This thesis is an exploration of a problem that exists between cutting edge Natural Language Processing (NLP) methodologies and their real world exploitation in clinical research. I detail the development and validation of a range of NLP methodologies on clinical records, with a specific focus on the case of the symptomatology of Serious Mental Illness (SMI). This publication based thesis covers five main themes: Pre-work to describe the eld of NLP within the context of clinical data. The proposition, development and evaluation of the TextHunter desktop application, a suite of high-throughput tools to overcome bottlenecks in the development of NLP applications. The application of the tools to the novel domain of SMI symptomatology, enabling the development of language models for 46 symptom concepts with a median F1 score of 0.87, and enabling the pro ling of symptom distribution amongst 7 962 patients, based on discharge summaries. A knowledge discovery project using artificial neural networks and clustering techniques, to identify real world patterns of symptom depiction in clinical free text. Here, I demonstrate a granularity and diversity of vocabulary beyond what is described in standard clinical terminologies. A commentary on the realities of text analytics in the NHS, and the development of a software architecture 'CogStack' to address these. This culminated in the establishment of the Clinical Analytics Platform at King's College Hospital.