Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Metalinguistic Knowledge and Language Ability in University-Level L2 Learners

Existing research indicates that instructed learners' L2 proficiency and their metalinguistic knowledge are moderately correlated. However, the operationalization of the construct of metalinguistic knowledge has varied somewhat across studies. Metalinguistic knowledge has typically been operationalized as learners' ability to correct, describe, and explain L2 errors. More recently, this operationalization has been extended to additionally include learners' L1 language-analytic ability as measured by tests traditionally used to assess components of language learning aptitude. This article reports on a study which employed a narrowly focused measure of L2 proficiency and incorporated L2 language-analytic ability into a measure of metalinguistic knowledge. It was found that the linguistic and metalinguistic knowledge of advanced university-level L1 English learners of L2 German correlated strongly. Moreover, the outcome of a principal components analysis suggests that learners' ability to correct, describe, and explain highlighted L2 errors and their L2 language-analytic ability may constitute components of the same construct. The theoretical implications of these findings for the concept of metalinguistic knowledge in L2 learning are considered. © Oxford University Press 2007

Computerization of African languages-French dictionaries

This paper relates work done during the DiLAF project. It consists in converting 5 bilingual African language-French dictionaries originally in Word format into XML following the LMF model. The languages processed are Bambara, Hausa, Kanuri, Tamajaq and Songhai-zarma, still considered as under-resourced languages concerning Natural Language Processing tools. Once converted, the dictionaries are available online on the Jibiki platform for lookup and modification. The DiLAF project is first presented. A description of each dictionary follows. Then, the conversion methodology from .doc format to XML files is presented. A specific point on the usage of Unicode follows. Then, each step of the conversion into XML and LMF is detailed. The last part presents the Jibiki lexical resources management platform used for the project.Comment: 8 page

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Towards a collocation writing assistant for learners of Spanish

This paper describes the process followed in creating a tool aimed at helping learners produce collocations in Spanish. First we present the Diccionario de colocaciones del español (DiCE), an online collocation dictionary, which represents the first stage of this process. The following section focuses on the potential user of a collocation learning tool: we examine the usability problems DiCE presents in this respect, and explore the actual learner needs through a learner corpus study of collocation errors. Next, we review how collocation production problems of English language learners can be solved using a variety of electronic tools devised for that language. Finally, taking all the above into account, we present a new tool aimed at assisting learners of Spanish in writing texts, with particular attention being paid to the use of collocations in this language

How Jurors Evaluate Fingerprint Evidence: The Relative Importance of Match Language, Method Information, and Error Acknowledgment

Fingerprint examiners use a variety of terms and phrases to describe a finding of a match between a defendant\u27s fingerprints and fingerprint impressions collected from a crime scene. Despite the importance and ubiquity of fingerprint evidence in criminal cases, no prior studies examine how jurors evaluate such evidence. We present two studies examining the impact of different match phrases, method descriptions, and statements about possible examiner error on the weight given to fingerprint identification evidence by laypersons. In both studies, the particular phrase chosen to describe the finding of a match-whether simple and imprecise or detailed and claiming near certainty-had little effect on participants\u27 judgments about the guilt of a suspect. In contrast, the examiner admitting the possibility of error reduced the weight given to the fingerprint evidence-regardless of whether the admission was made during direct or cross-examination. In addition, the examiner providing information about the method used to make fingerprint comparisons reduced the impact of admitting the possibility of error. We found few individual differences in reactions to the fingerprint evidence across a wide range of participant variables, and we found widespread agreement regarding the uniqueness of fingerprints and the reliability of fingerprint identifications. Our results suggest that information about the reliability of fingerprint identifications will have a greater impact on lay interpretations of fingerprint evidence than the specific qualitative or quantitative terms chosen to describe a fingerprint match