Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit

Kraljević, Željko; Searle, Thomas; Shek, Anthony; Roguski, Łukasz; Noor, Kawsar; Bean, Daniel; Mascio, Aurelie; Zhu, Leilei; Folarin, Amos; Roberts, Angus; Bendayan, Rebecca; Richardson, Mark P.; Stewart, Robert; Shah, Anoop D; Wong, Wai Keong; Ibrahim, Zina; Teo, James; Dobson, Richard

doi:10.1016/j.artmed.2021.102083

Cited by 116 publications

(101 citation statements)

References 29 publications

(31 reference statements)

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Section: Methodsmentioning

confidence: 99%

“…To extract SNOMED concepts from free text we used MedCAT,29 a medical concept annotation toolkit capable of named entity recognition linking (NER+L) with contextualisation. The base model used is described in Kraljevic et al ,29 and has shown very good performance (F1=0.90). In a first step, the base model was enriched with concept names from UMLS with the purpose of increasing recall and potentially catching all different name-forms for each concept.…”

Section: Methodsmentioning

confidence: 99%

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Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register

et al. 2022

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ObjectivesThe first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD).DesignObservational study.SettingSecondary mental healthcare services from South LondonParticipantsOur maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018.MeasuresWe designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale.ResultsPhysical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses.ConclusionsOur data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register

et al. 2022

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“…This simply means each phrase was assigned an ID and stored into a MedCAT concept database (CDB). 14 The database holds pairs of phrase->ID. Each concept is an abstract entity rather than a concept linked to a health ontology.…”

Section: Methodsmentioning

confidence: 99%

“…The cogstack suite of tools (DrugPipeline, 19 MedCAT 14 and MedCATTrainer 20 ) used for text extraction and NLP is available on https://github.com/CogStack under an open-source license (Apache V.2.0 license).…”

Section: Methodsmentioning

confidence: 99%

“…For each N-Gram, a 300-dimensional vector embedding was calculated using Word2Vec 11 with window_size=10, number_of_iterations=2 and minimum_word_frequency=10. All of this was done using MedCAT 14 which internally relies on gensim. 15 Given a set of root n-grams (“ceiling of care”, “withdrawal of care”, “limit of care”, “palliative treatments only”, “palliative care only”, “end of life care”, “liverpool care pathway”, “not for intubation”, “not for itu”, “not for critical care”), the top 10 most similar n-grams to each of the roots were collected based on the cosine distance between embeddings.…”

Section: Methodsmentioning

confidence: 99%

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Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life

Lau

Kraljević

Al-Agil

et al. 2021

BMJ Health Care Inform

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ObjectivesTo clarify real-world linguistic nuances around dying in hospital as well as inaccuracy in individual-level prognostication to support advance care planning and personalised discussions on limitation of life sustaining treatment (LST).DesignRetrospective cross-sectional study of real-world clinical data.SettingSecondary care, urban and suburban teaching hospitals.ParticipantsAll inpatients in 12-month period from 1 October 2018 to 30 September 2019.MethodsUsing unsupervised natural language processing, word embedding in latent space was used to generate phrase clusters with most similar semantic embeddings to ‘Ceiling of Treatment’ and their prognostication value.ResultsWord embeddings with most similarity to ‘Ceiling of Treatment’ clustered around phrases describing end-of-life care, ceiling of care and LST discussions. The phrases have differing prognostic profile with the highest 7-day mortality in the phrases most explicitly referring to end of life—‘Withdrawal of care’ (56.7%), ‘terminal care/end of life care’ (57.5%) and ‘un-survivable’ (57.6%).ConclusionVocabulary used at end-of-life discussions are diverse and has a range of associations to 7-day mortality. This highlights the importance of correct application of terminology during LST and end-of-life discussions.

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Angiotensin‐converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID‐19 infection in a multi‐site UK acute hospital trust

Bean

Kraljević

Searle

et al. 2020

European J of Heart Fail

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133

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Aims:The SARS-Cov2 virus binds to the ACE2 receptor for cell entry. It has been suggested that ACEinhibitors (ACEi) and Angiotensin-2 Blockers (ARB), which are commonly used in patients with hypertension or diabetes and may raise tissue ACE2 levels, could increase the risk of severe COVID19 infection. Methods and Results:We evaluated this hypothesis in a consecutive cohort of 1200 acute inpatients with COVID19 at two hospitals with a multi-ethnic catchment population in London (UK). The mean age was 68±17 years (57% male) and 74% of patients had at least 1 comorbidity. 415 patients (34.6%) reached the primary endpoint of death or transfer to a critical care unit for organ support within 21-days of symptom onset. 399 patients (33.3 %) were taking ACEi or ARB. Patients on ACEi/ARB were significantly older and had more comorbidities. The odds ratio (OR) for the primary endpoint in patients on ACEi and ARB, after adjustment for age, sex and co-morbidities, was 0.63 (CI 0.47-0.84, p<0.01). Conclusions:There was no evidence for increased severity of COVID19 disease in hospitalised patients on chronic treatment with ACEi or ARB. A trend towards a beneficial effect of ACEi/ARB requires further evaluation in larger meta-analyses and randomised clinical trials.

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Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit

Cited by 116 publications

References 29 publications

Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register

Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register

Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life

Angiotensin‐converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID‐19 infection in a multi‐site UK acute hospital trust

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