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Management of functional intrathoracic sympathetic paragangliomas in succinate dehydrogenase subunit D (SDHD) mutation carriers is challenging, and there is no uniform guideline for treatment to date. The risks of potential malignant behavior and long-term cardiovascular morbidity have to be weighed against the risks of treatment complications. We report the multidisciplinary and shared decision-making approach that resulted in successful surgical removal of 3 paragangliomas in a SDHD mutation carrier. (C) 2020 by The Society of Thoracic Surgeons

Global public health surveillance relies on reporting structures and transmission of trustworthy health reports. But in practice, these processes may not always be fast enough, or are hindered by procedural, technical, or political barriers. GPHIN, the Global Public Health Intelligence Network, was designed in the late 1990s to scour mainstream news for health events, as that travels faster and more freely. This paper outlines the next generation of GPHIN, which went live in 2017, and reports on design decisions underpinning its new functions and innovations.

Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures. We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information. While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data., Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), Nov. 3rd, 2019, Hong Kong

MRI in patients with a cochlear implant: how to proceed An increasing number of cochlear implantations are being performed for the treatment of severe sensorineural hearing loss. Implant-associated complications leading to malfunction are of major importance since patients are strongly dependent on their cochlear implant (CI) for communication. Here we describe two patients with a CI who underwent MRI for diagnostic purposes and which resulted in dislocation of the internal CI magnet. CIs are generally non-compatible with MRI. However, by taking precautionary measures it is possible to perform MRI under certain conditions, depending on the type of CI and the magnetic flux density of the MRI scanner. When using 1.5 Tesla equipment, a firm bandage is required to prevent the CI magnet from dislocating. If 3 Tesla equipment is used, almost all CIs must be surgically removed prior to scanning. Despite these precautionary measures, the risk of complications still exists. Patient, referring physician and radiologist should be aware of the risks and disadvantages of performing MRI in patients with a CI.

Objectives Data Abstraction Assistant (DAA) is a software for linking items abstracted into a data collection form for a systematic review to their locations in a study report. We conducted a randomized cross-over trial that compared DAA-facilitated single-data abstraction plus verification (“DAA verification”), single data abstraction plus verification (“regular verification”), and independent dual data abstraction plus adjudication (“independent abstraction”). Study Design and Setting This study is an online randomized cross-over trial with 26 pairs of data abstractors. Each pair abstracted data from six articles, two per approach. Outcomes were the proportion of errors and time taken. Results Overall proportion of errors was 17% for DAA verification, 16% for regular verification, and 15% for independent abstraction. DAA verification was associated with higher odds of errors when compared with regular verification (adjusted odds ratio [OR] = 1.08; 95% confidence interval [CI]: 0.99–1.17) or independent abstraction (adjusted OR = 1.12; 95% CI: 1.03–1.22). For each article, DAA verification took 20 minutes (95% CI: 1–40) longer than regular verification, but 46 minutes (95% CI: 26 to 66) shorter than independent abstraction. Conclusion Independent abstraction may only be necessary for complex data items. DAA provides an audit trail that is crucial for reproducible research.

Objectives To maximize the proportion of relevant studies identified for inclusion in systematic reviews (recall), complex time-consuming Boolean searches across multiple databases are common. Although MEDLINE provides excellent coverage of health science evidence, it has proved challenging to achieve high levels of recall through Boolean searches alone. Study Design and Setting Recall of one Boolean search method, the clinical query (CQ), combined with a ranking method, support vector machine (SVM), or PubMed-related articles, was tested against a gold standard of studies added to 6 updated Cochrane reviews and 10 Agency for Healthcare Research and Quality (AHRQ) evidence reviews. For the AHRQ sample, precision and temporal stability were examined for each method. Results Recall of new studies was 0.69 for the CQ, 0.66 for related articles, 0.50 for SVM, 0.91 for the combination of CQ and related articles, and 0.89 for the combination of CQ and SVM. Precision was 0.11 for CQ and related articles combined, and 0.11 for CQ and SVM combined. Related articles showed least stability over time. Conclusions The complementary combination of a Boolean search strategy and a ranking strategy appears to provide a robust method for identifying relevant studies in MEDLINE.

