimented in this study.
The flexibility and ease of use is obvious in the categorization framework produced.
The rest of the thesis is organized as follows. Chapter two introduces the concept of healthcare information systems along with health data sharing and its effects in healthcare delivery. Chapter three discusses the relevance of recommendation sys- tems in healthcare and the existing challenges. In chapter four, relevance of evalua- tion metrics and criteria in determining effectiveness of recommendation systems is discussed. Chapter five presents novel proposals and solutions. The conclusion and future work are presented in chapter six.
22
7 BIBLIOGRAPHY
Abbas, A., Bilal, K., Zhang, L., & Khan, S. U. 2015. A cloud-based health insurance plan recom- mendation system: A user centered approach. Future Generation Computer Systems 43: 99-109.
Adam M. Ross, Donna H. Rhodes, and Daniel E. Hastings. 2008. Adaptability, Flexibility, Modifiability and Scalability, and Robustness. Systems Engineering, 11: 242-262.
Adams, K. M. 2015. Adaptability, Flexibility, Modifiability and Scalability, and Robustness. In Nonfunctional Requirements in Systems Analysis and Design 169-182. Springer, Cham.
Adams, R. J., Sadasivam, R. S., Balakrishnan, K., Kinney, R. L., Houston, T. K., & Marlin, B. M.
2014. PERSPeCT: collaborative filtering for tailored health communications. In Proceedings of the 8th ACM Conference on Recommendation systems 329-332.
Adomavicius, G., & Tuzhilin, A. 2005. Toward the next generation of Recommendation systems:
A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17: 734-749.
Afolabi, A. O., & Toivanen, P. 2019. Integration of Recommendation Systems Into Connected Health for Effective Management of Chronic Diseases. IEEE Access, 7: 49201-49211
.
Afolabi, A. O., & Toivanen, P. 2018. Recommendation Systems in Healthcare: Towards Practical implementation of Real-Time Recommendations to Meet the Needs of Modern Caregiving.
In Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informat- ics IGI Global 323-346.
Afolabi, A. O., Toivanen, P., Haataja, K., & Mykkänen, J. 2015. Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommendation Systems:
Lessons Learned and a Novel Proposal. International Journal of Healthcare Information Systems and Informatics (IJHISI), 10: 1-21.
Agapito, G., Calabrese, B., Care, I., Falcone, D., Guzzi, P. H., Ielpo, N., Lamprinoudi, T., Milano, M., Simeoni, M.,& Cannataro, M. 2014. Profiling basic health information of tourists: towards a recommendation system for the adaptive delivery of medical certified nutrition contents. In High Performance Computing & Simulation (HPCS), 2014 International Conference on IEEE. 616- 620.
Ali SI, Amin MB, Kim S, Lee S 2018 A Hybrid Framework for a Comprehensive Physical Activity and Diet Recommendation System. International Conference on Smart Homes and Health Telematics, Springer, Cham, 101-109
Alian, S., Li, J., & Pandey, V. 2018. A Personalized Recommendation System to Support Diabetes Self-Management for American Indians. IEEE Access, 6: 73041-73051.
Anderson, J. G., & Aydin, C. E. 2005. Overview: Theoretical perspectives and methodologies for the evaluation of healthcare information systems. In Evaluating the organizational impact of healthcare information systems. Springer, New York, NY. 5-29
Andò, B., Lombardo, C. O., & Marletta, V. 2015. Smart homecare technologies for the visually impaired: recent advances. Smart Homecare Technol. TeleHealth, 3: 9-16.
Anelli, V. W., Di Noia, T., Lops, P., & Di Sciascio, E. 2017. Feature Factorization for top-n Recommendation: from item rating to features relevance. In Proceedings of the 1st Workshop on Intelligent Recommendation Systems by Knowledge Transfer & Learning co-located with ACM Con- ference on Recommendation Systems (RecSys 2017), Como, Italy 16-21..
Armentano, M. G., Christensen, I., & Schiaffino, S. 2015. Applying the technology acceptance model to evaluation of recommender systems. Polibits, 51: 73-79.
