Show simple item record

dc.contributor.authorDe Crescenzo, Franco
dc.contributor.authorHarvey, Jade
dc.contributor.authorCipriani, Andrea
dc.identifier.citationNemanja Vaci, Qiang Liu, Andrey Kormilitzin, Franco De Crescenzo, Ayse Kurtulmus, Jade Harvey, Bessie O'Dell, Simeon Innocent, Anneka Tomlinson, Andrea Cipriani Alejo Nevado-Holgado. Natural language processing for structuring clinical text data on depression using UK-CRIS. Evidence-Based Mental Health 2020;23:21-26.en
dc.description.abstractBackground Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information. Objective Our aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research. Methods We used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures. Findings Results show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably. Conclusions This study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. Clinical implications Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.en
dc.description.sponsorshipSupported by the NIHRen
dc.subjectDepressive Disordersen
dc.subjectNatural Language Processingen
dc.titleNatural language processing for structuring clinical text data on depression using UK-CRISen

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record