Objective: To rebuild the software that underpins the Global Public Health Intelligence Network using modern natural language processing techniques to support recent and future improvements in situational awareness capability. Introduction: The Global Public Health Intelligence Network is a non-traditional all-hazards multilingual surveillance system introduced in 1997 by the Government of Canada in collaboration with the World Health Organization. 1 GPHIN software collects news articles, media releases, and incident reports and analyzes them for information about communicable diseases, natural disasters, product recalls, radiological events and other public health crises. Since 2016, the Public Health Agency of Canada (PHAC) and National Research Council Canada (NRC) have collaborated to replace GPHIN with a modular platform that incorporates modern natural language processing techniques to support more ambitious situational awareness goals. Methods: The updated GPHIN platform assembles several natural language processing tools to annotate incoming data in order to support situational awareness; broadly, GPHIN aims to extract knowledge from data. Data are collected in 10 languages and are machine translated to English. Several of the machine translation models use high performance neural networks. Language models are updated regularly and support external dictionaries for handling emerging domain-specific terms that might not yet exist in the parallel corpora used to train the models. All incoming documents are assigned a relevance score. Machine learning models estimate a score based on similarity to sets of known high-relevance and known low-relevance documents. PHAC analysts provide feedback on the scoring from time to time in the course of their work, and this feedback is used to periodically retrain scoring models. Documents are assigned keywords using two ontologies: an all-hazards multilingual taxonomy hand-compiled at PHAC, and the U.S. National Library of Medicine’s Unified Medical Language System (UMLS). Categories are assigned probabilistically to incoming articles (e.g., human infectious diseases, animal infectious diseases, substance abuse, environmental hazards), largely using affinity scores that correspond to keywords. Dates and times are resolved to canonical forms, so that mentions like last Tuesday get resolved to specific dates; this makes it possible to sequence data about a single event that are released at varying frequencies and with varying timeliness. Cities, states/provinces, and countries are identified in the documents, and gaps in the hierarchical geographic relationships are filled in. Locations are disambiguated based on collocations; the system distinguishes between and correctly resolves Ottawa, KS vs. Ottawa, ON, Canada, for example. Countries are displayed with their socio-economic population statistics (Gini coefficient, human development index, median age, and so on). The system attempts to detect and reconcile near-duplicate articles in order to handle instances where one article is released on a newswire and subsequently gets lightly edited and syndicated in dozens or hundreds of local papers; this improves the signal-to-noise ratio of the data in GPHIN for better productivity. Template-based reports (where the same document may get re-issued with a new number of cases but no other changes, for example) are still a challenge, but whitelisting tools reduce the false positive rate. The system provides tools for constructing arbitrarily detailed searches, tied to colour-coded maps and graphs that update on-the-fly, and offers short extractive summaries of each search result for easy filtering. GPHIN also generates topical knowledge graphs about sets of articles that seek to reveal surprising correlations in the data; for example, graphically reconciling and highlighting cases where several neighbouring countries all have reports of a mysterious disease and where a particular mosquito is mentioned. Next steps in the ongoing rejuvenation involve collating discrete articles and documents into narrative timelines that track an ongoing event: collecting all data about the spread of an infectious disease outbreak or perhaps the aftermath of an earthquake in the developing world. Our research is focussing on how to build line lists from such a stream of news articles about an event and how to detect important change points in the ongoing narrative. Results: The new GPHIN platform was launched in August 2016 in order to support syndromic surveillance activities for the Rio 2016 Olympics, and has been updated incrementally since then to offer further capabilities to professional users in 30 countries. Its modular construction supports current situational awareness activities as well as further research into advanced natural language processing techniques. Conclusions: We improved (and continue to improve) GPHIN with modern natural language processing techniques, including better translations, relevance scoring, categorization, near-duplicate detection, and improved data visualization tools, all towards the goal of more productive and more trustworthy situational awareness.

Background Data abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or “pin”) the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction. Methods This is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication). Discussion The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches. Trial registration The trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li. All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion. Electronic supplementary material The online version of this article (doi:10.1186/s13643-016-0373-7) contains supplementary material, which is available to authorized users.

Crigler-Najjar (CN) patients have no bilirubin UDP glucuronosyltransferase (UGT1A1) activity and suffer brain damage because of bilirubin toxicity. Vectors based on adeno-associated virus (AAV) serotype 2 transduce liver cells with relatively low efficiency. Recently, AAV serotypes 1, 6, and 8 have been shown to be more efficient for liver cell transduction. We compared AAV serotypes 1, 2, 6, and 8 for correction of UGT1A1 deficiency in the Gunn rat model of CN disease. Adult Gunn rats were injected with CMV-UGT1A1 AAV vectors. Serum bilirubin was decreased over the first year by 64% for AAV1, 16% for AAV2, 25% for AAV6, and 35% for AAV8. Antibodies to UGT1A1 were detected after injection of all AAV serotypes. An AAV1 UGT1A1 vector with the liver-specific albumin promoter corrected serum bilirubin levels but did not induce UGT1A1 antibodies. Two years after injection of AAV vectors all animals had large lipid deposits in the liver. These lipid deposits were not seen in age-matched control animals. AAV1 vectors are promising candidates for CN gene therapy because they can mediate a reduction in serum bilirubin levels in Gunn rats that would be therapeutic in humans.

