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Abstract Background In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize terminology through the Adverse Event Terminology Collection of the Japan Federation of Medical Device Associations (JFMDA). Internationally, the Adverse Event Terminology of the International Medical Device Regulators Forum (IMDRF-AET) provides a standardized terminology collection in English. Mapping between the JFMDA terminology collection and the IMDRF-AET is critical to international harmonization. However, the process of translating the terminology collections from English to Japanese and reconciling them is done manually, resulting in high human workloads and potential inaccuracies. Objective The purpose of this study is to investigate the optimal machine translation model for the IMDRF-AET into Japanese for the part of a function for the automatic terminology mapping system. Methods English-Japanese parallel data for IMDRF-AET published by the Ministry of Health, Labor and Welfare in Japan was obtained from 50 sentences randomly extracted from the terms and their definitions. These English sentences were fed into the following machine translation models to produce Japanese translations: mBART50, m2m-100, Google Translation, Multilingual T5, GPT-3, ChatGPT, and GPT-4. The evaluations included the quantitative metrics of BiLingual Evaluation Understudy (BLEU), Character Error Rate (CER), Word Error Rate (WER), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Bidirectional Encoder Representations from Transformers (BERT) score, as well as qualitative evaluations by four experts. Results GPT-4 outperformed other models in both the quantitative and qualitative evaluations, with ChatGPT showing the same capability, but with lower quantitative scores, in the qualitative evaluation. Scores of other models, including mBART50 and m2m-100, lagged behind, particularly in the CER and BERT scores. Conclusion GPT-4’s superior performance in translating medical terminology, indicates its potential utility in improving the efficiency of the terminology mapping system.

Abstract This study focused on the heterogeneity in progress notes written by physicians or nurses. A total of 806 days of progress notes written by physicians or nurses from 83 randomly selected patients hospitalized in the Gastroenterology Department at Kagawa University Hospital from January to December 2021 were analyzed. We extracted symptoms as the International Classification of Diseases (ICD) Chapter 18 (R00–R99, hereinafter R codes) from each progress note using MedNER-J natural language processing software and counted the days one or more symptoms were extracted to calculate the extraction rate. The R-code extraction rate was significantly higher from progress notes by nurses than by physicians (physicians 68.5% vs. nurses 75.2%; p = 0.00112), regardless of specialty. By contrast, the R-code subcategory R10–R19 for digestive system symptoms (44.2 vs. 37.5%, respectively; p = 0.00299) and many chapters of ICD codes for disease names, as represented by Chapter 11 K00–K93 (68.4 vs. 30.9%, respectively; p

Objectives: We provided COVID-19 outbreak trends in South Africa during the Omicron (B.1.1.529), Delta (B.1.617.2), and Beta (B.1.351) variants outbreak periods from November 2020 to March 2022. Methods: We used the time series summary data of the COVID-19 outbreak for South Africa available in the COVID-19 data repository created by the Center for System and Science and Engineering at Johns Hopkins University and the Our World in Data database by the University of Oxford from January 2020 to March 2022. We used the joinpoint regression model with a data-driven Bayesian information criterion method for analyzing the outbreak trends. In addition, we used density ellipses and partition modeling on the outbreak data. Results: During the Omicron outbreak period, COVID-19 cases in South Africa significantly jumped by 4.7 times from December 01 to December 08, 2021. The average daily growth rate of incidence peaked at 23,000 cases/day until December 16, 2021, which was 18.6 % higher than the peak growth during the Delta outbreak period. South Africa experienced peak growth in COVID-19 cases with 18,611 cases/day (January 04 to January 14, 2021) during the Beta outbreak period and with 19,395 cases/day (July 01 to July 11, 2021) during the Delta outbreak period. Density ellipsoid showed a significant correlation between daily cases and daily death count during the Beta and Delta outbreak period which was not prominent in the Omicron outbreak period. Comparatively higher daily death tolls were reported in days with a recovery rate of less than 89.1 % and 91.9 % in the Beta and Delta outbreak period respectively. The backlog counts may be one of the reasons for the significant increase in daily death tolls during the Omicron period. Conclusions: During the Omicron period, COVID-19 cases peaked growth was 18.6 % higher than the peak growth during the Delta outbreak period. Despite that fact, growth in death trends in the Omicron outbreak period was found low which might be due to the low mortality rate and case fatality proportion. The emergence of the Omicron variant once again reminds us that- “no one is safe until everyone is safe”.

