Published on in Vol 19, No 10 (2017): October

Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

Journals

  1. Kordonouri O, Riddell M. Use of apps for physical activity in type 1 diabetes: current status and requirements for future development. Therapeutic Advances in Endocrinology and Metabolism 2019;10 View
  2. Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 2018;20(5):e10775 View
  3. Liao P, Greenewald K, Klasnja P, Murphy S. Personalized HeartSteps. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
  4. Ghanvatkar S, Kankanhalli A, Rajan V. User Models for Personalized Physical Activity Interventions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11098 View
  5. Esefeld K, Heinicke V, Kress S, Behrens M, Zimmer P, Stumvoll M, Brinkmann C, Halle M. Diabetes, Sport und Bewegung. Der Diabetologe 2020;16(3):292 View
  6. Shifrin M, Siegelmann H. Near-optimal insulin treatment for diabetes patients: A machine learning approach. Artificial Intelligence in Medicine 2020;107:101917 View
  7. Debon R, Coleone J, Bellei E, De Marchi A. Mobile health applications for chronic diseases: A systematic review of features for lifestyle improvement. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2019;13(4):2507 View
  8. Pizzol D, Smith L, Koyanagi A, Stubbs B, Grabovac I, Jackson S, Veronese N. Do Older People with Diabetes Meet the Recommended Weekly Physical Activity Targets? An Analysis of Objective Physical Activity Data. International Journal of Environmental Research and Public Health 2019;16(14):2489 View
  9. Smith D, Duque L, Huffman J, Healy B, Celano C. Text Message Interventions for Physical Activity: A Systematic Review and Meta-Analysis. American Journal of Preventive Medicine 2020;58(1):142 View
  10. Lal A, Pinevich Y, Gajic O, Herasevich V, Pickering B. Artificial intelligence and computer simulation models in critical illness. World Journal of Critical Care Medicine 2020;9(2):13 View
  11. Weatherall J, Paprocki Y, Meyer T, Kudel I, Witt E. Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes. JMIR mHealth and uHealth 2018;6(6):e131 View
  12. Higgins K, Nesbitt C, Horan L, Curtis A, Richard K, Stallter C, Verela S. Brief Educational Sessions to Promote Health App Use. The Journal for Nurse Practitioners 2019;15(8):e165 View
  13. Gasparetti F, Aiello L, Quercia D. Personalized weight loss strategies by mining activity tracker data. User Modeling and User-Adapted Interaction 2020;30(3):447 View
  14. Aguilera A, Figueroa C, Hernandez-Ramos R, Sarkar U, Cemballi A, Gomez-Pathak L, Miramontes J, Yom-Tov E, Chakraborty B, Yan X, Xu J, Modiri A, Aggarwal J, Jay Williams J, Lyles C. mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study. BMJ Open 2020;10(8):e034723 View
  15. Forman E, Kerrigan S, Butryn M, Juarascio A, Manasse S, Ontañón S, Dallal D, Crochiere R, Moskow D. Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?. Journal of Behavioral Medicine 2019;42(2):276 View
  16. Barshes N, Grant C. Advances in the Management of Peripheral Artery Disease. Current Diabetes Reports 2019;19(7) View
  17. Sporrel K, Nibbeling N, Wang S, Ettema D, Simons M. Unraveling Mobile Health Exercise Interventions for Adults: Scoping Review on the Implementations and Designs of Persuasive Strategies. JMIR mHealth and uHealth 2021;9(1):e16282 View
  18. Pirolli P, Youngblood G, Du H, Konrad A, Nelson L, Springer A. Scaffolding the Mastery of Healthy Behaviors with Fittle+ Systems: Evidence-Based Interventions and Theory. Human–Computer Interaction 2021;36(2):73 View
  19. Valentiner L, Thorsen I, Kongstad M, Brinkløv C, Larsen R, Karstoft K, Nielsen J, Pedersen B, Langberg H, Ried-Larsen M, Stepto N. Effect of ecological momentary assessment, goal-setting and personalized phone-calls on adherence to interval walking training using the InterWalk application among patients with type 2 diabetes—A pilot randomized controlled trial. PLOS ONE 2019;14(1):e0208181 View
