| Technical Foundations
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| Health Care Analytics |
master |
- Saranya, P., & Asha, P. (2019, November). Survey on big data analytics in health care. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 46-51). IEEE.
- White, S. E. (2014). A review of big data in health care: challenges and opportunities. Open Access Bioinformatics, 6, 13.
- Bhardwaj, R., Nambiar, A. R., & Dutta, D. (2017, July). A study of machine learning in healthcare. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 236-241). IEEE.
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| Health Information Systems/ ERP Health Systems |
bachelor |
- AbouZahr, C., & Boerma, T. (2005). Health information systems: the foundations of public health. Bulletin of the World Health Organization, 83, 578-583.
- Kuhn, K. A., & Giuse, D. A. (2001). From hospital information systems to health information systems. Methods of information in medicine, 40(04), 275-287.)
- Monteiro, E. (2003). Integrating health information systems: a critical appraisal. Methods of information in medicine, 42(04), 428-432.
- K. Siau, “Health care informatics,” in IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 1, pp. 1-7, March 2003, doi: 10.1109/TITB.2002.805449.
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| AR, VR, XR for digital health |
bachelor |
- Baghaei, N., Stemmet, L., Hlasnik, A., Emanov, K., Hach, S., Naslund, J. A., ... & Liang, H. N. (2020, April). Time to Get Personal: Individualised Virtual Reality for Mental Health. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-9).
- BreathCoach: A smart in-home breathing training system with bio-feedback via VR game
- Ahmad Abushakra and Miad Faezipour. 2014. Augmenting Breath Regulation Using a Mobile Driven Virtual Reality Therapy Framework. IEEE Journal of Biomedical and Health Informatics 18, 3: 746–752.
- Smartphone-Based Tapping Frequency as a Surrogate for Perceived Fatigue. An In-the-Wild Feasibility Study in Multiple Sclerosis Patients.
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| Data sharing in patient dossiers with focus on Distributed Ledgers |
bachelor |
- Dubovitskaya, Alevtina, et al. “ACTION-EHR: Patient-centric blockchain-based electronic health record data management for cancer care.” Journal of medical Internet research 22.8 (2020): e13598.
- Dubovitskaya, Alevtina, et al. “Intelligent health care data management using blockchain: current limitation and future research agenda.” Heterogeneous Data Management, Polystores, and Analytics for Healthcare. Springer, Cham, 2019. 277-288.
- Houtan, Bahar, Abdelhakim Senhaji Hafid, and Dimitrios Makrakis. “A survey on blockchain-based self-sovereign patient identity in healthcare.” IEEE Access 8 (2020): 90478-90494.
- Gordon, William J., and Christian Catalini. “Blockchain technology for healthcare: facilitating the transition to patient-driven interoperability.” Computational and structural biotec
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| Management, Ethics, Law |
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| Digital Transformation and Health Ecosystems |
master |
- Hermes, S., Riasanow, T., Clemons, E.K. et al. (2020) The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Bus Res 13, 1033–1069 (2020).
- Benis A, Tamburis O, Chronaki C, Moen A (2021) One Digital Health: A Unified Framework for Future Health Ecosystems. J Med Internet Res 2021, 23(2).
- Ritu Agarwal, et al., (2010) Research Commentary—The Digital Transformation of Healthcare: Current Status and the Road Ahead. Information Systems Research 21(4):796-809.
- Mustafa Bayram, et al. (2020). COVID-19 Digital Health Innovation Policy: A Portal to Alternative Futures in the Making. OMICS: A Journal of Integrative Biology. Aug 2020.460-469.
- Kim, S.H., Song, H. (2022). How Digital Transformation Can Improve Hospitals’ Operational Decisions. Harvard Business Review.
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Data protection concerns and solutions in health records
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bachelor |
- Spriggs, M., Arnold, M. V., Pearce, C. M., & Fry, C. (2012). Ethical questions must be considered for electronic health records. Journal of Medical Ethics, 38(9), 535-539.
- Sulmasy, L. S., López, A. M., & Horwitch, C. A. (2017). Ethical implications of the electronic health record: in the service of the patient. Journal of general internal medicine, 32(8), 935-939.
- Rumbold, J. M. M., & Pierscionek, B. (2017). The effect of the general data protection regulation on medical research. Journal of medical Internet research, 19(2), e7108.
- Kwon, J., & Johnson, M. E. (2013). Health-care security strategies for data protection and regulatory compliance. Journal of Management Information Systems, 30(2), 41-66.
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Ethical concerns and justifications of automated decision making in healthcare (might be limited by adding ‘from the perspective of x’, x in {health provider, patient, etc.})
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master |
- Grote, Thomas, and Philipp Berens. “On the ethics of algorithmic decision-making in healthcare.” Journal of medical ethics 46.3 (2020): 205-211.
- Schönberger, Daniel. “Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications.” International Journal of Law and Information Technology 27.2 (2019): 171-203.
- (not scientific) World Health Organization. “Ethics and governance of artificial intelligence for health: WHO guidance.” (2021).
- Durán, Juan Manuel, and Karin Rolanda Jongsma. “Who is afraid of black box algorithms? on the epistemological and ethical basis of trust in medical AI.” Journal of Medical Ethics 47.5 (2021): 329-335.
- Quinn, Thomas P., et al. “Trust and medical AI: the challenges we face and the expertise needed to overcome them.” Journal of the American Medical Informatics Association 28.4 (2021): 890-894.
- Tsamados, Andreas, et al. “The ethics of algorithms: key problems and solutions.” AI & SOCIETY (2021)
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| Business Process Modeling in Healthcare |
bachelor |
- Ruiz, F., Garcia, F., Calahorra, L., Llorente, C., Gonçalves, L., Daniel, C., & Blobel, B. (2012). Business process modeling in healthcare. Stud Health Technol Inform, 179, 75-87.