96
Avila-Vazquez, D., Lopez-Martinez, M., Maya, D., Olvera, X., Guzman, G., Torres, M., Quintero, R., & Quintana, I. 2014. Geospatial Recommendation system for the location of health ser- vices. In Information Systems and Technologies (CISTI), 2014 9th Iberian Conference on IEE. 1-4.
Bellogin, A., Castells, P., & Cantador, I. 2011. Precision-oriented evaluation of Recommendation systems: an algorithmic comparison. In Proceedings of the fifth ACM conference on Recommenda- tion systems ACM. 333-336.
Berg, M. 2001. Implementing information systems in health care organizations: myths and challenges. International journal of medical informatics, 64: 143-156.
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. 2013. Recommendation systems survey.
Knowledge-Based Systems, 46:109-132.
Böhmer, M., Ganev, L., & Krüger, A. 2013. Appfunnel: A framework for usage-centric evaluation of recommender systems that suggest mobile applications. In Proceedings of the 2013 interna- tional conference on Intelligent user interfaces , ACM, 267-276
Breese, J. S., Heckerman, D., & Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence Morgan Kaufmann Publishers Inc. 43-52.
Burbach, L., Nakayama, J., Plettenberg, N., Ziefle, M., & Valdez, A. C. 2018. User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms. In Proceedings of the 12th ACM Conference on Recommender Systems, 306-310
Burke, R. 2002. Hybrid Recommendation systems: Survey and experiments. User modeling and user-adapted interaction, 12: 331-370.
Caamares, R., & Castells, P. 2018. Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommendation Systems [C]. In ACM. 415-424.
Chai Z, Li Y, Han Y, Zhu S 2018 Recommendation system based on Singular Value Decomposi- tion and Multi-Objective Immune Optimization. IEEE Access.
Champiri, Z. D., Shahamiri, S. R., & Salim, S. S. B. 2015. A systematic review of scholar context- aware Recommendation systems. Expert Systems with Applications, 42:1743-1758.
Ciaramella, A., Cimino, M. G., Lazzerini, B., & Marcelloni, F. 2010. Using context history to personalize a resource Recommendation via a genetic algorithm. In Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on IEEE. 965-970.
Cremonesi, P., Garzotto, F., & Turrin, R. 2013. User-centric vs. system-centric evaluation of Recommendation systems. In IFIP Conference on Human-Computer Interaction Springer, Berlin, Heidelberg. 334-351.
Cremonesi, P., Garzotto, F., Negro, S., Papadopoulos, A. V., & Turrin, R. 2011. Looking for
“good” recommendations: A comparative evaluation of Recommendation systems. In IFIP Conference on Human-Computer Interaction Springer, Berlin, Heidelberg. 152-168.
Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. 2017. The impact of the hybrid platform of internet of things and cloud computing on healthcare sys- tems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Hu- manized Computing, 1-16.
de Souza, J. W., Alves, S. S., Rebouças, E. D. S., Almeida, J. S., & Rebouças Filho, P. P. 2018. A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis. Computational intelligence and neuroscience, 2018.
Del Olmo, F. H., & Gaudioso, E. 2008. Evaluation of Recommendation systems: A new approach.
Expert Systems with Applications, 35: 790-804.
Duan, L., Street, W. N., & Xu, E. 2011. Healthcare information systems: data mining methods in the creation of a clinical Recommendation system. Enterprise Information Systems, 5: 169-181.
Erlirianto, L. M., Ali, A. H. N., & Herdiyanti, A. 2015. The Implementation of the Human, Organization, and Technology–Fit (HOT–Fit) Framework to evaluate the Electronic Medical Record (EMR) System in a Hospital. Procedia Computer Science, 72: 580-587.
Epifania, F., & Cremonesi, P. 2012. User-centered evaluation of recommender systems with comparison between short and long profile. In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems , IEEE, 204-211
Espín, V., Hurtado, M. V., Noguera, M., & Benghazi, K. 2013. Semantic-Based Recommendation of Nutrition Diets for the Elderly from Agroalimentary Thesauri. In Flexible Query Answering Systems Springer Berlin Heidelberg. 471-482.
Fazeli, S., Drachsler, H., Bitter-Rijpkema, M., Brouns, F., Van der Vegt, W., & Sloep, P. B. 2018.