  20. Levy A, Biswas M, Weber R, Tarakji K, Chung M, Noseworthy P, Newton-Cheh C, Rosenberg M, Rasmusson R. Applications of machine learning in decision analysis for dose management for dofetilide. PLOS ONE 2019;14(12):e0227324 View
  21. Coronato A, Naeem M, De Pietro G, Paragliola G. Reinforcement learning for intelligent healthcare applications: A survey. Artificial Intelligence in Medicine 2020;109:101964 View
  22. Figueroa C, Hernandez-Ramos R, Boone C, Gómez-Pathak L, Yip V, Luo T, Sierra V, Xu J, Chakraborty B, Darrow S, Aguilera A. A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2021;10(1):e23592 View
  23. Davis A, Sweigart R, Ellis R. A systematic review of tailored mHealth interventions for physical activity promotion among adults. Translational Behavioral Medicine 2020;10(5):1221 View
  24. Howland C, Wakefield B. Assessing telehealth interventions for physical activity and sedentary behavior self‐management in adults with type 2 diabetes mellitus: An integrative review. Research in Nursing & Health 2021;44(1):92 View
  25. Wang S, Scheider S, Sporrel K, Deutekom M, Timmer J, Kröse B. What Are Good Situations for Running? A Machine Learning Study Using Mobile and Geographical Data. Frontiers in Public Health 2021;8 View
  26. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Brinkmann C, Halle M. Diabetes, Sport und Bewegung. Diabetologie und Stoffwechsel 2020;15(S 01):S148 View
  27. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Brinkmann C, Halle M. Diabetes, Sports and Exercise. Experimental and Clinical Endocrinology & Diabetes 2021;129(S 01):S52 View
  28. Alam Khan Z, Feng Z, Uddin M, Mast N, Ali Shah S, Imtiaz M, Al-Khasawneh M, Mahmoud M, Ali S. Optimal Policy Learning for Disease Prevention Using Reinforcement Learning. Scientific Programming 2020;2020:1 View
  29. Markert C, Sasangohar F, Mortazavi B, Fields S. The Use of Telehealth Technology to Support Health Coaching for Older Adults: Literature Review. JMIR Human Factors 2021;8(1):e23796 View
  30. Sporrel K, De Boer R, Wang S, Nibbeling N, Simons M, Deutekom M, Ettema D, Castro P, Dourado V, Kröse B. The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining. Frontiers in Public Health 2021;8 View
  31. Figueroa C, Aguilera A, Chakraborty B, Modiri A, Aggarwal J, Deliu N, Sarkar U, Jay Williams J, Lyles C. Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. Journal of the American Medical Informatics Association 2021;28(6):1225 View
  32. Bharadwaj H, Agarwal A, Chamola V, Lakkaniga N, Hassija V, Guizani M, Sikdar B. A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications. IEEE Access 2021;9:38859 View
  33. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Brinkmann C, Halle M. Diabetes, Sport und Bewegung. Der Diabetologe 2021;17(3):330 View
  34. De Chiara F, Ferret-Miñana A, Ramón-Azcón J. The Synergy between Organ-on-a-Chip and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical Research. Biomedicines 2021;9(3):248 View
  35. Steele R, Hillsgrove T, Khoshavi N, Jaimes L. A survey of cyber-physical system implementations of real-time personalized interventions. Journal of Ambient Intelligence and Humanized Computing 2022;13(5):2325 View
  36. Zhu J, Dallal D, Gray R, Villareale J, Ontañón S, Forman E, Arigo D. Personalization Paradox in Behavior Change Apps. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW1):1 View
  37. Wang S, Sporrel K, van Hoof H, Simons M, de Boer R, Ettema D, Nibbeling N, Deutekom M, Kröse B. Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study. International Journal of Environmental Research and Public Health 2021;18(11):6059 View
