Communicating in Emergency Settings: BabelDr or Telephone Interpreting?

In this paper, we present a study comparing two mediums that can be used to communicate with allophone patients: a speech-enabled phraselator (BabelDr) and telephone interpreting. To identify the satisfaction provided by these mediums and their pros and cons, we conducted a crossover experiment where doctors and standardized patients completed anamneses and filled in surveys. Our findings suggest that telephone interpreting offers better overall satisfaction, but both mediums presented advantages. Consequently, we argue BabelDr and telephone interpreting can be complementary.

Translating Medical Dialogues into Pictographs: An Approach Using UMLS.

This paper describes a first attempt to map UMLS concepts to pictographs as a resource for translation systems for the medical domain. An evaluation of pictographs from two freely available sets shows that for many concepts no pictograph could be found and that word-based lookup is inadequate for this task.

Predicting the Linguistic Accessibility of Chinese Health Translations: Machine Learning Algorithm Development.

BACKGROUND: Linguistic accessibility has an important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been understudied. OBJECTIVE: Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. METHODS: Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from the China Center for Disease Control and Prevention. RESULTS: A pairwise adjusted t test showed that the following 3 new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of area under the curve: C5.0 decision tree (95% CI -0.249 to -0.152; P<0.001), random forest (95% CI 0.139-0.239; P<0.001) and extreme gradient boosting tree (95% CI 0.099-0.193; P<0.001). There was, however, no significant difference between C5.0 decision tree and random forest (P=0.513). The extreme gradient boosting tree was the best model, achieving statistically significant improvement over the C5.0 model (P=0.003) and the random forest model (P=0.006) at an adjusted Bonferroni P value at 0.008. CONCLUSIONS: The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.

[I'm having trouble communicating with my patient at the emergency unit: what tools are available ?] / J'ai de la peine à communiquer avec mon patient aux urgences : quels sont les outils disponibles ?

Today's healthcare systems are increasingly confronted with communication problems between allophone patients and health care staff. Geneva, due to its cosmopolitan character, is at the core of this phenomenon. Several studies attest to the negative effects of the language barrier and its consequences on the quality of care, ethics, safety and financial costs. Different tools, such as semi-professional interpreters or translation applications, make it possible to deal with situations where a lack of communication can be crucial. However, they have many drawbacks. Therefore, the Geneva University Hospitals, in collaboration with the Faculty of Translation and Interpretation, have developed a reliable and innovative tool for the translation of medical language.

Les systèmes de santé actuels sont de plus en plus confrontés à des problèmes de communication entre des patients allophones et le personnel soignant. Genève, du fait de son aspect cosmopolite, est au centre de ce phénomène. Plusieurs études attestent des effets négatifs de la barrière de la langue et ses conséquences sur la qualité des soins, l'éthique, la sécurité et les coûts financiers. Différents outils, comme les interprètes semi-professionnels ou les applications de traduction, permettent de faire face à des situations où un défaut de communication peut s'avérer crucial. Cependant, ils présentent de nombreux inconvénients. Par conséquent, les HUG, en collaboration avec la Faculté de traduction et d'interprétation, ont développé un outil fiable et innovant pour la traduction du langage médical.

An Albanian Text-to-Speech System for the BabelDr Medical Speech Translator.

In this paper we present work on creating and evaluating a Text-to-Speech system for the Albanian language to be used in the BabelDr medical speech translation system. Its quality was assessed by twelve native speakers who provided feedback on 60 prompts generated by the synthesizer and on 60 real human recordings across three dimensions, namely comprehensibility, naturalness and likeability. The results suggest that the newly created voice can be incorporated in the content creation pipeline of the BabelDr platform.

User Satisfaction with a Speech-Enabled Translator in Emergency Settings.

In medical emergency situations, the language barrier is often a problem for healthcare quality. To face this situation, we developed BabelDr, an innovative and reliable fixed phrase speech-enabled translator specialised for medical language. Majority of participants (>85%) showed a positive satisfaction level using BabelDr.