User-centric Evaluation of Recommendation Systems in Social Learning Platforms: Accuracy is Just the Tip of the Iceberg. IEEE Transactions on Learning Technologies, 11: 294-306..
Fernandez-Luque, L., Karlsen, R., & Vognild, L. K. 2009. Challenges and opportunities of using Recommendation systems for personalized health education. In MIE. 903-907.
Fichman, R. G., Kohli, R., & Krishnan, R. (Eds.). 2011. Editorial overview—the role of infor- mation systems in healthcare: current research and future trends. Information Systems Re- search, 22: 419-428.
Folino, F., & Pizzuti, C. 2010. A comorbidity-based recommendation engine for disease predic- tion. In Computer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on IEEE. 6-12.
Fong, A. C. M., Zhou, B., Hui, S. C., Hong, G. Y., & Do, T. A. 2011. Web content Recommenda- tion system based on consumer behavior modeling. IEEE Transactions on Consumer Electron- ics. 57: 962-969..
Frolov, E., & Oseledets, I. (2017). Tensor methods and Recommendation systems. Wiley Interdis- ciplinary Reviews: Data Mining and Knowledge Discovery, 7: e1201
Ge, M., Delgado-Battenfeld, C., & Jannach, D. 2010. Beyond accuracy: evaluating Recommenda- tion systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Rec- ommendation systems ACM. 257-260.
Gil, M., El Sherif, R., Pluye, M., Fung, B. C., Grad, R., & Pluye, P. 2011. Towards a Knowledge- Based Recommender System for Linking Electronic Patient Records With Continuing Medi- cal Education Information at the Point of Care. IEEE Access, 7:15955-15966.
Gilson, L. 2003. Trust and the development of health care as a social institution. Social science &
medicine, 56:7:1453-1468.
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35:61-70.
Guo, L., Jin, B., Yao, C., Yang, H., Huang, D., & Wang, F. 2016. Which doctor to trust: A recom- mender system for identifying the right doctors. Journal of medical Internet research, 18:7: 186.
Haux, R. (2006). Health information systems–past, present, future. International journal of medical informatics, 75:268-281.
He, C., Parra, D., & Verbert, K. 2016. Interactive Recommendation systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications.
56: 9-27
He, M., Wu, X., Zhang, J., & Dong, R. 2019. UP-TreeRec: Building dynamic user profiles tree for news recommendation. China Communications, 16:4: 219-233.
98
Herlocker, J. L., Konstan, J. A., & Riedl, J. 2000. Explaining collaborative filtering recommenda- tions. In Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM.
241-250.
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval ACM. 230-237.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. 2004. Evaluating collaborative filtering Recommendation systems. ACM Transactions on Information Systems (TOIS), 22: 5-53.
Hsu, M. H. 2011. Proposing a charting Recommendation system for second-language nurses.
Expert Systems with Applications, 38:9281-9286.
Husain, W., & Pheng, L. T. 2010. The development of personalized wellness therapy Recom- mendation system using hybrid case-based reasoning. In Computer Technology and Develop- ment (ICCTD), 2010 2nd International Conference on IEEE. 85-89.
Hussein, A. S., Omar, W. M., Li, X., & Ati, M. 2012. Efficient chronic disease diagnosis prediction and recommendation system. In Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on IEEE. 209-214.
Iqbal, M., Ghazanfar, M. A., Sattar, A., Maqsood, M., Khan, S., Mehmood, I., & Baik, S. W. 2019.
Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm. IEEE Access.
Jabeen, F., Hamid, Z., Akhunzada, A., Abdul, W., & Ghouzali, S. 2018. Trust and reputation management in healthcare systems: Taxonomy, requirements and open issues. IEEE Access, 6: 17246-17263.
Jung H, Chung K 2016 Knowledge-based dietary nutrition recommendation for obese manage- ment. Information Technology and Management, 17:1:29-42.
Kahara, T., Haataja, K., & Toivanen, P. 2013a. A novel recommendation system approach utilizing social network profiles. In Hybrid Intelligent Systems (HIS), 2013 13th International Conference on IEEE. 155-160.
