13 Smart Learning Using Big and Small Data for Mobile and IOT e-Health
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32. A.C. King, E.B. Hekler, L.A. Grieco, S.J. Winter, J.L. Sheats, M.P. Buman, B. Banerjee,
T.N. Robinson, J. Cirimele, Harnessing different motivational frames via mobile phones to
promote daily physical activity and reduce sedentary behavior in aging adults. Plos ONE
8(4), e62613 (2013)
33. S.J. Winter, J.L. Sheats, A.C. King, The use of behavior change techniques and theory in
technologies for cardiovascular disease prevention and treatment in adults: A comprehensive
review. Prog. Cardiovasc. Dis. 58(6), 605–612 (2016)
34. D. Ben-Zeev, K.E. Davis, S. Kaiser, I. Krzsos, R.E. Drake, Mobile technologies among people
with serious mental illness: Opportunities for future services. Adm. Policy Ment. Health
Ment. Health Serv. Res. 40(4), 340–343 (2013)
35. C.A. Christmann, A. Hoffmann, G. Bleser, Stress management apps with regard to emotionfocused coping and behavior change techniques: A content analysis. JMIR Mhealth Uhealth
5(2), e22 (2017)
36. E.B. Litvin, A.M. Abrantes, R.A. Brown, Computer and mobile technology-based interventions for substance use disorders: An organizing framework. Addict. Behav. 38(3), 1747–1756
(2013)
37. P. Domingos, A. Pedro, A few useful things to know about machine learning. Commun. ACM
55(10), 78 (2012)
38. D. Castelvecchi, Can we open the black box of AI? Nature 538(7623), 20–23 (2016)
39. S.A. Murphy, A generalization error for Q-learning. J. Mach. Learn. Res 6(Jul), 1073–1097
(2005a)
40. Y.K. Cheung, B. Chakraborty, K.W. Davidson, Sequential multiple assignment randomized
trial (SMART) with adaptive randomization for quality improvement in depression treatment
program. Biometrics 71(2), 450–459 (2015)
41. P.J. Schulte, A.A. Tsiatis, E.B. Laber, M. Davidian, Q-and A-learning methods for estimating
optimal dynamic treatment regimes. Stat. Sci 29(4), 640 (2014)
42. S.A. Murphy, An experimental design for the development of adaptive treatment strategies.
Stat. Med. 24(10), 1455–1481 (2005b)
43. L.I. Wagner, J. Duffecy, F. Penedo, D.C. Mohr, D. Cella, Coping strategies tailored to
the management of fear of recurrence and adaptation for E-health delivery: The FoRtitude
intervention. Cancer 123(6), 906–910 (2017)
44. P. Klasnja, E.B. Hekler, S. Shiffman, A. Boruvka, D. Almirall, A. Tewari, S.A. Murphy,
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychol. 34(Suppl), 1220–1228 (2015). https://doi.org/10.1037/hea0000305
45. R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, N. Elhadad, in Proceedings of KDD. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission.
