Combatting Veteran PTSD with Deep Learning on Longitudinal UK Health Data: A Comprehensive Review

Gbenga Adeniyi Adediran, Andrew Ayemere Okhueigbe, Ruth Ese Otaigboria, Chiamaka Pamela Agu, Chizube Obinna Chikezie

Abstract

PTSD in UK veterans often emerges long after service, complicating detection. The objective of this review is to assess whether deep learning applied to longitudinal NHS and Ministry of Defence data can facilitate a shift from late recognition to anticipatory intervention, and to define the requirements for its safe deployment. Evidence on sequence models (LSTM, transformers), multimodal integration of structured records and clinical notes, and explanation layers that render outputs legible to clinicians is synthesised. UK constraints, fragmented records, inconsistent veteran identifiers, and uneven digital maturity limit scale; feasible mitigations include secure data environments and federated training. Priority actions include standardising veteran coding, establishing a Veteran Health Analytics Hub, conducting prospective trials with health-economic endpoints, and integrating risk scores with concise, validated explanations into existing workflows. Properly implemented, longitudinal deep learning can reduce missed cases, accelerate access to effective support, and enable services to learn from their data while protecting privacy and trust.


10.22178/pos.122-8


Keywords


veteran PTSD; deep learning; longitudinal EHR; NHS data; model interpretability

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References


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Copyright (c) 2025 Gbenga Adeniyi Adediran, Andrew Ayemere Okhueigbe, Ruth Ese Otaigboria, Chiamaka Pamela Agu, Chizube Obinna Chikezie

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