Abstract Background The prevalence of extracorporeal cardiopulmonary resuscitation (ECPR) in patients with out-of-hospital cardiac arrest (OHCA) has been increasing rapidly worldwide. However, guidelines or clinical studies do not provide sufficient data on ECPR practice. The aim of this study was to provide real-world data on ECPR for patients with OHCA, including details of complications. Methods We did a retrospective database analysis of observational multicenter cohort study in Japan. Adult patients with OHCA of presumed cardiac etiology who received ECPR between 2013 and 2018 were included. The primary outcome was favorable neurological outcome at hospital discharge, defined as a cerebral performance category of 1 or 2. Results A total of 1644 patients with OHCA were included in this study. The patient age was 18–93 years (median: 60 years). Shockable rhythm in the initial cardiac rhythm at the scene was 69.4%. The median estimated low flow time was 55 min (interquartile range: 45–66 min). Favorable neurological outcome at hospital discharge was observed in 14.1% of patients, and the rate of survival to hospital discharge was 27.2%. The proportions of favorable neurological outcome at hospital discharge in terms of shockable rhythm, pulseless electrical activity, and asystole were 16.7%, 9.2%, and 3.9%, respectively. Complications were observed during ECPR in 32.7% of patients, and the most common complication was bleeding, with the rates of cannulation site bleeding and other types of hemorrhage at 16.4% and 8.5%, respectively. Conclusions In this large cohort, data on the ECPR of 1644 patients with OHCA show that the proportion of favorable neurological outcomes at hospital discharge was 14.1%, survival rate at hospital discharge was 27.2%, and complications were observed during ECPR in 32.7%.

Abstract Background For standardization of terms in the reports of medical device adverse events, 89 Japanese medical device adverse event terminologies were published in March 2015. The 89 terminologies were developed independently by 13 industry associations, suggesting that there may be inconsistencies among the terms proposed. The purpose of this study was to integrate the 89 sets of terminologies and evaluate inconsistencies among them using SPARQL. Methods In order to evaluate the inconsistencies among the integrated terminology, the following six items were evaluated: (1) whether the two-layer structure between category term and preferred term is consistent, (2) whether synonyms of a preferred term are involved. Reversing the layer-category order of matching was also performed, (3) whether each preferred term is subordinate to only one category term, (4) whether the definitions of terms are uniquely determined, (5) whether CDRH-NCIt terms corresponding to preferred terms are uniquely determined, (6) whether a term in a medical device problem is used for patient problems. Results About 60% of the total number of duplicated terms were found. This is because industry associations that created multiple terminologies adopted the same terms in terminologies of similar medical device groups. In the case that all terms with the same spelling have the same concept, efficient integration can be achieved automatically using RDF. Furthermore, we evaluated six matters of inconsistency in this study, terms that need to be reviewed accounted for about 10% or less than 10% in each item. Conclusions The RDF and SPARQL were useful tools to explore inconsistencies of hierarchies, definition statements, and synonyms when integrating terminolgy by term notation, and these had the advantage of reducing the physical and time burden.

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Aim: This study aimed to investigate the relationship between transient return of spontaneous circulation (ROSC) before extracorporeal membrane oxygenation (ECMO) initiation and outcomes in out-of-hospital cardiac arrest (OHCA) patients, who were resuscitated with extracorporeal cardiopulmonary resuscitation (ECPR). Methods: This study was a secondary analysis of the SAVE-J II study, which was a retrospective multicentre registry study involving 36 participating institutions in Japan. We classified patients into two groups according to the presence or absence of transient ROSC before ECMO initiation. Transient ROSC was defined as any palpable pulse of ≥1 min before ECMO initiation. The primary outcome was favourable neurological outcomes (cerebral performance categories 1–2). Results: Of 2,157 patients registered in the SAVE-J II study, 1,501 met the study inclusion criteria; 328 (22%) experienced transient ROSC before ECMO initiation. Patients with transient ROSC had better outcomes than those without ROSC (favourable neurological outcome, 26% vs 12%, P

