BackgroundThe recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results.ObjectiveThis study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts.MethodsWe presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information.ResultsThe estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points).ConclusionsThe proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.

The advent of microblogging services represented by Twitter evidently stirred a popular trend of personal update sharing from all over the world. Furthermore, the recent mobile device and wireless network technologies are greatly expanding the connectivity between people over the social networking sites. Regarding the shared buzzes over the sites as a crowd-sourced database reflecting a various kind of real-world events, we are able to conduct a variety of social analytics using the crowd power in much easier ways. In this paper, we propose a geo-social event detection method by finding out unusually crowded places based on the conception of social networking sites as a social event detector. In order to detect unusual statuses of a region, we previously construct geographical regularities deduced from geo-tagged microblogs. Especially, we utilize a large number of geo-tagged Twitter messages which are collected by means of our own tweets acquisition method in terms of geographic relevancy. By comparing to those regularities, we decide if there are any unusual events happening in monitoring geographical areas. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities which are computed from a large number of real geo-tagged tweet dataset around Japan.

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Objectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions. Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas. Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP. Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.