Kahara, T., Haataja, K., & Toivanen, P. 2013b. Towards more accurate and intelligent recom- mendation systems. In Intelligent Systems Design and Applications (ISDA), 2013 13th Interna- tional Conference on IEEE. 165-171.
Kamishima, T., & Akaho, S. 2017. Considerations on Recommendation Independence for a Find- Good-Items Task. Workshop on Responsible Recommendation, Como, Italy
Katariya S, Bose J, Reddy MV, Sharma A, Tappashetty S 2018 A Personalized Health Recom- mendation System Based on Smartphone Calendar Events. In International Conference on Smart Homes and Health Telematics, Springer, Cham, 110-120
Kaur, H., Kumar, N., & Batra, S. 2018. An efficient multi-party scheme for privacy preserving collaborative filtering for healthcare recommender system. Future Generation Computer Sys- tems, 86: 297-307.
Kavu, T. D., Dube, K., Raeth, P. G., & Hapanyengwi, G. T. 2017. A Characterisation and Frame- work for User-Centric Factors in Evaluation Methods for Recommendation Systems. Interna- tional Journal of ICT Research in Africa and the Middle East (IJICTRAME), 6: 1-16.
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, (2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22:4-5:
441-504.
Komkhao, M., Lu, J., & Zhang, L. 2012. Determining pattern similarity in a medical Recommen- dation system. In Data and Knowledge Engineering Springer Berlin Heidelberg. 103-114.
Krawczyk, H., Knopa, R., Lipczynska, K., & Lipczynski, M. 2000. Web-based endoscopy Recommendation system-ERS. In parelec IEEE. 257.
Kulkarni, A., & Sathe, S. 2014. Healthcare applications of the Internet of Things: A Review.
International Journal of Computer Science and Information Technologies, 5:6229-32.
Lam, S., Frankowski, D., & Riedl, J. 2006. Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. Emerging trends in information and communication security, 14-29.
Li, Y. M., & Kao, C. P. 2009. TREPPS: A trust-based Recommendation system for peer production services. Expert systems with applications, 36:3263-3277.
Lin, Y., Jessurun, J., De Vries, B., & Timmermans, H. 2011. Motivate: Towards context-aware recommendation mobile system for healthy living. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on IEEE. 250-253.
Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., & Li, X. 2018. Efficient user profiling based intelligent travel recommender system for individual and group of users. Mobile Networks and Applications, 1-16.
López-Nores, M., Blanco-Fernández, Y., Pazos-Arias, J. J., & Díaz-Redondo, R. P. 2010. Property- based collaborative filtering: A new paradigm for semantics-based, health-aware Recom- mendation systems. In Semantic Media Adaptation and Personalization (SMAP), 2010 5th Inter- national Workshop on IEEE. 98-103.
López-Nores, M., Blanco-Fernández, Y., Pazos-Arias, J. J., García-Duque, J., & Martín-Vicente, M.
2011. Enhancing Recommendation Systems with Access to Electronic Health Records and Groups of Interest in Social Networks. In Signal-Image Technology and Internet-Based Systems (SITIS), Seventh International Conference on IEEE. 105-110.
Lü, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., & Zhou, T. 2012. Recommendation systems. Physics Reports, 519:1-49.
Lv, G., Hu, C., & Chen, S. 2016. Research on Recommendation system based on ontology and genetic algorithm. Neurocomputing, 187:92-97.
Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. 2018. A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82: 375-387.
Marinho, L. B., de Souza Junior, A. H., & Rebouças Filho, P. P. 2016. A new approach to Human Activity Recognition using Machine Learning techniques. In International Conference on Intel- ligent Systems Design and Applications Springer, Cham. 529-538.
McNee, S. M., Riedl, J., & Konstan, J. A. 2006. Making recommendations better: an analytic model for human-Recommendation interaction. In CHI'06 extended abstracts on Human factors in computing systems ACM. 1103-1108.
Mechanic, D., & Meyer, S. 2000. Concepts of trust among patients with serious illness. Social science & medicine, 5:5: 657-668.
