The use of Natural Language Processing techniques to support Health Literacy: an evidence-based review

Background and objectives: To conduct a literature search and analysis of the existing research using natural language processing for improving or helping health literacy, as well as to discuss the importance and potentials of addressing both fields in a joint manner. This review targets researchers who are unfamiliar with natural language processing in the field of health literacy, and in general, any researcher, regardless of his or her background, interested in multi-disciplinary research involving technology and health care. Methods: We introduce the concepts of health literacy and natural language processing. Then, a thorough search is performed using relevant databases and well-defined criteria. We review the existing literature addressing these topics, both in an independent and joint manner, and provide an overview of the state of the art using natural language processing in health literacy. We additionally discuss how the different issues in health literacy that are related to the comprehension of specialised health texts can be improved using natural language processing techniques, and the challenges involved in these processes. Results: The search process yielded 235 potential relevant references, 49 of which fully fulfilled the established search criteria, and therefore they were later analysed in more detail. These articles were clustered into groups with respect to their purpose, and most of them were focused on the development of specific natural language processing modules, such as question answering, information retrieval, text simplification or natural language generation in order to facilitate the understanding of health information.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the projects, "Tratamiento inteligente de la informacion para la ayuda a la toma de decisiones" (GRE12-44), "Explotacion y tratamiento de la informacion disponible en Internet para la anotacion y generacion de textos adaptados al usuario" (GRE13-15), DIIM2.0 (PROMETEOII/2014/001), ATTOS (TIN2012-38536-C03-03), LEGOLANG-UAGE (TIN2012-31224), SAM (FP7-611312), and FIRST (FP7-287607)

A retrospective look at the predictions and recommendations from the 2009 AMIA Policy Meeting: Did we see EHR-related clinician burnout coming?

Clinicians often attribute much of their burnout experience to use of the electronic health record, the adoption of which was greatly accelerated by the Health Information Technology for Economic and Clinical Health Act of 2009. That same year, AMIA\u27s Policy Meeting focused on possible unintended consequences associated with rapid implementation of electronic health records, generating 17 potential consequences and 15 recommendations to address them. At the 2020 annual meeting of the American College of Medical Informatics (ACMI), ACMI fellows participated in a modified Delphi process to assess the accuracy of the 2009 predictions and the response to the recommendations. Among the findings, the fellows concluded that the degree of clinician burnout and its contributing factors, such as increased documentation requirements, were significantly underestimated. Conversely, problems related to identify theft and fraud were overestimated. Only 3 of the 15 recommendations were adjudged more than half-addressed

Automated Detection of Substance-Use Status and Related Information from Clinical Text

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability

Hypersensitivity Adverse Event Reporting in Clinical Cancer Trials: Barriers and Potential Solutions to Studying Severe Events on a Population Level

ABSTRACT HYPERSENSITIVITY ADVERSE EVENT REPORTING IN CLINICAL CANCER TRIALS: BARRIERS AND POTENTIAL SOLUTIONS TO STUDYING ALLERGIC EVENTS ON A POPULATION LEVEL by Christina Eldredge The University of Wisconsin-Milwaukee, 2020 Under the Supervision of Professor Timothy Patrick Clinical cancer trial interventions are associated with hypersensitivity events (HEs) which are recorded in the national clinical trial registry, ClinicalTrials.gov and publicly available. This data could potentially be leveraged to study predictors for HEs to identify at risk patients who may benefit from desensitization therapies to prevent these potentially life-threatening reactions. However, variation in investigator reporting methods is a barrier to leveraging this data for aggregation and analysis. The National Cancer Institute has developed the CTCAE classification system to address this barrier. This study analyzes the comprehensiveness of CTCAE to describe severe HEs in clinical cancer trials in comparison to other systems or terminologies. An XML parser was used to extract readable text from adverse event tables. Queries of the parsed data elements were performed to identify immune disorder events associated with biological and chemotherapy interventions. A data subset of severe anaphylactic and anaphylactoid events was created and analyzed. 1,331 clinical trials with 13088 immune disorder events occurred from September 20, 1999 to March 2018. 2409 (18.4%) of these were recorded as “serious” events. In the severe subset, MedDRA terminology, CTCAE or CTC classification systems were used to describe HEs, however, a large number of studies did not specify the system. The CTCAE term “anaphylaxis” was miscoded as “other (not including serious)” in 76.2% of events. The CTCAE classification system severity grades levels were not used to describe any of the severe events and the majority of terms did not include the allergen and therefore, in dual or multi- drug therapies, the etiologic agent was not identifiable. Furthermore, collection methods were not specified in 76% of events. Therefore, CTCAE was not found to improve the ability to capture event etiology or severity in anaphylaxis and anaphylactoid events in cancer clinical trials. Potential solutions to improving CTCAE HE description include adapting terms with a low percentage of HE severity miscoding (e.g. anaphylactic reaction) and terms which include drugs, biological agents and/or drug classes to improve study of anaphylaxis etiology and incidence in multi-drug cancer therapy, therefore, making a significant impact on patient safety

