UCL Institute of Health Informatics


New paper: Identifying features of patients with iNPH using natural language processing

23 November 2022

IHI's Prof Richard Dobson and cross-institutional team publish on the 'Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system'.

Figure taken from the relevant paper. It shows a flowchart of training, dataset and thematic analysis in three different colours. There is lots of text in boxes.

Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterise this cohort, with the intention to later target the development of artificial intelligence–driven tools for early detection.

In total, 293 eligible patients responsive to cerebrospinal fluid (CSF) diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92).

Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and haematological problems.

This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence–driven early detection of iNPH and prompt treatment.

Read the full paper