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For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case., QC 20191028

In this paper we describe the participation of the KCL-Health-NLP team in the CLEF eHealth 2018 lab, specifically Task 1: Multilingual Information Extraction-ICD10 coding. The task involves the automatic coding of causes of death in death certificates in French, Italian and Hungarian according to the ICD-10 taxonomy. Choosing to work on the two Romance languages, we treated the task as a sequence-to-sequence prediction problem. Our system has an encoder-decoder architecture, with convolutional neural networks based on character em-beddings as encoders and recurrent neural network decoders. Our hypothesis was that a character-level representation would allow our model to generalise across two genealogically related languages. Results obtained by pre-training our Italian model on the French data set confirmed this intuition. We also explored the impact of character-level features extracted from dictionary-matched ICD codes. We obtained F-measures of 0.72/0.64 and 0.78 on the French aligned/raw and Italian raw internal test data, respectively. On the blind test set released by the task organisers, our top results were 0.65/0.52 and 0.69 F-measure, respectively., QC 20180906

For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain., QC 20220622Part of proceedings: ISBN 978-194808774-2