## 5 Conclusions  
This review provides insights into the current state of machine learning approaches to ICP prediction in patients with TBI. While the reviewed studies demonstrated accuracy in predicting ICP crises, we identified several areas for future research, to advance the field and maximise the clinical applicability of prediction models. Further research efforts should focus on addressing three key aspects:  
1. Definition of ICP crises: The absence of a universally accepted definition for ICP crises poses a challenge to achieving consistency and comparability across studies. Establishing a standardized definition that encompasses specific thresholds and durations of elevated ICP would facilitate meaningful comparisons of prediction models. Future research should prioritize the establishment of a consensus definition, through expert panels and collaborative efforts involving multiple research institutions;
2. Optimal Prediction Horizon: Determining the optimal prediction horizon is essential for timely interventions and improved patient outcomes. Our review shows divergent views on the ideal time window for predicting ICP crises, with variations ranging from 30 min to 2 h. Resolving this discrepancy and establishing a widely accepted prediction horizon would guide researchers and clinicians in the development and application of prediction models. We suggest that 2 h is too long a time window, as it would potentially consume too many scarce healthcare resources. Studies that involve healthcare professionals with experience caring for TBI patients could provide crucial insights into the most clinically relevant prediction horizon;
3. Model Intelligibility: While the reviewed studies focused primarily on the performance aspects of machine learning algorithms, there is a need for ethical considerations and the impact on patients to be given increased attention. Research should prioritize the development of clinically interpretable models that provide transparency and intelligibility to patients and healthcare professionals. An evaluation of potential challenges--including ethical, legal and social implications--is needed, to ensure responsible and accountable deployment of machine learning approaches in clinical practice. While machine learning methods show promise in predicting ICP crises in TBI patients, this systematic review highlights the need for further research, to address critical gaps in the field. Establishing a consensus definition of ICP crises, determining the optimal prediction horizon and incorporating ethical considerations will strengthen the clinical relevance and applicability of prediction models, thus enhancing patient care and contributing to improved outcomes in the management of TBI. Conceptualization, G.R.E.B. and P.A.K. ; methodology, G.R.E.B. and M.R. ; formal analysis, G.R.E.B. and M.R. ; writing--original draft preparation, G.R.E.B; writing--review and editing, G.R.E.B. and P.A.K. ; visualisation, G.R.E.B. All authors have read and agreed to the published version of the manuscript. The study was financially supported by the [PERSON] Doctoral Studentship at City University of London. Not applicable. The authors declare no conflict of interest. * [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON] Management of intracranial hypertension. _Neurol. Clin._**2008**, _26_, 521-541. [CrossRef]
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