This paper describes the National Research Council of Canada's submission to the 2011 i2b2 NLP challenge on the detection of emotions in suicide notes. In this task, each sentence of a suicide note is annotated with zero or more emotions, making it a multi-label sentence classification task. We employ two distinct large-margin models capable of handling multiple labels. The first uses one classifier per emotion, and is built to simplify label balance issues and to allow extremely fast development. This approach is very effective, scoring an F-measure of 55.22 and placing fourth in the competition, making it the best system that does not use web-derived statistics or re-annotated training data. Second, we present a latent sequence model, which learns to segment the sentence into a number of emotion regions. This model is intended to gracefully handle sentences that convey multiple thoughts and emotions. Preliminary work with the latent sequence model shows promise, resulting in comparable performance using fewer features.

The Receiver-Operating Characteristic curve or ROC has been a long standing and well appreciated tool to assess performance of classifiers or diagnostic tests. Likewise, the Area Under the ROC (AUC) has been a metric to summarize the power of a test or ability of a classifier in one measurement. This article aims to revisit the AUC, and ties it to key characteristics of the noncentral hypergeometric distribution. It is demonstrated that this statistical distribution can be used in modeling the behaviour of classifiers, which is of value for comparing classifiers.

Objective As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge. Design The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline. Measurements Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set. Results The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second). Conclusion for all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks.

Background Clinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs). Methods ExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study. Results We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (second stage) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers. Conclusions Our experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).

The authors performed this study to determine the accuracy of several text classification methods to categorize wrist x-ray reports. We randomly sampled 751 textual wrist x-ray reports. Two expert reviewers rated the presence (n = 301) or absence (n = 450) of an acute fracture of wrist. We developed two information retrieval (IR) text classification methods and a machine learning method using a support vector machine (TC-1). In cross-validation on the derivation set (n = 493), TC-1 outperformed the two IR based methods and six benchmark classifiers, including Naive Bayes and a Neural Network. In the validation set (n = 258), TC-1 demonstrated consistent performance with 93.8% accuracy; 95.5% sensitivity; 92.9% specificity; and 87.5% positive predictive value. TC-1 was easy to implement and superior in performance to the other classification methods.

Spoken audio documents are becoming more and more common on the World Wide Web, and this is likely to be accelerated by the widespread deployment of broadband technologies. Unfortunately, speech documents are inherently hard to browse because of their transient nature. One approach to this problem is to label segments of a spoken document with keyphrases that summarise them. In this paper, we investigate an approach for automatically extracting keyphrases from spoken audio documents. We use a keyphrase extraction system (Extractor) originally developed for text, and apply it to errorful Speech Recognition transcripts, which may contain multiple hypotheses for each of the utterances. We show that keyphrase extraction is an "easier" task than full text transcription and that keyphrases can be extracted with reasonable precision from transcripts with Word Error Rates (WER) as high as 62%. This robustness to noise can be attributed to the fact that keyphrase words have a lower WER than non-keyphrase words and that they tend to have more redundancy in the audio. From this we conclude that keyphrase extraction is feasible for a wide range of spoken documents, including less-than-broadcast casual speech. We also show that including multiple utterance hypotheses does not improve the precision of the extracted keyphrases., In Information Retrieval Techniques for Speech Applications, August, 2002

A method is presented to classify pathology reports that were composed in natural language. This way, the pathologist need not formulate a formal classification, but can adopt automatically suggested classifications. This paper describes the underlying theoretical aspects of the nearest neighbour method that was used, gives considerations for practical use, and presents the results of two studies on the method.

Background This paper examines how the adoption of a subject-specific library service has changed the way in which its users interact with a digital library. The LitMiner text-analysis application was developed to enable biologists to explore gene relationships in the published literature. The application features a suite of interfaces that enable users to search PubMed as well as local databases, to view document abstracts, to filter terms, to select gene name aliases, and to visualize the co-occurrences of genes in the literature. At each of these stages, LitMiner offers the functionality of a digital library. Documents that are accessible online are identified by an icon. Users can also order documents from their institution's library collection from within the application. In so doing, LitMiner aims to integrate digital library services into the research process of its users. Methods Case study Results This integration of digital library services into the research process of biologists results in increased access to the published literature. Conclusion In order to make better use of their collections, digital libraries should customize their services to suit the research needs of their patrons.