- Rad, A. A., Benyoucef, M., & Kuziemsky, C. E. (2009). An evaluation framework for business process modeling languages in healthcare. Journal of theoretical and applied electronic commerce research, 4(2), 1-19.
- Braun, R., Schlieter, H., Burwitz, M., & Esswein, W. (2015). Extending a business process modeling language for domain-specific adaptation in healthcare.
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| When Is It Adequate to Rely on AI in the Medical Field? Evaluating Current Topics on (Appropriate) Reliance in AI |
bachelor |
- Trust and medical AI: the challenges we face and the expertise needed to overcome them (Quinn et al., 2020).
- Trust in AI: why we should be designing for APPROPRIATE reliance (Benda et al. , 2021).
- Medical Informatics in a Tension Between Black-Box AI and Trust (Sariyar and Holm, 2022)
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| Applications |
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| Community Health Information System (CHIS) Design in Low- and Middle-Income Countries (LMICs) (e.g., system models & functional components) |
bachelor |
- Krist, A. H., Phillips, R., Leykum, L., & Olmedo, B. (2021). Digital health needs for implementing high-quality primary care: recommendations from the National Academies of Sciences, Engineering, and Medicine. Journal of the American Medical Informatics Association, 28(12), 2738-2742.
- Tummers, J., Tobi, H., Catal, C., & Tekinerdogan, B. (2021). Designing a reference architecture for health information systems. BMC Medical Informatics and Decision Making, 21(1), 1-14.
- Were, M. C., Savai, S., Mokaya, B., Mbugua, S., Ribeka, N., Cholli, P., & Yeung, A. (2021). mUzima Mobile Electronic Health Record (EHR) System: Development and Implementation at Scale. Journal of Medical Internet Research, 23(12), e26381.
- Marcolino, M. S., Oliveira, J. A. Q., Cimini, C. C. R., Maia, J. X., Pinto, V. S. O. A., Sá, T. Q. V., … & Ribeiro, A. L. (2021). Development and implementation of a decision support system to improve control of hypertension and diabetes in a resource-constrained area in Brazil: mixed methods study. Journal of medical Internet research, 23(1), e18872.
- Zaidi, S., Kazi, A. M., Riaz, A., Ali, A., Najmi, R., Jabeen, R., … & Sayani, S. (2020). Operability, usefulness, and task-technology fit of an mhealth app for delivering primary health care services by community health workers in underserved areas of Pakistan and Afghanistan: Qualitative study. Journal of Medical Internet Research, 22(9), e18414.
- Faujdar, D. S., Sahay, S., Singh, T., Kaur, M., & Kumar, R. (2020). Field testing of a digital health information system for primary health care: a quasi-experimental study from India. International Journal of Medical Informatics, 141, 104235.
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| Closing the loop/ Integration of Apps with Health Information Systems |
bachelor |
- Brahmbhatt Ronak; Niakan Shadi; Saha Nishita; Tewari Anukriti; Pirani Ashfiya; Keshavjee Natasha et al. (2017): Diabetes mHealth Apps: Designing for Greater Uptake. In Studies in Health Technology and Informatics 234, pp. 49–53. DOI: 10.3233/978-1-61499-742-9-49.
- Kong, Tracie; Scott, Mary Morgan; Li, Yang; Wichelman, Cynthia (2020): Physician attitudes towards-and adoption of-mobile health. In Digital health 6, 2055207620907187. DOI: 10.1177/2055207620907187.
- Lobelo, Felipe; Kelli, Heval M.; Tejedor, Sheri Chernetsky; Pratt, Michael; McConnell, Michael V.; Martin, Seth S.; Welk, Gregory J. (2016): The Wild Wild West: A Framework to Integrate mHealth Software Applications and Wearables to Support Physical Activity Assessment, Counseling and Interventions for Cardiovascular Disease Risk Reduction. In Progress in cardiovascular diseases 58 (6), pp. 584–594. DOI: 10.1016/j.pcad.2016.02.007.
- Paglialonga, Alessia; Patel, Anisha A.; Pinto, Erica; Mugambi, Dora; Keshavjee, Karim (2019): The Healthcare System Perspective in mHealth. In Giuseppe Andreoni, Paolo Perego, Enrico Frumento (Eds.): m_Health Current and Future Applications. Cham: Springer International Publishing (SpringerLink Bücher), pp. 127–142
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| A Framework for Explainable Image Detection (Would involve the implementation of a deep learning approach using state-of-the-art XAI approaches, e.g., Grad-CAM) |
master |
- van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2021). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. arXiv preprint arXiv:2107.10912.
- Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88
- Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.
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| Supporting Doctoral Advice-Giving |
bachelor |
- Crampton, Noah H., Shmuel Reis, and Aviv Shachak. “Computers in the clinical encounter: a scoping review and thematic analysis.” Journal of the American Medical Informatics Association 23.3 (2016): 654-665.
- Saleem, Jason J., et al. “You and me and the computer makes three: variations in exam room use of the electronic health record.” Journal of the American Medical Informatics Association 21.e1 (2014): e147-e151.
- Duke, Pamela, Richard M. Frankel, and Shmuel Reis. “How to integrate the electronic health record and patient-centered communication into the medical visit: a skills-based approach.” Teaching and learning in medicine 25.4 (2013): 358-365.
- Mørck, Peter, et al. “Variations in oncology consultations: how dictation allows variations to be documented in standardized ways.” Computer Supported Cooperative Work (CSCW) 27.3 (2018): 539-568.
- Pearce, Christopher, et al. “The patient and the computer in the primary care consultation.” Journal of the American Medical Informatics Association 18.2 (2011): 138-142.
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