Mohallick, I., De Moor, K., Özgöbek, Ö., & Gulla, J. A. 2018. Towards New Privacy Regulations in Europe: Users’ Privacy Perception in Recommender Systems. In International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, Springer, Cham, 319-330.
Mohammed, O., & Benlamri, R. 2014. Developing a Semantic Web Model for Medical Differen- tial Diagnosis Recommendation. Journal of medical systems, 38:1-18.
Moreau, L., Zaini, N., Zhou, J., Jennings, N. R., Wei, Y. Z., Hall, W., De Roure, D.,Gilchrist, I., O'Dell, M., Sigi, B., Berka, T., & Di Napoli, C. 2002. A market-based Recommendation sys-
100
tem. In The Fourth International Bi-Conference Workshop on Agent-Oriented Information Systems at AAMAS (AOIS). 50-67.
Moreira, M. W., Rodrigues, J. J., Kumar, N., Al-Muhtadi, J., & Korotaev, V. 2018. Nature-Inspired Algorithm for Training Multilayer Perceptron Networks in e-health Environments for High- Risk Pregnancy Care. Journal of medical systems, 42: 51.
Morrell, T. G., & Kerschberg, L. 2012. Personal health explorer: A semantic health recommenda- tion system. In Data Engineering Workshops (ICDEW), 2012 IEEE 28th International Conference on IEEE. 55-59.
Murakami T, Mori K, Orihara R 2007. Metrics for evaluating the serendipity of recommendation lists. In Annual conference of the Japanese society for artificial intelligence, Springer, Berlin, Heidelberg, 40-46
Narducci F, Lops P, Semeraro G 2017 Power to the patients: The HealthNetsocial network.
Information Systems, 71:111-122
Nathanson, T., Bitton, E., & Goldberg, K. 2007. Eigentaste 5.0: constant-time adaptability in a Recommendation system using item clustering. In Proceedings of the 2007 ACM conference on Recommendation systems ACM. 149-152.
Neto, E. C., Cortez, P. C., Cavalcante, T. S., Rodrigues, V. E., Reboucas Filho, P. P., & Holanda, M. A. 2016. 3D lung fissure segmentation in TC images based in textures. IEEE Latin America Transactions, 14: 254-258.
Niles, A. N., & O’Donovan, A. 2018. Personalizing Affective Stimuli Using a Recommender Algorithm: An Example with Threatening Words for Trauma Exposed Populations. Cognitive Therapy and Research, 42:6: 747-757.
O’Mahony, M. P., & Smyth, B. 2010. A classification-based review Recommendation. Knowledge- Based Systems, 23:323-329.
O'Mahony, M., Hurley, N., Kushmerick, N., & Silvestre, G. 2004. Collaborative recommendation:
A robustness analysis. ACM Transactions on Internet Technology (TOIT), 4:344-377.
Osei-Frimpong, K., Wilson, A., & Lemke, F. 2018. Patient co-creation activities in healthcare service delivery at the micro level: The influence of online access to healthcare information.
Technological Forecasting and Social Change, 126:14-27.
Pai, F. Y., & Huang, K. I. 2011. Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78: 650-660.
Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. 2012. A literature review and classification of Recommendation systems research. Expert Systems with Applications, 39:10059-10072.
Paraschakis, D. 2016. Recommender systems from an industrial and ethical perspective. In Proceedings of the 10th ACM Conference on Recommender Systems, ACM, 463-466
Paraschakis, D. (2017, May). Towards an ethical recommendation framework. In 2017 11th International Conference on Research Challenges in Information Science (RCIS), IEEE, 211-220 Pattaraintakorn, P., Zaverucha, G. M., & Cercone, N. 2007. Web based health Recommendation
system using rough sets, survival analysis and rule-based expert systems. In Rough Sets, Fuzzy Sets, Data Mining and Granular Computing Springer Berlin Heidelberg. 491-499.
Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering.
Artificial Intelligence Review, 13: 393-408.
Pérez-Gallardo, Y., Alor-Hernández, G., Cortes-Robles, G., & Rodríguez-González, A. 2013.
Collective intelligence as mechanism of medical diagnosis: The iPixel approach. Expert Sys- tems with Applications, 40:2726-2737.
