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While Dementia with Lewy Bodies (DLB) is the second most common type of neurodegenerative dementia following Alzheimer’s Disease (AD), it is difficult to distinguish from AD. We propose a method for DLB detection by using mental health record (MHR) documents from a (3-month) period before a patient has been diagnosed with DLB or AD. Our objective is to develop a model that could be clinically useful to differentiate between DLB and AD across datasets from different healthcare institutions. We cast this as a classification task using Convolutional Neural Network (CNN), an efficient neural model for text classification. We experiment with different representation models, and explore the features that contribute to model performances. In addition, we apply temperature scaling, a simple but efficient model calibration method, to produce more reliable predictions. We believe the proposed method has important potential for clinical applications using routine healthcare records, and for generalising to other relevant clinical record datasets. To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.

Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically. In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.

To automatically analyse complex trajectory information enclosed in clinical text (e.g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time. In the clinical domain, few temporally annotated corpora are currently available. Moreover, underlying annotation schemas - which mainly rely on the TimeML standard - are not necessarily easily applicable for applications such as patient timeline reconstruction. In this work, we investigated how temporal information is documented in clinical text by annotating a corpus of medical reports with time expressions (TIMEXes), based on TimeML. The developed corpus is available to the NLP community. Starting from our annotations, we analysed the suitability of the TimeML TIMEX schema for capturing timeline information, identifying challenges and possible solutions. As a result, we propose a novel annotation schema that could be useful for timeline reconstruction: CALendar EXpression (CALEX).

A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data., BioNLP 2019

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.

The 2016 Clinical TempEval continued the 2015 shared task on temporal information extraction with a new evaluation test set. Our team, UtahBMI, participated in all subtasks using machine learning a ...

The 2015 Clinical TempEval Challenge addressed the problem of temporal reasoning in the clinical domain by providing an annotated corpus of pathology and clinical notes related to colon cancer patients. The challenge consisted of six subtasks: TIMEX3 and event span detection, TIMEX3 and event attribute classification, document relation time and narrative container relation classification. Our BluLab team participated in all six subtasks. For the TIMEX3 and event subtasks, we developed a ClearTK support vector machine pipeline using mainly simple lexical features along with information from rule-based systems. For the relation subtasks, we employed a conditional random fields classification approach, with input from a rule-based system for the narrative container relation subtask. Our team ranked first for all TIMEX3 and event subtasks, as well as for the document relation subtask.

Generating Patient Problem Lists from the ShARe Corpus using SNOMED CT/SNOMED CT CORE Problem List

One of the most important features of health care is to be able to follow a patient’s progress over time and identify events in a temporal order. We describe initial steps in creating resources for automatic temporal reasoning of Swedish medical text. As a first step, we focus on the identification of temporal expressions by exploiting existing resources and systems available for English. We adapt the HeidelTime system and manually evaluate its performance on a small subset of Swedish intensive care unit documents. On this subset, the adapted version of HeidelTime achieves a precision of 92% and a recall of 66%. We also extract the most frequent temporal expressions from a separate, larger subset, and note that most expressions concern parts of days or specific times. We intend to further develop resources for temporal reasoning of Swedish medical text by creating a gold standard corpus also annotated with events and temporal links, in addition to temporal expressions and their normalised values.

Halla Norden is a web site with information regarding mobility between the Nordic countries in five different languages; Swedish, Danish, Norwegian, Icelandic and Finnish. We wanted to create a Nordic cross-language dictionary for the use in a cross-language search engine for Halla Norden. The entire set of texts on the web site was treated as one multilingual parallel corpus. From this we extracted parallel corpora for each language pair. The corpora were very sparse, containing on average less than 80 000 words per language pair. We have used the Uplug word alignment system (Tiedemann 2003a), for the creation of the dictionaries. The results gave on average 213 new dictionary words (frequency > 3) per language pair. The average error rate was 16 percent. Different combinations with Finnish had a higher error rate, 33 percent, whereas the error rate for the remaining language pairs only yielded on average 9 percent errors. The high error rate for Finnish is possibly due to the fact that the Finnish language belongs to a different language family. Although the corpora were very sparse the word alignment results for the combinations of Swedish, Danish, Norwegian and Icelandic were surprisingly good compared to other experiments with larger corpora.