  38. Flores A, Demsas F, Leeper N, Ross E. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circulation Research 2021;128(12):1833 View
  39. Nibbeling N, Simons M, Sporrel K, Deutekom M. A Focus Group Study Among Inactive Adults Regarding the Perceptions of a Theory-Based Physical Activity App. Frontiers in Public Health 2021;9 View
  40. Tomkins S, Liao P, Klasnja P, Murphy S. IntelligentPooling: practical Thompson sampling for mHealth. Machine Learning 2021;110(9):2685 View
  41. De Croon R, Van Houdt L, Htun N, Štiglic G, Vanden Abeele V, Verbert K. Health Recommender Systems: Systematic Review. Journal of Medical Internet Research 2021;23(6):e18035 View
  42. Sun X, Bee Y, Lam S, Liu Z, Zhao W, Chia S, Abdul Kadir H, Wu J, Ang B, Liu N, Lei Z, Xu Z, Zhao T, Hu G, Xie G. Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation. Journal of Medical Internet Research 2021;23(7):e27858 View
  43. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Brinkmann C, Halle M. Diabetes, Sport und Bewegung. Diabetologie und Stoffwechsel 2021;16(S 02):S299 View
  44. Joachim S, Forkan A, Jayaraman P, Morshed A, Wickramasinghe N. A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes. Sensors 2022;22(12):4620 View
  45. Honka A, Nieminen H, Simila H, Kaartinen J, Gils M. A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation. IEEE Access 2022;10:116766 View
  46. Chong M, Sit J, Karthikesu K, Chair S. Effectiveness of technology-assisted cardiac rehabilitation: A systematic review and meta-analysis. International Journal of Nursing Studies 2021;124:104087 View
  47. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sport und Bewegung. Diabetologie und Stoffwechsel 2022;17(S 02):S301 View
  48. Trella A, Zhang K, Nahum-Shani I, Shetty V, Doshi-Velez F, Murphy S. Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines. Algorithms 2022;15(8):255 View
  49. Yu C, Liu J, Nemati S, Yin G. Reinforcement Learning in Healthcare: A Survey. ACM Computing Surveys 2023;55(1):1 View
  50. Lim Y, Ho B, Goh Y. Effectiveness of game‐based exercise interventions on modifiable cardiovascular risk factors of individuals with type two diabetes mellitus: A systematic review and meta‐analysis. Worldviews on Evidence-Based Nursing 2023;20(4):377 View
  51. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sports and Exercise. Experimental and Clinical Endocrinology & Diabetes 2023;131(01/02):51 View
  52. Makroum M, Adda M, Bouzouane A, Ibrahim H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors 2022;22(5):1843 View
  53. Michaelsen M, Esch T. Functional Mechanisms of Health Behavior Change Techniques: A Conceptual Review. Frontiers in Psychology 2022;13 View
  54. Wang S, Zhang C, Kröse B, van Hoof H. Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. Journal of Medical Systems 2021;45(12) View
  55. Daryabeygi-Khotbehsara R, Shariful Islam S, Dunstan D, McVicar J, Abdelrazek M, Maddison R. Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review. Journal of Medical Internet Research 2021;23(9):e26315 View
  56. Lauffenburger J, Yom-Tov E, Keller P, McDonnell M, Bessette L, Fontanet C, Sears E, Kim E, Hanken K, Buckley J, Barlev R, Haff N, Choudhry N. REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial. BMJ Open 2021;11(12):e052091 View
  57. Bogina V, Kuflik T, Jannach D, Bielikova M, Kompan M, Trattner C. Considering temporal aspects in recommender systems: a survey. User Modeling and User-Adapted Interaction 2023;33(1):81 View
  58. Vrátná E, Husáková J, Jarošíková R, Dubský M, Wosková V, Bém R, Jirkovská A, Králová K, Pyšková B, Lánská V, Fejfarová V. Effects of a 12-Week Interventional Exercise Programme on Muscle Strength, Mobility and Fitness in Patients With Diabetic Foot in Remission: Results From BIONEDIAN Randomised Controlled Trial. Frontiers in Endocrinology 2022;13 View
