Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

The accessibility of translated Zulu health texts : an investigation of translation strategies

In disseminating information about health issues, government health departments and NGOs use, inter alia, written health texts. In a country like South Africa, these texts are generally written by medical experts and thereafter translated into the languages of the people. One of these languages is Zulu, which is spoken by the majority of South Africans. A large percentage of Zulu speakers are illiterate or semi-literate, especially in the rural areas. For this reason, Zulu translators have to use ‘simple’ language that these readers would understand when translating English texts into Zulu. Translators are expected to use strategies that can deal with non-lexicalized, problematic or other related terms that appear in health texts, as well as geographical and cultural constraints. This study focuses on the strategies used by Zulu translators in an attempt to make translated Zulu health texts accessible to the target readership. The investigation includes the use of self-administered questionnaires for respondents from two of South Africa’s nine provinces, where Zulu speakers are found (Gauteng and KwaZulu-Natal), to determine whether the health texts do reach the target readership. Focus groups, semi-structured interviews and other complementary techniques were used to collect data from the selected respondents. Furthermore, a parallel concordance called ParaConc was used to extract and analyse data from the corpus as compiled for the present study, in an attempt to investigate the strategies used to make the translated health texts easier to read. The study uncovers various strategies which are used when translating English health texts into Zulu. These strategies include the use of loan words, paraphrasing, cultural terms and so on. In future, the use of ParaConc can be broadened to investigate newly discovered translation strategies, with the aim of making health texts more accessible to the target readers. Furthermore, this software programme can also be used to study translation strategies as used in other types of texts, for example journalistic texts.Linguistics and Modern LanguagesD. Litt. et Phil. (Linguistics (Translation Studies)

Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health

Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of under-studied types of medical information, and demonstrate its applicability via a case study on physical mobility function. Mobility is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is coded in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in medical informatics, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This study has implications for the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research.Comment: Updated final version, published in Frontiers in Digital Health, https://doi.org/10.3389/fdgth.2021.620828. 34 pages (23 text + 11 references); 9 figures, 2 table

Bruk av naturlig språkprosessering i psykiatri: En systematisk kartleggingsoversikt

Bakgrunn: Bruk av kunstig intelligens (AI) har et stadig økende fokus, også i helsevesenet. En metode som virker lovende, er naturlig språkprosessering (NLP), som kan brukes til analysering av skriftlig tekst, for eksempel tekst i elektroniske pasientjournaler. Denne undersøkelsen har som formål å undersøke forskning som er gjort på bruk av naturlig språkprosessering for analysering av elektroniske journaler fra pasienter med alvorlige psykiske lidelser, som affektive lidelser og psykoselidelser. Den overordnete hensikten med dette, er å få et inntrykk av om noe av forskningen som er gjort har fokus på forbedring av pasientenes helsesituasjon. Materiale og metode: Det ble gjennomført en systematisk kartleggingsoversikt («scoping review»). Litteratursøket ble gjort i én database for medisinsk forskning, PubMed, med søketermene «psychiatry», «electronic medical records» og «natural language processing». Søket var ikke avgrenset i tid. For at en artikkel skulle bli inkludert i undersøkelsen måtte den være empirisk, ha utført analyser på journaldata i fritekst, ha brukt elektroniske journaler fra psykiatriske pasienter med psykoselidelser og/eller affektive lidelser og være skrevet på engelsk språk. Resultater: Litteratursøket resulterte i totalt 211 unike artikler, av disse oppfylte 37 artikler inklusjonskriteriene i kartleggingsoversikten, og ble undersøkt videre. De fleste av studiene var gjennomført i Storbritannia og USA. Størrelsen på studiepopulasjonen varierte mye, fra noen hundre til flere hundre tusen inkluderte pasienter i studiene. Det var lite av forskningen som var gjort på spesifikke dokumenttyper fra pasientjournal, som for eksempel epikriser eller innkomstjournaler. Hensikten for studiene varierte mye, men kunne deles inn i noen felles kategorier: 1) identifisering av informasjon fra journal, 2) kvantitative undersøkelser av populasjonen eller journalene, 3) seleksjon av pasienter til kohorter og 4) vurdering av risiko. Fortolkning: Det trengs mer grunnforskning før teknologi for naturlig språkprosessering til analyse av elektronisk journal vil bidra med forbedring av psykiatriske pasienters helsesituasjon

Lexicography of coronavirus-related neologisms

This volume brings together contributions by international experts reflecting on Covid19-related neologisms and their lexicographic processing and representation. The papers analyze new words, new meanings of existing words, and new multiword units, where they come from, how they are transmitted (or differ) across languages, and how their use and meaning are reflected in dictionaries of all sorts. Recent trends in as many as ten languages are considered, including general and specialized language, monolingual as well as bilingual and printed as well as online dictionaries

The Problems of Translating Medical Terms from English into Arabic

Abstract This study tackles the problems of translating medical terms from English into Arabic a. It uses an evaluative approach to investigate and discuss the problems and intricacies of translating medical terms from English into Arabic. The purpose of the study is to display the difficulties of translating medical terms and how they were tackled by postgraduate students who are competent in medical translation and professional Arabic translators who work in the medical field. The study adopts a qualitative-quantitative approach. It focuses on different types of medical terms, excluding pharmacy-related terms. In order to find out and identify the real difficulties behind translating medical terms and how they could be approached by experienced translators, the researcher utilized a questionnaire test that included a set of English medical terms to be translated into Arabic by students who were doing a PhD in translation. The same questionnaire was also given to a group of professional Arabic translators. As medical terms are the key components of medical texts, the questionnaire included forty-five diversified English medical terms taken from different medical reports, namely National Health Service (NHS) leaflets and flyers and World Health Organization (WHO) reports for 2007 and 2008. The official Arabic translations of these documents were used to assess the translations given by the subjects in comparison to and contrast with some medical dictionaries and reliable medical websites. The population of the study included 54 postgraduate students (doing PhDs in Arabic translation) in Libyan (the researcher’s origin country) and UK universities and 12 Arabic translators working in UK hospitals and clinics. The results from the data analysis showed that the translation of the medical terms posed real difficulties and challenges for the students and inexperienced professional translators although the experienced professional translators found them comparatively straightforward. Hence, the result highlights the problems of translating medical terms from English into Arabic and the importance of training to work in the medical field as a translator. Also, the study concluded that literal translation, the heavy use of transliteration, inconsistency, the students’ lack of sufficient experience and practice in medical translation, and lack of up-to date English-Arabic medical dictionaries are factors that have given rise to problems in medical translation. Also, the study showed that almost no professional translators use CAT tools or MT to help them translate the medical terms

Models of atypical development must also be models of normal development

Functional magnetic resonance imaging studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have illustrated the importance of the process of development in understanding brain mechanisms underlying cognition and including children ill the study of the etiology of developmental disorders