A Speech-Enabled Fixed-Phrase Translator for Emergency Settings: Crossover Study.

BACKGROUND: In the context of the current refugee crisis, emergency services often have to deal with patients who have no language in common with the staff. As interpreters are not always available, especially in emergency settings, medical personnel rely on alternative solutions such as machine translation, which raises reliability and data confidentiality issues, or medical fixed-phrase translators, which sometimes lack usability. A collaboration between Geneva University Hospitals and Geneva University led to the development of BabelDr, a new type of speech-enabled fixed-phrase translator. Similar to other fixed-phrase translators (such as Medibabble or UniversalDoctor), it relies on a predefined list of pretranslated sentences, but instead of searching for sentences in this list, doctors can freely ask questions. OBJECTIVE: This study aimed to assess if a translation tool, such as BabelDr, can be used by doctors to perform diagnostic interviews under emergency conditions and to reach a correct diagnosis. In addition, we aimed to observe how doctors interact with the system using text and speech and to investigate if speech is a useful modality in this context. METHODS: We conducted a crossover study in December 2017 at Geneva University Hospitals with 12 French-speaking doctors (6 doctors working at the outpatient emergency service and 6 general practitioners who also regularly work in this service). They were asked to use the BabelDr tool to diagnose two standardized Arabic-speaking patients (one male and one female). The patients received a priori list of symptoms for the condition they presented with and were instructed to provide a negative or noncommittal answer for all other symptoms during the diagnostic interview. The male patient was standardized for nephritic colic and the female, for cystitis. Doctors used BabelDr as the only means of communication with the patient and were asked to make their diagnosis at the end of the dialogue. The doctors also completed a satisfaction questionnaire. RESULTS: All doctors were able to reach the correct diagnosis based on the information collected using BabelDr. They all agreed that the system helped them reach a conclusion, even if one-half felt constrained by the tool and some considered that they could not ask enough questions to reach a diagnosis. Overall, participants used more speech than text, thus confirming that speech is an important functionality in this type of tool. There was a negative association (P=.02) between the percentage of successful speech interactions (spoken sentences sent for translation) and the number of translated text items, showing that the doctors used more text when they had no success with speech. CONCLUSIONS: In emergency settings, when no interpreter is available, speech-enabled fixed-phrase translators can be a good alternative to reliably collect information from the patient.

Semantic Relations in Compound Nouns: Perspectives from Inter-Annotator Agreement.

Semantic relations have been studied for decades without yet reaching consensus on the set of these relations. However, biomedical language processing and ontologies rely on these relations, so it is important to be able to evaluate their suitability. In this paper we examine the role of inter-annotator agreement in choosing between competing proposals regarding the set of such relations. The experiments consisted of labeling the semantic relations between two elements of noun-noun compounds (e.g. cell migration). Two judges annotated a dataset of terms from the biomedical domain using two competing sets of relations and analyzed the inter-annotator agreement. With no training and little documentation, agreement on this task was fairly high and disagreements were consistent. The results support the utility of the relation-based approach to semantic representation.

Breaking the language barrier: machine assisted diagnosis using the medical speech translator.

In this paper, we describe and evaluate an Open Source medical speech translation system (MedSLT) intended for safety-critical applications. The aim of this system is to eliminate the language barriers in emergency situation. It translates spoken questions from English into French, Japanese and Finnish in three medical subdomains (headache, chest pain and abdominal pain), using a vocabulary of about 250-400 words per sub-domain. The architecture is a compromise between fixed-phrase translation on one hand and complex linguistically-based systems on the other. Recognition is guided by a Context Free Grammar Language Model compiled from a general unification grammar, automatically specialised for the domain. We present an evaluation of this initial prototype that shows the advantages of this grammar-based approach for this particular translation task in term of both reliability and use.