  59. Di S, Petch J, Gerstein H, Zhu R, Sherifali D. Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study. JMIR Formative Research 2022;6(9):e37838 View
  60. Zini F, Le Piane F, Gaspari M. Adaptive Cognitive Training with Reinforcement Learning. ACM Transactions on Interactive Intelligent Systems 2022;12(1):1 View
  61. Nagpal M, Barbaric A, Sherifali D, Morita P, Cafazzo J. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021;6(4):e29027 View
  62. Trewick N, Neumann D, Hamilton K, Vandoni M. Effect of affective feedback and competitiveness on performance and the psychological experience of exercise within a virtual reality environment. PLOS ONE 2022;17(6):e0268460 View
  63. Bardram J, Cramer-Petersen C, Maxhuni A, Christensen M, Bækgaard P, Persson D, Lind N, Christensen M, Nørgaard K, Khakurel J, Skinner T, Kownatka D, Jones A. DiaFocus: A Personal Health Technology for Adaptive Assessment in Long-Term Management of Type 2 Diabetes. ACM Transactions on Computing for Healthcare 2023;4(2):1 View
  64. Vetrovsky T, Kral N, Pfeiferova M, Kuhnova J, Novak J, Wahlich C, Jaklova A, Jurkova K, Janek M, Omcirk D, Capek V, Maes I, Steffl M, Ussher M, Tufano J, Elavsky S, Van Dyck D, Cimler R, Yates T, Harris T, Seifert B. mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial. BMC Public Health 2023;23(1) View
  65. . Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population. Advances in Geriatric Medicine and Research 2023 View
  66. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sport und Bewegung. Die Diabetologie 2023;19(4):513 View
  67. Ekpezu A, Wiafe I, Oinas-Kukkonen H. Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review. JMIR AI 2023;2:e46779 View
  68. Watanabe K, Okusa S, Sato M, Miura H, Morimoto M, Tsutsumi A. mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial. JMIR Formative Research 2023;7:e51334 View
  69. An R, Shen J, Wang J, Yang Y. A scoping review of methodologies for applying artificial intelligence to physical activity interventions. Journal of Sport and Health Science 2024;13(3):428 View
  70. Salinari A, Machì M, Armas Diaz Y, Cianciosi D, Qi Z, Yang B, Ferreiro Cotorruelo M, Villar S, Dzul Lopez L, Battino M, Giampieri F. The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases 2023;11(3):97 View
  71. Michaelsen M, Esch T. Understanding health behavior change by motivation and reward mechanisms: a review of the literature. Frontiers in Behavioral Neuroscience 2023;17 View
  72. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sport und Bewegung. Diabetologie und Stoffwechsel 2023;18(S 02):S314 View
  73. King Z, Yu H, Vaessen T, Myin-Germeys I, Sano A. Towards the Understanding of Receptivity and Affect in EMAs using Physiological based Machine Learning Method: Analysis of Receptivity and Affect (Preprint). JMIR mHealth and uHealth 2023 View
  74. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sport und Bewegung. Diabetes aktuell 2023;21(08):373 View
  75. Fang J, Lee V, Wang H. Optimal service resource management strategy for IoT-based health information system considering value co-creation of users. Industrial Management & Data Systems 2024;124(3):1132 View
  76. Ahmed B, Ali M, Masud M, Naznin M. Recent trends and techniques of blood glucose level prediction for diabetes control. Smart Health 2024;32:100457 View
  77. Lauffenburger J, Yom-Tov E, Keller P, McDonnell M, Crum K, Bhatkhande G, Sears E, Hanken K, Bessette L, Fontanet C, Haff N, Vine S, Choudhry N. The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial. npj Digital Medicine 2024;7(1) View
  78. Muse E, Topol E. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metabolism 2024;36(4):670 View
  79. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sport und Bewegung. Die Diabetologie 2024;20(3):379 View
