Ecological interface design
Rasmussen J, Vicente KJ. Coping with human errors through system design: implications for ecological interface design. International Journal of Man-Machine Studies, 1992.
Vicente KJ. Ecological interface design: progress and challenges. Human Factors, 2002.
Working memory and cognitive load
Miller GA. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 1956.
Cowan N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behavioral and Brain Sciences, 2001.
Protective mechanical ventilation
Amato MBP, Meade MO, Slutsky AS, et al. Driving pressure and survival in the acute respiratory distress syndrome. New England Journal of Medicine, 2015.
ARDSnet Investigators. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. New England Journal of Medicine, 2000.
Mechanical power as a marker of injurious ventilation
Serpa Neto A, Deliberato RO, Johnson AEW, et al. Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Medicine, 2018.
Gattinoni L, Marini JJ, Collino F, et al. Mechanical power: meaning, uses and limitations. Intensive Care Medicine, 2023.
Dead-space physiology
Enghoff H. Volumen inefficax: Bemerkungen zur Frage des schädlichen Raumes. Uppsala Läkareförenings Förhandlingar, 1938. The original derivation of the Enghoff modification of the Bohr equation used to compute Vd/Vt in the ventilator constraint model.
Closest published precedents in constraint visualisation
Karbing DS, Spadaro S, Rees SE, et al. Beacon Caresystem decision-support for mechanical ventilation. Multiple publications, Aalborg / Ferrara group. The closest published visual-synthesis precedent for the constraint polygon design, using a hexagonal display and model-based physiological simulation.
Spadaro S, Karbing DS, Mauri T, et al. An open-loop, physiological model–based decision support system for mechanical ventilation. Multiple publications from 2016 onwards.
Implementation science for synthesis displays in ICU
Moorman LP. Principles for real-world implementation of bedside predictive analytics monitoring. Applied Clinical Informatics, 2021. Six principles for real-world deployment of synthesis and monitoring tools in ICU — drawn from the CoMET system implementation. Absorbed as inherited wisdom for VCM rollout planning.
Knowledge-guided and interpretable machine learning
Rudin C. Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead. Nature Machine Intelligence, 2019. The foundational argument for interpretable ML in high-stakes settings; provides the academic grounding for the knowledge-guided ML framing adopted by the project.
Karpatne A, Atluri G, Faghmous JH, et al. Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 2017; 29(10): 2318–2331. The canonical reference for knowledge-guided machine learning (KGML) as a named paradigm — the framework within which the constraint-scaffold approach is positioned.
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021; 596(7873): 583–589. The paradigm case for knowledge-guided ML at scale: embedding evolutionary, physical, and geometric constraints directly into the neural network architecture. Cited in the Approach section as the exemplar of the constraint-scaffold principle.
Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988. The foundational text establishing Bayesian networks as a formal framework for probabilistic reasoning under uncertainty — the theoretical basis for the Bayesian ML architecture proposed in the project's longer-term framing.
Lucas PJF, van der Gaag LC, Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine, 2004; 30(3): 201–214. The most widely cited review of Bayesian network applications in clinical medicine; grounds the project's positioning of Bayesian networks as the natural architecture for interpretable, prior-informed clinical AI.
AI-based ICU decision support comparators
Komorowski M, Celi LA, Badawi O, et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 2018. The leading UK ICU AI programme; provides both the methodological template for dual-database validation (MIMIC training + eICU external validation) and the outcome-first paradigm against which the scaffold-first approach is positioned.