Volumetric and three-dimensional examination of sella turcica by cone-beam computed tomography: reference data for guidance to pathologic pituitary morphology

Background: The aim of the study was to assess the dimensions and volume of sella turcica in healthy Caucasian adults with normal occlusion and facial appearance from cone-beam computed tomography (CBCT) images. Materials and methods: CBCT images of 80 Caucasian adult patients (40 males, 40 females) with normal facial appearance and occlusion taken previously for diagnostic purposes were evaluated. Two groups were constructed in accordance to gender. The volume, length, diameter, and depth of the sella turcica were measured by Romexis software programme. Mann-Whitney U test and Independent t-tests were used for statistical analysis. Results: The mean lengths of the sella were 9.9 mm and 10.2 mm, depths were 9.2 mm and 8.8 mm and diameters were 12.3 mm and 12.1 mm in female and male groups, respectively. Between the genders, no statistically significant differences were found for any of the measurements. There were significantly higher values for the volume of sella turcica in males than in females (1102 ± 285.3 mm3 and 951.3 ± 278.5 mm3, respectively). Conclusions: The dimensions of sella turcica in healthy Caucasian adults with normal occlusion and facial appearance revealed nonsignificant differences between the genders. Individual variability in dimensions and gender differences in the volume are of importance in comparison of patients with craniofacial syndromes and aberrations. Knowledge concerning the dimensions and volume of sella turcica will be clinically relevant for a guidance to consciously realize pituitary disorders

Multilingual Word Sense Induction to Improve Web Search Result Clustering

In [12] a novel approach to Web search result clustering based on Word Sense Induction, i.e. the automatic discovery of word senses from raw text was presented; key to the proposed approach is the idea of, first, automatically in- ducing senses for the target query and, second, clustering the search results based on their semantic similarity to the word senses induced. In [1] we proposed an innovative Word Sense Induction method based on multilingual data; key to our approach was the idea that a multilingual context representation, where the context of the words is expanded by considering its translations in different languages, may im- prove the WSI results; the experiments showed a clear per- formance gain. In this paper we give some preliminary ideas to exploit our multilingual Word Sense Induction method to Web search result clustering

Automatic de-identification of textual documents in the electronic health record: a review of recent research

<p>Abstract</p> <p>Background</p> <p>In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.</p> <p>Methods</p> <p>This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers.</p> <p>Results</p> <p>The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries.</p> <p>Conclusions</p> <p>In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.</p

De-identification of primary care electronic medical records free-text data in Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Electronic medical records (EMRs) represent a potentially rich source of health information for research but the free-text in EMRs often contains identifying information. While de-identification tools have been developed for free-text, none have been developed or tested for the full range of primary care EMR data</p> <p>Methods</p> <p>We used <it>deid </it>open source de-identification software and modified it for an Ontario context for use on primary care EMR data. We developed the modified program on a training set of 1000 free-text records from one group practice and then tested it on two validation sets from a random sample of 700 free-text EMR records from 17 different physicians from 7 different practices in 5 different cities and 500 free-text records from a group practice that was in a different city than the group practice that was used for the training set. We measured the sensitivity/recall, precision, specificity, accuracy and F-measure of the modified tool against manually tagged free-text records to remove patient and physician names, locations, addresses, medical record, health card and telephone numbers.</p> <p>Results</p> <p>We found that the modified training program performed with a sensitivity of 88.3%, specificity of 91.4%, precision of 91.3%, accuracy of 89.9% and F-measure of 0.90. The validations sets had sensitivities of 86.7% and 80.2%, specificities of 91.4% and 87.7%, precisions of 91.1% and 87.4%, accuracies of 89.0% and 83.8% and F-measures of 0.89 and 0.84 for the first and second validation sets respectively.</p> <p>Conclusion</p> <p>The <it>deid </it>program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.</p

Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search

Radiology reports contain information that can be mined using a search engine for teaching, research, and quality assurance purposes. Current search engines look for exact matches to the search term, but they do not differentiate between reports in which the search term appears in a positive context (i.e., being present) from those in which the search term appears in the context of negation and uncertainty. We describe RadReportMiner, a context-aware search engine, and compare its retrieval performance with a generic search engine, Google Desktop. We created a corpus of 464 radiology reports which described at least one of five findings (appendicitis, hydronephrosis, fracture, optic neuritis, and pneumonia). Each report was classified by a radiologist as positive (finding described to be present) or negative (finding described to be absent or uncertain). The same reports were then classified by RadReportMiner and Google Desktop. RadReportMiner achieved a higher precision (81%), compared with Google Desktop (27%; p < 0.0001). RadReportMiner had a lower recall (72%) compared with Google Desktop (87%; p = 0.006). We conclude that adding negation and uncertainty identification to a word-based radiology report search engine improves the precision of search results over a search engine that does not take this information into account. Our approach may be useful to adopt into current report retrieval systems to help radiologists to more accurately search for radiology reports

Residents' views about family medicine specialty education in Turkey

<p>Abstract</p> <p>Background</p> <p>Residents are one of the key stakeholders of specialty training. The Turkish Board of Family Medicine wanted to pursue a realistic and structured approach in the design of the specialty training programme. This approach required the development of a needs-based core curriculum built on evidence obtained from residents about their needs for specialty training and their needs in the current infrastructure. The aim of this study was to obtain evidence on residents' opinions and views about Family Medicine specialty training.</p> <p>Methods</p> <p>This is a descriptive, cross-sectional study. The board prepared a questionnaire to investigate residents' views about some aspects of the education programme such as duration and content, to assess the residents' learning needs as well as their need for a training infrastructure. The questionnaire was distributed to the Family Medicine Departments (n = 27) and to the coordinators of Family Medicine residency programmes in state hospitals (n = 11) by e-mail and by personal contact.</p> <p>Results</p> <p>A total of 191 questionnaires were returned. The female/male ratio was 58.6%/41.4%. Nine state hospitals and 10 university departments participated in the study. The response rate was 29%. Forty-five percent of the participants proposed over three years for the residency duration with either extensions of the standard rotation periods in pediatrics and internal medicine or reductions in general surgery. Residents expressed the need for extra rotations (dermatology 61.8%; otolaryngology 58.6%; radiology 52.4%). Fifty-nine percent of the residents deemed a rotation in a private primary care centre necessary, 62.8% in a state primary care centre with a proposed median duration of three months. Forty-seven percent of the participants advocated subspecialties for Family Medicine, especially geriatrics. The residents were open to new educational methods such as debates, training with models, workshops and e-learning. Participation in courses and congresses was considered necessary. The presence of a department office and the clinical competency of the educators were more favored by state residents.</p> <p>Conclusions</p> <p>This study gave the Board the chance to determine the needs of the residents that had not been taken into consideration sufficiently before. The length and the content of the programme will be revised according to the needs of the residents.</p

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform