References
- Beam A.L., Kohane I.S., Big Data and Machine Learning in Health Care, JAMA, 319(13), 2018, 1317–1318.
- Borsboom D., A network theory of mental disorders, World Psychiatry, 16(1), 2017, 5–13.
- Branchi I., A mathematical formula of plasticity: Measuring susceptibility to change in mental health and data science, Neuroscience and Biobehavioral Reviews, 152, 2023, 105272.
- Collins G.S., Dhiman P., Ma J., Schlussel M.M., Archer L., Van Calster B. et al., Evaluation of clinical prediction models (part 1): from development to external validation, BMJ, 384, 2024, e074819.
- Esteva A., Robicquet A., Ramsundar B. et al., A guide to deep learning in healthcare, Nature Medicine, 25, 2019, 24–29.
- Frank S.A., Two kinds of causality in age-related disease, F1000Research, 5, 2016.
- Genkin M., Shenoy K.V., Chandrasekaran C., Engel T., The dynamics and geometry of choice in the premotor cortex, Nature, 645, 2025, 168–176.
- Gudibanda K., Fousek J., Petkoski S., Jirsa V., The role of connectivity for the degeneracy of the brain’s resting state dynamics, Journal of Computational Neuroscience, 54(1), 2026, 1–21.
- Hauskrecht M., Fraser H., Planning treatment of ischemic heart disease with partially observable Markov decision processes, Artificial Intelligence in Medicine, 18(3), 2000, 221–244.
- Hood L., Friend S.H., Predictive, personalized, preventive, participatory (P4) cancer medicine, Nature Reviews Clinical Oncology, 2011.
- Kędys J., Mazurek C., Energy Landscape Analysis for studying neural state-space dynamics, Poster presented at EBRAINS Summit 2025, Brussels, Belgium, 2025.
- Komorowski M., Celi L.A., Badawi O. et al., The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care, Nature Medicine, 24, 2018, 1716–1720.
- Kwon B.C., Achenbach P., Dunne J.L. et al., Modeling disease progression trajectories from longitudinal observational data, AMIA Annual Symposium Proceedings, 2020, 668–676.
- Li Y., Rao S., Solares J.R.A., Mamouei M., Canoy R., Tran J., Zotti N., Rahimi K., BEHRT: Transformer for Electronic Health Records, Scientific Reports, 10, 2020, 7155.
- Lipton Z.C., Kale D.C., Elkan C., Wetzell R., Learning to diagnose with LSTM recurrent neural networks, arXiv preprint, 2016, arXiv:1511.03677.
- Miotto R., Li L., Kidd B.A., Dudley J.T., Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, Scientific Reports, 6, 2016, 26094.
- Murphy K.P., Dynamic Bayesian Networks: Representation, Inference and Learning, PhD thesis, University of California, Berkeley, 2002.
- Oxtoby N.P., Data-Driven Disease Progression Modeling, in: Colliot O. (ed.), Machine Learning for Brain Disorders, Humana, New York, 2023.
- Rabiner L.R., A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77(2), 1989, 257–286.
- Saria S., Butte A., Sheikh A., Better medicine through machine learning: What’s real, and what’s artificial?, PLoS Medicine, 15(12), 2018, e1002721.
- Scheffer M., Bascompte J., Brock W.A. et al., Early-warning signals for critical transitions, Nature, 461, 2009, 53–59.
- Siebra C.A., Kurpicz-Briki M., Wac K., Transformers in health: a systematic review on architectures for longitudinal data analysis, Artificial Intelligence Review, 57, 2024, 32.
- Świerczyński H., Pukacki J., Szczęsny S., Mazurek C., Wasilewicz R., Sensor data analysis and development of machine learning models for detection of glaucoma, Biomedical Signal Processing and Control, 86, 2023.
- Świerczyński H. et al., Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support, Scientific Reports, 15, 2025, 7940.
- Urai A.E., Structure uncovered: understanding temporal variability in perceptual decision-making, Trends in Cognitive Sciences, 30(1), 2026, 54–65.
- Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., Polosukhin I., Attention is all you need, Advances in Neural Information Processing Systems, 2017, 6000–6010.
- Wang H.E., Triebkorn P., Breyton M., Dollomaja B., Lemarechal J.D., Petkoski S., Jirsa V.K., Virtual brain twins: From basic neuroscience to clinical use, National Science Review, 2024.
- Young A.L., Oxtoby N.P., Daga P., Cash D.M., Fox N.C., Ourselin S., Schott J.M., Alexander D.C., A data-driven model of biomarker changes in sporadic Alzheimer’s disease, Brain, 137(9), 2014, 2564–2577.
Language: English
Page range: 213 - 238
Submitted on: Mar 31, 2026
Accepted on: May 29, 2026
Published on: Jun 26, 2026
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
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© 2026 Cezary Mazurek, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.