(2015)
46. Z.C. Lipton, in Proceedings of ICML Workshop on Human Interpretability in Machine
Learning (WHI 2016). The mythos of model interpretability. (2016)
47. P.S. Hsueh, S. Das, S. Dey, T. Wetter, in Proceedings of MEDINFO. Making sense of Patient
Generated Health Data (PGHD) with better interpretability: The transition from more to
better. (2017a)
48. X. Hu, P.-Y.S. Hsueh, C.-H. Chen, K.M. Diaz, Y.-K.K. Cheung, M. Qian, A first step towards
behavioral coaching for managing stress: A case study on optimal policy estimation with
multi-stage threshold Q-learning, in AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2017, (2017), pp. 930–939. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/
29854160
49. X. Hu, P.S. Hsueh, C. Chen, M. Qian, F.E. Parsons, I. Ensari, K. Daz, Y.K. Ceung, An
interpretable health behavioral policy for mobile device users. IBM J. Res. Dev. 62(1) (Jan
2018). https://doi.org/10.1147/JRD.2017.2769320
50. K.A. Bartholomew, The perspective of a practitioner, in Knowledge Coupling, (Springer, New
York, 1991), pp. 235–277
633
32. A.C. King, E.B. Hekler, L.A. Grieco, S.J. Winter, J.L. Sheats, M.P. Buman, B. Banerjee,
T.N. Robinson, J. Cirimele, Harnessing different motivational frames via mobile phones to
promote daily physical activity and reduce sedentary behavior in aging adults. Plos ONE
8(4), e62613 (2013)
33. S.J. Winter, J.L. Sheats, A.C. King, The use of behavior change techniques and theory in
technologies for cardiovascular disease prevention and treatment in adults: A comprehensive
review. Prog. Cardiovasc. Dis. 58(6), 605–612 (2016)
34. D. Ben-Zeev, K.E. Davis, S. Kaiser, I. Krzsos, R.E. Drake, Mobile technologies among people
with serious mental illness: Opportunities for future services. Adm. Policy Ment. Health
Ment. Health Serv. Res. 40(4), 340–343 (2013)
35. C.A. Christmann, A. Hoffmann, G. Bleser, Stress management apps with regard to emotionfocused coping and behavior change techniques: A content analysis. JMIR Mhealth Uhealth
5(2), e22 (2017)
36. E.B. Litvin, A.M. Abrantes, R.A. Brown, Computer and mobile technology-based interventions for substance use disorders: An organizing framework. Addict. Behav. 38(3), 1747–1756
(2013)
37. P. Domingos, A. Pedro, A few useful things to know about machine learning. Commun. ACM
55(10), 78 (2012)
38. D. Castelvecchi, Can we open the black box of AI? Nature 538(7623), 20–23 (2016)
39. S.A. Murphy, A generalization error for Q-learning. J. Mach. Learn. Res 6(Jul), 1073–1097
(2005a)
40. Y.K. Cheung, B. Chakraborty, K.W. Davidson, Sequential multiple assignment randomized
trial (SMART) with adaptive randomization for quality improvement in depression treatment
program. Biometrics 71(2), 450–459 (2015)
41. P.J. Schulte, A.A. Tsiatis, E.B. Laber, M. Davidian, Q-and A-learning methods for estimating
optimal dynamic treatment regimes. Stat. Sci 29(4), 640 (2014)
42. S.A. Murphy, An experimental design for the development of adaptive treatment strategies.
Stat. Med. 24(10), 1455–1481 (2005b)
43. L.I. Wagner, J. Duffecy, F. Penedo, D.C. Mohr, D. Cella, Coping strategies tailored to
the management of fear of recurrence and adaptation for E-health delivery: The FoRtitude
intervention. Cancer 123(6), 906–910 (2017)
44. P. Klasnja, E.B. Hekler, S. Shiffman, A. Boruvka, D. Almirall, A. Tewari, S.A. Murphy,
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychol. 34(Suppl), 1220–1228 (2015). https://doi.org/10.1037/hea0000305
45. R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, N. Elhadad, in Proceedings of KDD. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission.
(2015)
46. Z.C. Lipton, in Proceedings of ICML Workshop on Human Interpretability in Machine
Learning (WHI 2016). The mythos of model interpretability. (2016)
47. P.S. Hsueh, S. Das, S. Dey, T. Wetter, in Proceedings of MEDINFO. Making sense of Patient
Generated Health Data (PGHD) with better interpretability: The transition from more to
better. (2017a)
48. X. Hu, P.-Y.S. Hsueh, C.-H. Chen, K.M. Diaz, Y.-K.K. Cheung, M. Qian, A first step towards
behavioral coaching for managing stress: A case study on optimal policy estimation with
multi-stage threshold Q-learning, in AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2017, (2017), pp. 930–939. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/
29854160
49. X. Hu, P.S. Hsueh, C. Chen, M. Qian, F.E. Parsons, I. Ensari, K. Daz, Y.K. Ceung, An
interpretable health behavioral policy for mobile device users. IBM J. Res. Dev. 62(1) (Jan
2018). https://doi.org/10.1147/JRD.2017.2769320
50. K.A. Bartholomew, The perspective of a practitioner, in Knowledge Coupling, (Springer, New
York, 1991), pp. 235–277