SIFR BioPortal : Un portail ouvert et générique d’ontologies et de terminologies biomédicales françaises au service de l’annotation sémantique

National audienceContexte – Le volume de données en biomédecine ne cesse de croître. En dépit d'une large adoption de l'anglais, une quantité significative de ces données est en français. Dans le do-maine de l’intégration de données, les terminologies et les ontologies jouent un rôle central pour structurer les données biomédicales et les rendre interopérables. Cependant, outre l'existence de nombreuses ressources en anglais, il y a beaucoup moins d'ontologies en français et il manque crucialement d'outils et de services pour les exploiter. Cette lacune contraste avec le montant considérable de données biomédicales produites en français, par-ticulièrement dans le monde clinique (e.g., dossiers médicaux électroniques). Methode & Résultats – Dans cet article, nous présentons certains résultats du projet In-dexation sémantique de ressources biomédicales francophones (SIFR), en particulier le SIFR BioPortal, une plateforme ouverte et générique pour l’hébergement d’ontologies et de terminologies biomédicales françaises, basée sur la technologie du National Center for Biomedical Ontology. Le portail facilite l’usage et la diffusion des ontologies du domaine en offrant un ensemble de services (recherche, alignements, métadonnées, versionnement, vi-sualisation, recommandation) y inclus pour l’annotation sémantique. En effet, le SIFR An-notator est un outil d’annotation basé sur les ontologies pour traiter des données textuelles en français. Une évaluation préliminaire, montre que le service web obtient des résultats équivalents à ceux reportés précedement, tout en étant public, fonctionnel et tourné vers les standards du web sémantique. Nous présentons également de nouvelles fonctionnalités pour les services à base d’ontologies pour l’anglais et le français

Safeguarding Privacy Through Deep Learning Techniques

Over the last few years, there has been a growing need to meet minimum security and privacy requirements. Both public and private companies have had to comply with increasingly stringent standards, such as the ISO 27000 family of standards, or the various laws governing the management of personal data. The huge amount of data to be managed has required a huge effort from the employees who, in the absence of automatic techniques, have had to work tirelessly to achieve the certification objectives. Unfortunately, due to the delicate information contained in the documentation relating to these problems, it is difficult if not impossible to obtain material for research and study purposes on which to experiment new ideas and techniques aimed at automating processes, perhaps exploiting what is in ferment in the scientific community and linked to the fields of ontologies and artificial intelligence for data management. In order to bypass this problem, it was decided to examine data related to the medical world, which, especially for important reasons related to the health of individuals, have gradually become more and more freely accessible over time, without affecting the generality of the proposed methods, which can be reapplied to the most diverse fields in which there is a need to manage privacy-sensitive information

Deep learning in clinical natural language processing: a methodical review.

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a long tail of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. DISCUSSION: Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). CONCLUSION: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field

MS 184 Guide to Carlos Vallbona, MD Papers (1968-2014)

The Carlos Vallbona, MD papers contains correspondence, course materials, slides; files from his 3701 Kirby office; and other material detailing the career of Dr. Vallbona as a pediatrician, educator, advocate, physical therapy and post-polio syndrome specialist. He held positions at Baylor College of Medicine and TIRR. The materials date from between 1968 and 2014. See more at MS 184