Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.

Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches - a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database. Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database. The novelty of the two approaches lies in the malleability of each classifier if a need to refine performance, or meet alternate classification requirements arises. The algorithms can also be adapted to fit infrastructures of other clinical datasets given sufficient clinical recording practice knowledge, without dependency on medical codes or additional data extraction of known risk factors to predict suicidal behaviour.

Motor signs in patients with dementia are associated with a higher risk of cognitive decline, institutionalisation, death and increased health care costs, but prevalences differ between studies. The aims of this study were to employ a natural language processing pipeline to detect motor signs in a patient cohort in routine care; to explore which other difficulties occur co-morbid to motor signs; and whether these, as a group and individually, predict adverse outcomes.

We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes.

It is well known that loneliness can worsen physical and mental health outcomes, but there is a dearth of research on the impact of loneliness in populations receiving mental healthcare. This study aimed to investigate cross-sectional correlates of loneliness among such patients and longitudinal risk for acute general hospitalisations.

Antipsychotic treatments are associated with safety concerns in people with dementia. The authors aimed to investigate whether risk of adverse outcomes related to antipsychotic prescribing differed according to major neuropsychiatric syndromes-specifically psychosis, agitation, or a combination. A cohort of 10,106 patients with a diagnosis of dementia was assembled from a large dementia care database in South East London. Neuropsychiatric symptoms closest to first dementia diagnosis were determined according to the Health of the Nation Outcome Scales' mental and behavioural problem scores and the sample was divided into four groups: 'agitation and psychosis', 'agitation, but no psychosis', 'psychosis, but no agitation', and 'neither psychosis nor agitation'. Antipsychotic prescription in a one-year window around first dementia diagnosis was ascertained as exposure variable through natural language processing from free text. Cox regression models were used to analyse associations of antipsychotic prescription with all-cause and stroke-specific mortality, emergency hospitalisation and hospitalised stroke adjusting for sixteen potential confounders including demographics, cognition, functioning, as well as physical and mental health. Only in the group 'psychosis, but no agitation' (n = 579), 30% of whom were prescribed an antipsychotic, a significant antipsychotic-associated increased risk of hospitalised stroke was present after adjustment (adjusted hazard ratio (HR) 2.16; 95% confidence interval (CI) 1.09-4.25). An increased antipsychotic-related all-cause (adjusted HR 1.14; 95% CI 1.04-1.24) and stroke-specific mortality risk (adjusted HR 1.28; 95% CI 1.01-1.63) was detected in the whole sample, but no interaction between the strata and antipsychotic-related mortality. In conclusion, the adverse effects of antipsychotics in dementia are complex. Stroke risk may be highest when used in patients presenting with psychosis without agitation, indicating the need for novel interventions for this group.

The higher mortality rates in people with severe mental illness (SMI) may be partly due to inadequate integration of physical and mental healthcare. Accurate recording of SMI during hospital admissions has the potential to facilitate integrated care including tailoring of treatment to account for comorbidities. We therefore aimed to investigate the sensitivity of SMI recording within general hospitals, changes in diagnostic accuracy over time, and factors associated with accurate recording.

Delirium is an acute and fluctuating change in attention and cognition that increases the risk of functional decline, institutionalisation and death in hospitalised patients. After delirium, patients have a significantly higher risk of readmission to hospital. Our aim was to investigate factors associated with hospital readmission in people with delirium.

Across international contexts, people with serious mental illnesses (SMI) experience marked reductions in life expectancy at birth. The intersection of ethnicity and social deprivation on life expectancy in SMI is unclear. The aim of this study was to assess the impact of ethnicity and area-level deprivation on life expectancy at birth in SMI, defined as schizophrenia-spectrum disorders, bipolar disorders and depression, using data from London, UK.

Loneliness is associated with psychiatric morbidity. Restrictions placed on the population during the first COVID-19 lockdown may have disproportionately affected older adults, possibly through increasing loneliness. We sought to investigate this by examining loneliness in referrals to mental health of older adults (MHOA) services during the first UK COVID-19 lockdown.

Social distancing restrictions in the COVID-19 pandemic may have had adverse effects on older adults' mental health. Whereby the impact on mood is well-described, less is known about psychotic symptoms. The aim of this study was to compare characteristics associated with psychotic symptoms during the first UK lockdown and a pre-pandemic comparison period.

To identify predictors of recurrent cerebrovascular morbidity in a cohort of patients with depression and a cerebrovascular disease (CBVD) history.

Accurate recognition and recording of intellectual disability in those who are admitted to general hospitals is necessary for making reasonable adjustments, ensuring equitable access, and monitoring quality of care. In this study, we determined the rate of recording of intellectual disability in those with the condition who were admitted to hospital and factors associated with the condition being unrecorded.

High smoking prevalence is a major public health concern for people with mental disorders. Improved monitoring could be facilitated through electronic health record (EHR) databases. We evaluated whether EHR information held in structured fields might be usefully supplemented by open-text information. The prevalence and correlates of EHR-derived current smoking in people with severe mental illness were also investigated.