  80. Ghosh S, Kim R, Chhabria P, Dwivedi R, Klasnja P, Liao P, Zhang K, Murphy S. Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling. Machine Learning 2024;113(7):3961 View
  81. Felfernig A, Wundara M, Tran T, Le V, Lubos S, Polat-Erdeniz S. Sports recommender systems: overview and research directions. Journal of Intelligent Information Systems 2024;62(4):1125 View
  82. Bucher A, Blazek E, Symons C. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):375 View
  83. Sheng B, Pushpanathan K, Guan Z, Lim Q, Lim Z, Yew S, Goh J, Bee Y, Sabanayagam C, Sevdalis N, Lim C, Lim C, Shaw J, Jia W, Ekinci E, Simó R, Lim L, Li H, Tham Y. Artificial intelligence for diabetes care: current and future prospects. The Lancet Diabetes & Endocrinology 2024;12(8):569 View
  84. Martelli E, Capoccia L, Di Francesco M, Cavallo E, Pezzulla M, Giudice G, Bauleo A, Coppola G, Panagrosso M. Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease. Biomimetics 2024;9(8):465 View
  85. Deliu N, Williams J, Chakraborty B. Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions. International Statistical Review 2024 View
  86. Aguilera A, Arévalo Avalos M, Xu J, Chakraborty B, Figueroa C, Garcia F, Rosales K, Hernandez-Ramos R, Karr C, Williams J, Ochoa-Frongia L, Sarkar U, Yom-Tov E, Lyles C. Effectiveness of a Digital Health Intervention Leveraging Reinforcement Learning: Results From the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) Randomized Clinical Trial. Journal of Medical Internet Research 2024;26:e60834 View
  87. Glavas C, Scott D, Sood S, George E, Daly R, Gvozdenko E, de Courten B, Jansons P. Exploring the Feasibility of Digital Voice Assistants for Delivery of a Home-Based Exercise Intervention in Older Adults With Obesity and Type 2 Diabetes Mellitus: Randomized Controlled Trial. JMIR Aging 2024;7:e53064 View
  88. Asgari Mehrabadi M, Khatibi E, Jimah T, Labbaf S, Borg H, Narvaez L, Pimentel P, Turner A, Dutt N, Guo Y, Rahmani A. PERFECT: Personalized Exercise Recommendation Framework and architECTure. ACM Transactions on Computing for Healthcare 2024;5(4):1 View
  89. Brons A, Wang S, Visser B, Kröse B, Bakkes S, Veltkamp R. Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview. Journal of Medical Internet Research 2024;26:e47774 View
  90. Choubey U, Upadrasta V, Kaur I, Banker H, Kanagala S, Anamika F, Virmani M, Jain R. From prevention to management: exploring AI’s role in metabolic syndrome management: a comprehensive review. The Egyptian Journal of Internal Medicine 2024;36(1) View
  91. Esefeld K, Kress S, Behrens M, Zimmer P, Stumvoll M, Thurm U, Gehr B, Halle M, Brinkmann C. Diabetes, Sport und Bewegung. Diabetologie und Stoffwechsel 2024;19(S 02):S279 View
  92. den Braber N, Vollenbroek-Hutten M, Kappert K, Laverman G. Analysing physical activity measures and clustering in patients with type 2 diabetes in secondary care: insights from the DIAbetes and LifEstyle Cohort Twente (DIALECT)—an observational cohort study. BMJ Open 2024;14(12):e082059 View
  93. Doherty C, Lambe R, O’Grady B, O’Reilly-Morgan D, Smyth B, Lawlor A, Hurley N, Tragos E. An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial. JMIR mHealth and uHealth 2024;12:e49443 View

Books/Policy Documents

  1. Lamba D, Hsu W, Alsadhan M. Machine Learning, Big Data, and IoT for Medical Informatics. View
  2. Qian X, Zhu Z, Wang K, Zhou Z. Advances in Intelligent Automation and Soft Computing. View
  3. Ozkaynak M, Skiba D. Nursing Informatics. View
  4. Mejova Y. Handbook of Computational Social Science for Policy. View
  5. de Bruijn G, Liu S, Rhodes R. The International Encyclopedia of Health Communication. View
  6. El rhatassi F, El Ghali B, Daoudi N. Proceedings of the 6th International Conference on Big Data and Internet of Things. View