Constructing Japanese MeSH term dictionaries related to the COVID-19 literature

The coronavirus disease 2019 (COVID-19) pandemic has led to a flood of research papers and the information has been updated with considerable frequency. For society to derive benefits from this research, it is necessary to promote sharing up-to-date knowledge from these papers. However, because most research papers are written in English, it is difficult for people who are not familiar with English medical terms to obtain knowledge from them. To facilitate sharing knowledge from COVID-19 papers written in English for Japanese speakers, we tried to construct a dictionary with an open license by assigning Japanese terms to MeSH unique identifiers (UIDs) annotated to words in the texts of COVID-19 papers. Using this dictionary, 98.99% of all occurrences of MeSH terms in COVID-19 papers were covered. We also created a curated version of the dictionary and uploaded it to PubDictionary for wider use in the PubAnnotation system.

O-JMeSH: creating a bilingual English-Japanese controlled vocabulary of MeSH UIDs through machine translation and mutual information

Previous approaches to create a controlled vocabulary for Japanese have resorted to existing bilingual dictionary and transformation rules to allow such mappings. However, given the possible new terms introduced due to coronavirus disease 2019 (COVID-19) and the emphasis on respiratory and infection-related terms, coverage might not be guaranteed. We propose creating a Japanese bilingual controlled vocabulary based on MeSH terms assigned to COVID-19 related publications in this work. For such, we resorted to manual curation of several bilingual dictionaries and a computational approach based on machine translation of sentences containing such terms and the ranking of possible translations for the individual terms by mutual information. Our results show that we achieved nearly 99% occurrence coverage in LitCovid, while our computational approach presented average accuracy of 63.33% for all terms, and 84.51% for drugs and chemicals.

A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

effect of aromatherapy treatment on fatigue and relaxation for mothers during the early puerperal period in Japan: a pilot study

Background: Early in the postpartum period, mothers are often nervous and tired from the delivery, breast-feeding and caring for a new-born. The aim of this study was to evaluate the process and outcome of using aromatherapy treatments to increase relaxation and decrease fatigue for mothers during the first to the seventh day of the postpartum period

Methods: This non-randomized controlled study with a quasi-experimental one-group pretest post-test design was used to evaluate scores in relaxation and fatigue before and after the intervention. Aromatherapy hand treatments were performed on a purposive sample of 34 postpartum mothers in Tokyo, Japan, from May to July 2016. The single treatment included a choice of one of five essential aroma oils through hand and forearm massage. Relaxation and fatigue were measured by self-administered valid and reliable questionnaires. Wilcoxon signed-rank test was conducted to analyze the data before and after the intervention. The software programs SPSS, v. 23.0 [SPSS, Tokyo], was used to analyze the data, with the significance level set at 5%

Results: Valid responses were obtained from 29 participants. A comparison of the scores before and after aroma treatment intervention indicated that the participants' relaxation scores increased significantly [P<0.001] and fatigue scores were significantly reduced [P<0.001]. The majority of participants [77.8%] were satisfied with the treatment

Conclusion: The aroma treatments significantly improved relaxation and reduced fatigue for mothers in the early puerperal period and were well received. Therefore, a larger study using a pretest-posttest random control trial is recommended