Pu, P., Chen, L., & Hu, R. 2011. A user-centric evaluation framework for Recommendation systems. In Proceedings of the fifth ACM conference on Recommendation systems ACM. 157-164).
Raad, M.W., & Yang L.T. 2009. A Ubiquitous Smart Home for Elderly. Information Systems Frontiers, 11:529-536
Ragone, A., Tomeo, P., Magarelli, C., Di Noia, T., Palmonari, M., Maurino, A., & Di Sciascio, E.
2017. Schema-summarization in linked-data-based feature selection for Recommendation systems. In Proceedings of the Symposium on Applied Computing ACM. 330-335.
Ramalho, G. L. B., Rebouças Filho, P. P., Medeiros, F. N. S. D., & Cortez, P. C. 2014. Lung disease detection using feature extraction and extreme learning machine. Revista Brasileira de Engen- haria Biomédica, 30:207-214.
Rebouças Filho, P. P., Cortez, P. C., da Silva Barros, A. C., Albuquerque, V. H. C., & Tavares, J.
M. R. 2017. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Medical image analysis, 35:503-516.
Rebouças Filho, P. P., da Silva Barros, A. C., Ramalho, G. L., Pereira, C. R., Papa, J. P., de Albuquerque, V. H. C., & Tavares, J. M. R. 2017. Automated recognition of lung diseases in CT images based on the optimum-path forest classifier. Neural Computing and Applications, 1- 14.
Rebouças Filho, P. P., Rebouças, E. D. S., Marinho, L. B., Sarmento, R. M., Tavares, J. M. R., & de Albuquerque, V. H. C. 2017. Analysis of human tissue densities: A new approach to extract features from medical images. Pattern Recognition Letters, 94:211-218.
Rebouças Filho, P. P., Sarmento, R. M., Holanda, G. B., & de Alencar Lima, D. 2017. New approach to detect and classify stroke in skull CT images via analysis of brain tissue densi- ties. Computer methods and programs in biomedicine, 148:27-43.
Riecken, D. 2000. Personalized views of personalization. Communications of the ACM, 43:27-28.
Rivero-Rodriguez, A., Konstantinidis, S. T., Sanchez-Bocanegra, C. L., & Fernández-Luque, L.
(2013, June). A health information Recommendation system: Enriching YouTube health vid- eos with Medline Plus information by the use of SnomedCT terms. In Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on IEEE. 257-261.
Rodrigues, M. B., Da Nóbrega, R. V. M., Alves, S. S. A., Rebouças Filho, P. P., Duarte, J. B. F., Sangaiah, A. K., & De Albuquerque, V. H. C. 2018. Health of Things Algorithms for Malig- nancy Level Classification of Lung Nodules. IEEE Access, 6:18592-18601.
Russell, S., & Yoon, V. 2008. Applications of wavelet data reduction in a Recommendation system. Expert Systems with Applications, 34: 2316-2325.
Said, A., Tikk, D., Stumpf, K., Shi, Y., Larson, M., & Cremonesi, P. 2012. Recommendation Systems Evaluation: A 3D Benchmark. In RUE@ RecSys. 21-23.
Said, A., Fields, B., Jain, B. J., & Albayrak, S. 2013. User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm. In Proceedings of the 2013 conference on Computer supported cooperative work , ACM, 1399-1408
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. 2001. Item-based collaborative filtering recom- mendation algorithms. In Proceedings of the 10th international conference on World Wide Web ACM. 285-295.
Sethi N. K, M.Y. Melissa, A. Weathers, A. D. Wu, D. A. Evans. 2017. Sharing notes with patients:
A review of current practice and considerations for neurologists. Neurology: Clinical Practice 7:
188-188.
Schäfer, H., Hors-Fraile, S., Karumur, R. P., Calero Valdez, A., Said, A., Torkamaan, H., T. Ulmer
& Trattner, C. 2017. Towards health (aware) recommender systems. In Proceedings of the 2017 international conference on digital health,, ACM,157-161
Seaver, N. 2018. Captivating algorithms: Recommender systems as traps. Journal of Material Culture, 1-16
102
Sezgin, E., & Ozkan, S. 2013. A systematic literature review on Health Recommendation Systems. In E-Health and Bioengineering Conference (EHB), 2013 IEEE. 1-4.
