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Evaluation of Machine Translation Methods Applied to Medical Terminologies

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Evaluation of Machine Translation Methods Applied to Medical Terminologies Evaluation of Machine Translation Methods applied to Medical Terminologies Konstantinos Skianis Yann Briand, Florent Desgrippes BLUAI Agence du Numerique´ en Sante´ Athens, Greece Paris, France [email protected] [email protected] [email protected] Abstract process can be expensive both in terms of time and resources, while the vocabulary and number of Medical terminologies resources and stan- medical terms can reach high numbers and require dards play vital roles in clinical data ex- changes, enabling significantly the services’ health professional efforts for evaluation. interoperability within healthcare national in- This work constitutes a generic, language- formation networks. Health and medical sci- independent and open methodology for medical ence are constantly evolving causing require- terminology translation. To illustrate our approach, ments to advance the terminologies editions. which is based on automated machine translation In this paper, we present our evaluation work methods, we will attempt to develop a first baseline of the latest machine translation techniques ad- translation from English to French for the ICD- dressing medical terminologies. Experiments 11 classification. We also test on the International have been conducted leveraging selected statis- tical and neural machine translation methods. Classification of Functioning, Disability and Health 2 The devised procedure is tested on a validated (ICF) terminology . sample of ICD-11 and ICF terminologies from First, we are going to investigate existing ma- English to French with promising results. chine translation research studies concerning medi- cal terms and documents, with a comparison of the 1 Introduction relative methods. Next, we present our proposed Medical terminologies are of essential importance methodology. Afterwards, we show our experi- for health institutions to store, organize and ex- ments and results. Last, we conclude with recom- change all medical-related data generated in labs, mendations for future work. hospitals and other healthcare entities. They are ar- 2 Related Work ranged systematically in dictionaries and lexicons, that follow specific structures and coding rules. In Translating medical terminologies has been a well- order to facilitate hierarchies and connections, the studied topic, with many approaches coming from terms are represented by ontologies, enabling us machine translation. Traditional machine transla- to keep additional information (e.g. a family of tion models first incorporated statistical models, diseases). whose parameters are set through the analysis of WHO International Classification of Diseases bilingual text corpora. (ICD)1 terminology is a diagnostic classification Statistical machine translation (SMT) Eck standard for epidemiology, clinical and research et al.(2004) investigated the usefulness of a large purposes. It is the most used medical dictionary medical database (the Unified Medical Language across national health organizations worldwide. System) for the translation of dialogues between WHO is responsible to maintain the ICD editions doctors and patients using a statistical machine for the English language. ICD-11 is the latest edi- translation system. They showed that the extrac- tion, adopted on May 25th, 2019. As the initial tion of a large dictionary and the usage of semantic medical lexicons which contain these ontologies type information to generalize the training data sig- are created in English, there is an evident need nificantly improves the translation performance. for translation in other languages. This translation 2 1 http://bioportal.lirmm.fr/ontologies/ https://icd.who.int/en ICF 59 Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 59–69 November 20, 2020. c 2020 Association for Computational Linguistics https://doi.org/10.18653/v1/P17 Resources Type Method Languages Nystrom¨ et al.(2006) ICD-10, ICF, MeSH SMT Alignment En-Swe Deleger´ et al.(2010) MeSH, SNMI, MedDRA 17, WHO-ART SMT Knowledge, Corpus En-Fr Laroche and Langlais(2010) Wiki SMT Projection-based Fr-En Dusekˇ et al.(2014) EMEA, UMLS, MAREC SMT Domain Multi Silva et al.(2015) SNOMED CT, DBPedia Auto Alignment En-Por Wołk and Marasek(2015) EMEA NMT Encoder-Decoder Pol-En Arcan et al.(2016) Organic.Lingua SMT Domain En-(Ge, It, Sp) Arcan and Buitelaar(2017) ICD, Wiki Both Knowledge Base En-Ge Renato et al.(2018) DeCS, Dicionario Medico, Wiki SMT Domain Sp-Por Khan et al.