Additional References

Digital inclusion of elderly people: designing a purposeful serious game interface with memorable music by Sunny Choi, PhD 

References Continued:

  1. Vuust, P., & Frith, C. D. (2008). Anticipation is the key to understanding music and the effects of music on emotion. Behavioral and Brain Sciences, 31(5), 599–600.

The Doctor Will See You Now… and 24×7 by Larry Garber, MD

References Continued:

6 Kannampallil T, Ajilore OA, et al. Effects of a virtual voice-based coach delivering problem-solving treatment on emotional distress and brain function: a pilot RCT in depression and anxiety. Transl Psychiatry. 2023 May 12;13(1):166. doi: 10.1038/s41398-023-02462-x. Erratum in: Transl Psychiatry. 2023 Jul 4;13(1):242. PMID: 37173334; PMCID: PMC10175049.

Artificial Intelligence in Mental Healthcare: A Story of Hope and Hazard by Rajendra Aldis, MD, MSCS and Nicholas Carson, MD, FRCPC

References Continued:

6) Ross, E. L., Zuromski, K. L., Reis, B. Y., Nock, M. K., Kessler, R. C., & Smoller, J. W. (2021). Accuracy Requirements for Cost-effective Suicide Risk Prediction Among Primary Care Patients in the US. JAMA psychiatry, 78(6), 642–650.

7) Allsopp, K., Read, J., Corcoran, R., & Kinderman, P. (2019). Heterogeneity in psychiatric diagnostic classification. Psychiatry research, 279, 15–22.

8) Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., Schatzberg, A. F., Sudheimer, K., Keller, J., Mayberg, H. S., Gunning, F. M., Alexopoulos, G. S., Fox, M. D., Pascual-Leone, A., Voss, H. U., Casey, B. J., … Liston, C. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature medicine, 23(1), 28–38.

9) Zhang, Y., Wu, W., Toll, R. T., Naparstek, S., Maron-Katz, A., Watts, M., Gordon, J., Jeong, J., Astolfi, L., Shpigel, E., Longwell, P., Sarhadi, K., El-Said, D., Li, Y., Cooper, C., Chin-Fatt, C., Arns, M., Goodkind, M. S., Trivedi, M. H., Marmar, C. R., … Etkin, A. (2021). Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nature biomedical engineering, 5(4), 309–323.

10) Pigoni, A., Delvecchio, G., Madonna, D., Bressi, C., Soares, J., & Brambilla, P. (2019). Can Machine Learning help us in dealing with treatment resistant depression? A review. Journal of affective disorders, 259, 21–26.

11) Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.), 366(6464), 447–453.

12) Duckworth, C., Chmiel, F. P., Burns, D. K., Zlatev, Z. D., White, N. M., Daniels, T. W. V., Kiuber, M., & Boniface, M. J. (2021). Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Scientific reports, 11(1), 23017.

13) Reddy, S., Rogers, W., Makinen, V. P., Coiera, E., Brown, P., Wenzel, M., Weicken, E., Ansari, S., Mathur, P., Casey, A., & Kelly, B. (2021). Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ health & care informatics, 28(1), e100444.

14) Russell, R. G., Lovett Novak, L., Patel, M., Garvey, K. V., Craig, K. J. T., Jackson, G. P., Moore, D., & Miller, B. M. (2023). Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals. Academic medicine : journal of the Association of American Medical Colleges, 98(3), 348–356.