This publication evaluates data-driven prognosis models in clinical neuroscience, focusing on how heterogeneous patient data can be used to estimate disease risk, treatment response, and functional outcomes. It discusses model calibration, external validation, missing-data bias, and ethical considerations in decision support. The central conclusion is that predictive systems should augment clinical reasoning rather than replace expert interpretation.
Data Science Excellence in Clinical Prognosis
Selected for its careful discussion of predictive modeling, validation bias, and ethical decision support in clinical neuroscience.
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