(2018) UFAL, PatTR NMT Domain En-Fr Table 1: Summary of recent techniques for medical terms and texts translation. Claveau and Zweigenbaum(2005) presented a approach. NMT uses a large artificial neural method to automatically translate a large class of network which takes as an input a source sen- terms in the biomedical domain from one language tence (x1; : : : ; xm) and generates its translation to another; it is evaluated on translations between (y1; : : : ; yn), where x and y are source and target French and English. Their technique relies on a words respectively. Till recently, the dominant ap- supervised machine-learning algorithm, called OS- proach to NMT encodes the input sequence and TIA (Oncina, 1991), that infers transducers from subsequently generates a variable length translated examples of bilingual term-pairs. Such transducers, sequence using recurrent neural networks (RNN) when given a new term in English (respectively (Bahdanau et al., 2014; Sutskever et al., 2014). French), must propose the corresponding French NMT differs entirely from phrase-based statistical (resp. English) term. approaches that use separately engineered subcom- Later, Nystrom¨ et al.(2006) reports on a parallel ponents (Wołk and Marasek, 2015). collection of rubrics from the medical terminology Domain adaptation In machine translation, do- systems ICD-10, ICF, MeSH, NCSP and KSH97- main adaptation can be applied when a large P and its use for semi-automatic creation of an amount of out-of-domain data co-exists with a English-Swedish dictionary of medical terminol- small amount of in-domain data. ogy. The methods presented are relevant for many Arcan and Buitelaar(2017) presented a perfor- other West European language pairs. mance comparison between SMT and NMT meth- Deleger´ et al.(2009) presented a methodology ods on translating highly domain-specific expres- aiming to ease this process by automatically ac- sions, i.e. terminologies, documented in the ICD quiring new translations of medical terms based on ontology from the medical domain. They showed word alignment in parallel text corpora, and test it that domain adaptation with only terminological on English and French. After collecting a parallel, expressions significantly improves the translation English-French corpus, French translations of En- quality, which is specifically evident if an existing glish terms were detected from three terminologies- generic neural network is retrained with a limited MeSH, Snomed CT and the MedlinePlus Health vocabulary of the targeted domain. Last, they ob- Topics. A sample of the MeSH translations was served the benefit of subword models over word- submitted to expert review and a relatively high per- based NMT models for terminology translation. centage of 61.5% were deemed desirable additions All previous work focus on training with specific to the French MeSH. In conclusion, they success- terminologies. Although these methods are widely fully obtained good quality new translations, which used, their vocabulary may be limited. Moreover, underlines the suitability of using alignment in text their size is not sufficient for training NMT meth- corpora to help translating terminologies. Their ods, resulting in low translation performance. method may be applied to different European lan- To address these problems, Khan et al.(2018) guages and provides a methodological framework trained NMT systems by applying transfer learn- that may be used with different processing tools. ing. Transfer learning falls under the umbrella of Neural machine translation (NMT) In recent domain adaptation. In transfer learning the knowl- years, NMT has emerged as the state-of-the-art edge learned from a pre-trained existing model is 60 Terminology Size avg len(en) Incl avg len(en) descriptions on metabolism, blood, dermatological ICD-10 32474 5.49 7655 3.78 CHU Rouen HeTOP 202402 3.63 3892 3.69 and other contents. ORDO 50425 6.2 3716 5.56 ACAD 47603 2.45 2394 1.84 CLADIMED (CLADIMED, 2019) is a five lev- MedDRA 23954 2.72 1739 2.33 ATC 5536 2.06 1588 1.11 els classification for medical devices, based on the MESH 29351 1.99 1460 1.69 ATC classification approach (same families). De- ICD-O 3671 3.24 1122 2.88 vices are classified according to their main use and DBPEDIA 912 1.78 381 1.85 ICPC 3046 7.09 235 2.26 validated indications. It was originally developed ICF 3112 10.67 41 3.24 by AP-HP (hospitals of Paris). CLADIMED 4169 3.72 8 1.75 LOINC 2.66 91388 8.14 5 1.2 Total 499885 4.62 24242 3.35 ACAD (Academie´ de Medecine´ , 2019). The ”dictionnaire medical´ de l’academie´ de medecine”´ Table 2: Reference terminologies and statistics regard- identifies terms used in health and defines them ing the validated sample of ICD-11. Number of sen- under the supervision of the French National tences, average length in number of words (english cor- Academy of Medicine. pus), number of included sentences in the validated sample of ICD-11, and their corresponding average ICD-O (World Health Organization, 2019). The length (number of words). International Classification of Diseases for Oncol- ogy (ICD-O) (1) has been used for nearly 35 years, principally in tumor or cancer registries, for cod- transferred to a new model. Specifically, the au- ing the site (topography) and the histology (mor- thors used an existing out-of-domain model trained phology) of the neoplasm, usually obtained from a on News data. Afterwards, they train their NMT pathology report. system on the in-domain Biomedical’18 corpus3. Table1 summarizes the related work on medi- MESH (Medical Subject Headings) (FR MESH, cal terms and texts translation, showing resources, 2019) is a reference thesaurus in the biomedi- family of machine translation approach, specific cal field.
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