Shafna, S., & Rajendran, V. V. 2017. Fuzzy ontology-based Recommendation system with diversification mechanism. In Intelligent Computing and Control (I2C2), 2017 International Con- ference on IEEE. 1-6
Shani, G., A. Gunawardana, 2011. Evaluating recommendation systems. In Recommendation systems handbook. Springer, Boston, MA. 257-297.
Shardanand, U., & Maes, P. 1995. Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM Press/Addison-Wesley Publishing Co. 210-217
Sligo, J., Gauld, R., Roberts, V., & Villa, L. 2017. A literature review for large-scale health information system project planning, implementation and evaluation. International journal of medical informatics, 97:86-97.
Song, I., & Marsh, N. V. 2012. Anonymous indexing of health conditions for a similarity measure. Information Technology in Biomedicine, IEEE Transactions on, 16:737-744.
Song, I., Dillon, D., Goh, T. J., & Sung, M. 2011. A health social network Recommendation system. In Agents in Principle, Agents in Practice. Springer Berlin Heidelberg. 361-372.
Swearingen, K., & Sinha, R. 2001. Beyond algorithms: An HCI perspective on Recommendation systems. In ACM SIGIR Workshop on Recommendation Systems. 13:1-11.
Taylor, C., & Dajani, L. 2008. The future of homecare systems in the context of the ubiquitous web and its related mobile technologies. In Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments ACM. 43.
Ten Haken, I., Allouch, S. B., & Van Harten, W. H. 2018. The use of advanced medical technolo- gies at home: a systematic review of the literature. BMC public health, 18:284.
Thong, N. T. 2015. HIFCF: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy Recommendation systems for medical diagnosis. Expert Systems With Applications, 42:3682-3701.
Toivanen, M., Häkkinen, H., Eerola, A., Korpela, M., & Mursu, A. 2004. Gathering, structuring and describing information needs in home care: a method for requirements exploration in a"
gray area". In Medinfo. 1398-1402.
Torrent-Fontbona, F. 2018. Adaptive basal insulin Recommendation system based on Kalman filter for type 1 diabetes. Expert Systems with Applications, 101:1-7.
Tran, T. H., Chao, P. C. P., Hsu, L. C., Chiu, C. W., & Kao, Y. H. 2015. Design and development of a new wireless fall detection module for homecare. Microsystem Technologies, 21:2737-2745.
Trewin, S. 2000. Knowledge-based Recommendation systems. Encyclopedia of Library and Information Science: 69: 180.
UNFPA (United Nations Population Fund). 2012. State of World Population 2018 “The Power of Choice: Reproductive Rights and the Demographic Transition. New York: UNFPA.
Valdez, A. C., Ziefle, M., Verbert, K., Felfernig, A., & Holzinger, A. 2016. Recommendation systems for health informatics: State-of-the-art and future perspectives. In Machine Learning for Health Informatics Springer, Cham. 391-414.
Valdez, A. C., & Ziefle, M. 2019. The users’ perspective on the privacy-utility trade-offs in health recommender systems. International Journal of Human-Computer Studies, 121: 108-121.
Vanesa Aciar, S., Serarols-Tarres, C., Royo-Vela, M., & De la Rosa i Esteva, J. L. 2007. Increasing effectiveness in e-commerce: recommendations applying intelligent agents. International Journal of Business and Systems Research, 1:81-97.
Vargas, S., & Castells, P. (2011, October). Rank and relevance in novelty and diversity metrics for Recommendation systems. In Proceedings of the fifth ACM conference on Recommendation sys- tems. ACM. 109-116.
Vartak, M., Huang, S., Siddiqui, T., Madden, S., & Parameswaran, A. 2017. Towards visualiza- tion recommendation systems. ACM SIGMOD Record, 45:34-39.
Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. 2012.
Context-aware Recommendation systems for learning: a survey and future challenges. Learn- ing Technologies, IEEE Transactions on, 5:318-335.