Objective: In recent years, natural language processing (NLP) techniques have progressed, and their application in the medical field has been tested. However, the use of NLP to detect symptoms from medical progress notes written in Japanese, remains limited. We aimed to detect 2 gastrointestinal symptoms that interfere with the continuation of chemotherapy—nausea/vomiting and diarrhea—from progress notes using NLP, and then to analyze factors affecting NLP. Materials and methods: In this study, 200 patients were randomly selected from 5277 patients who received intravenous injections of cytotoxic anticancer drugs at Kagawa University Hospital, Japan, between January 2011 and December 2018. We aimed to detect the first occurrence of nausea/vomiting (Group A) and diarrhea (Group B) using NLP. The NLP performance was evaluated by the concordance with a review of the physicians’ progress notes used as the gold standard. Results: Both groups showed high concordance: 83.5% (95% confidence interval [CI] 74.1-90.1) in Group A and 97.7% (95% CI 91.3-99.9) in Group B. However, the concordance was significantly better in Group B ( P = .0027). There were significantly more misdetection cases in Group A than in Group B (15.3% in Group A; 1.2% in Group B, P = .0012) due to negative findings or past history. Conclusion: We detected occurrences of nausea/vomiting and diarrhea accurately using NLP. However, there were more misdetection cases in Group A due to negative findings or past history, which may have been influenced by the physicians’ more frequent documentation of nausea/vomiting.

Japanese medical device adverse events terminology, published by the Japan Federation of Medical Devices Associations (JFMDA terminology), contains entries for 89 terminology items, with each of the terminology entries created independently. It is necessary to establish and verify the consistency of these terminology entries and map them efficiently and accurately. Therefore, developing an automatic synonym detection tool is an important concern. Such tools for edit distances and distributed representations have achieved good performance in previous studies. The purpose of this study was to identify synonyms in JFMDA terminology and evaluate the accuracy using these algorithms. A total of 125 definition sentence pairs were created from the terminology as baselines. Edit distances (Levenshtein and Jaro–Winkler distance) and distributed representations (Word2vec, fastText, and Doc2vec) were employed for calculating similarities. Receiver operating characteristic analysis was carried out to evaluate the accuracy of synonym detection. A comparison of the accuracies of the algorithms showed that the Jaro–Winkler distance had the highest sensitivity, Doc2vec with DM had the highest specificity, and the Levenshtein distance had the highest value in area under the curve. Edit distances and Doc2vec makes it possible to obtain high accuracy in predicting synonyms in JFMDA terminology.

The purpose of this study was to evaluate the accuracy of deep neural machine translation focused on medical device adverse event terminology. 10 models were obtained, and their English-to-Japanese translation accuracy was evaluated using quantitative and qualitative measures. No significant difference was found in the quantitative index except for a few pairs. In the qualitative evaluation, there was a significant difference and googletrans and GPT-3 were regarded as useful models. [ABSTRACT FROM AUTHOR]

The purpose of our study is to identify the patterns of vectors in similar/dissimilar pairs of definition sentence created by Word2vec and doec2vec for elaboration of the terminology for Japanese Medical Device Adverse Events. 2-dimension vector space created by t-SNE showed that the pair with true positive located closer in a vector space, especially Doc2vec had a strong tendency. Comparing with Word2vec, Similar vectors in Doc2vec were close and tended to form clusters. [ABSTRACT FROM AUTHOR]

The purpose of this study is to extract similar term definitions used in the terminology of Japanese medical device adverse events. We employed Levenshtein and Jaro-Winkler distances as edit distances and Skip-gram, continuous-bag of words, and fast text to produce distributed representations in Word2Vec. A comparison of the accuracies of the models showed that Levenshtein distance had higher specificity whereas Skip-gram had higher sensitivity as compared to the other models. [ABSTRACT FROM AUTHOR]

We started a multi-year project to collect discharge summaries from multiple hospitals and create a big text database to build a common document vector space, and develop various applications such as the autoselection of the disease. As the first step, we extracted discharge summary from two hospitals. Using a text mining method, we carried out a DPC selection. There was a difference in term structure and number of terms between the discharge summaries from both hospitals. Nevertheless, the selection rate of the disease is resembled closely. [ABSTRACT FROM AUTHOR]

We performed the multi-year project to collect discharge summary from multiple hospitals and made the big text database to build a common document vector space, and developed various applications. We extracted 243,907 discharge summaries from seven hospitals. There was a difference in term structure and number of terms between the hospitals, however the differences by disease were similar. We built the vector space using TF-IDF method. We performed a cross-match analysis of DPC selection among seven hospitals. About 80% cases were correctly matched. The use of model data of other hospitals reduced selection rate to around 10%; however, integrated model data from all hospitals restored the selection rate.