Mining and Representing Unstructured Nicotine Use Data in a Structured Format for Secondary Use

The objective of this study was to use rules, NLP and machine learning for addressing the problem of clinical data interoperability across healthcare providers. Addressing this problem has the potential to make clinical data comparable, retrievable and exchangeable between healthcare providers. Our focus was in giving structure to unstructured patient smoking information. We collected our data from the MIMIC-III database. We wrote rules for annotating the data, then trained a CRF sequence classifier. We obtained an f-measure of 86%, 72%, 69%, 80%, and 12% for substance smoked, frequency, amount, temporal, and duration respectively. Amount smoked yielded a small value due to scarcity of related data. Then for smoking status we obtained an f-measure of 94.8% for non-smoker class, 83.0% for current-smoker, and 65.7% for past-smoker. We created a FHIR profile for mapping the extracted data based on openEHR reference models, however in future we will explore mapping to CIMI models

Алгоритмы и программное обеспечение идентификации временных конструкций в слабоструктурированных электронных медицинских текстах

Работа направлена на повышение эффективности анализа электронных медицинских карт (ЭМК) с помощью разработки инструментов автоматического извлечения временных конструкций из медицинской документации. Полученные инструменты позволят перенести данные конструкции на временную шкалу и представить их в удобном для медицинских сотрудников виде.The work is aimed at increasing the efficiency of the analysis of electronic medical records (EMR) by developing tools for the automatic extraction of temporary structures from medical records. The resulting tools will allow doctors to transfer these structures to the timeline and present them in a form convenient for medical staff

Алгоритмы и программное обеспечение идентификации временных конструкций в слабоструктурированных электронных медицинских текстах

Работа направлена на повышение эффективности анализа электронных медицинских карт (ЭМК) с помощью разработки инструментов автоматического извлечения временных конструкций из медицинской документации. Полученные инструменты позволят перенести данные конструкции на временную шкалу и представить их в удобном для медицинских сотрудников виде.The work is aimed at increasing the efficiency of the analysis of electronic medical records (EMR) by developing tools for the automatic extraction of temporary structures from medical records. The resulting tools will allow doctors to transfer these structures to the timeline and present them in a form convenient for medical staff

Novel Event Detection and Classification for Historical Texts

Event processing is an active area of research in the Natural Language Processing community but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts particularly in the current era of massive digitization of historical sources: research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorised into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry so far underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus and new models for automatic annotation

Table-to-Text: Generating Descriptive Text for Scientific Tables from Randomized Controlled Trials

Unprecedented amounts of data have been generated in the biomedical domain, and the bottleneck for biomedical research has shifted from data generation to data management, interpretation, and communication. Therefore, it is highly desirable to develop systems to assist in text generation from biomedical data, which will greatly improve the dissemination of scientific findings. However, very few studies have investigated issues of data-to-text generation in the biomedical domain. Here I present a systematic study for generating descriptive text from tables in randomized clinical trials (RCT) articles, which includes: (1) an information model for representing RCT tables; (2) annotated corpora containing pairs of RCT table and descriptive text, and labeled structural and semantic information of RCT tables; (3) methods for recognizing structural and semantic information of RCT tables; (4) methods for generating text from RCT tables, evaluated by a user study on three aspects: relevance, grammatical quality, and matching. The proposed hybrid text generation method achieved a low bilingual evaluation understudy (BLEU) score of 5.69; but human review achieved scores of 9.3, 9.9 and 9.3 for relevance, grammatical quality and matching, respectively, which are comparable to review of original human-written text. To the best of our knowledge, this is the first study to generate text from scientific tables in the biomedical domain. The proposed information model, labeled corpora and developed methods for recognizing tables and generating descriptive text could also facilitate other biomedical and informatics research and applications