Verhaert, D., Nateghizad, M., & Erkin, Z. 2018. An Efficient Privacy-preserving Recommender System for e-Healthcare Systems. In 15th International Joint conference on e-Business and Telecommunications, 2: 188-199
Wei, Y. Z., Moreau, L., & Jennings, N. R. 2005. A market-based approach to Recommendation systems. ACM Transactions on Information Systems (TOIS), 23: 227-266.
Wiesner, M., & Pfeifer, D. 2010. Adapting Recommendation systems to the requirements of personal health record systems. In Proceedings of the 1st ACM International Health Informatics Symposium ACM. 410-414.
Wiesner, M., & Pfeifer, D. 2014. Health Recommendation systems: Concepts, requirements, technical basics and challenges. International journal of environmental research and public health, 11:2580-2607.
Wiesner, M., Rotter, S., & Pfeifer, D. 2011. Leveraging semantic networks for personalized content in health Recommendation systems. In Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on IEEE. 1-6.
Wilson, D., McLoughlin, E., O’Sullivan, D., & Bertolotto, M. 2006. Contextual media integration and recommendation for mobile medical diagnosis. In Adaptive Hypermedia and Adaptive Web- Based Systems. Springer Berlin Heidelberg. 333-337.
Wolfe, S. R., & Zhang, Y. 2010. Interaction and personalization of criteria in Recommendation systems. In International Conference on User Modeling, Adaptation, and Personalization. Springer, Berlin, Heidelberg. 183-194.
Wu, L., Liu, Q., Chen, E., Yuan, N. J., Guo, G., & Xie, X. 2016. Relevance meets coverage: A unified framework to generate diversified recommendations. ACM Transactions on Intelligent Systems and Technology (TIST), 7:39.
Wu, W., He, L., & Yang, J. 2012. Evaluating Recommendation systems. In Digital information management (icdim), 2012 seventh international conference on IEEE. 56-61.
Xu, Y., Yang, Q., & Chu, D. 2018. Exploring timeliness for accurate recommendation in location- based social networks. Mathematical Foundations of Computing, 1:11-48.
Yang S, Zhou P, Duan K, Hossain M S, Alhamid MF 2017 emHealth: towards emotion health through depression prediction and intelligent health recommender system. Mobile Networks and Applications, 1-11.
Yang, L., Bagdasaryan, E., Gruenstein, J., Hsieh, C. K., & Estrin, D. 2018. OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining ACM. 664-672.
Zaíane, O. R. 2002. Building a Recommendation agent for e-learning systems. In Computers in Education. Proceedings. International Conference on. IEEE. 55-59.
Zaman, F., Hogan, G., Van Der Meer, S., Keeney, J., Robitzsch, S., & Muntean, G. M. 2015. A Recommendation system architecture for predictive telecom network management. IEEE Communications Magazine, 53:286-293.
104
Zaman, N., & Li, J. 2014. Semantics-Enhanced Recommendation System for Social Healthcare. In Advanced Information Networking and Applications (AINA), IEEE 28th International Conference on. IEEE. 765-770.
Zhang, F., Liu, Q., & Zeng, A. 2017. Timeliness in Recommendation systems. Expert Systems with Applications, 85:270-278.
Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. 2017. Health-CPS: Healthcare cyber- physical system assisted by cloud and big data. IEEE Systems Journal, 11: 88-95.
Zhang, Z., Chen, C., Zhou, J., & Li, X. 2018. An Industrial-Scale System for Heterogeneous Information Card Ranking in Alipay. In International Conference on Database Systems for Ad- vanced Applications. Springer, Cham. 713-724.
Zhong, Z., & Li, Y. 2016. A Recommendation System for Healthcare Based on Human-Centric Modeling. In e-Business Engineering (ICEBE), IEEE 13th International Conference on. IEEE. 282- 286.
Zhang Y, Chen M, Huang D, Wu D, Li Y 2017. iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Compu- ter Systems, 66:30-35.
uef.fi
PUBLICATIONS OF
THE UNIVERSITY OF EASTERN FINLAND Dissertations in Forestry and Natural Sciences
Dissertations in Forestry and Natural Sciences
DISSERTATIONS | ADEKUNLE OLUSEYI AFOLABI | REAL-TIME RECOMMENDATION SYSTEMS IN HEALTHCAR
