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Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

According to the previous study by Aramaki et al [15], most people report influenza information precisely in the early stage of an influenza season.

Shoko Wakamiya, Yukiko Kawai, Eiji Aramaki

JMIR Public Health Surveill 2018;4(3):e65

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Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis

Shoko Wakamiya, Shoji Matsune, Kimihiro Okubo, Eiji Aramaki

J Med Internet Res 2019;21(2):e10450

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Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis

Shogo Ujiie, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki

JMIR Med Inform 2020;8(11):e22661

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Blog Posting After Lung Cancer Notification: Content Analysis of Blogs Written by Patients or Their Families

Akira Sato, Eiji Aramaki, Yumiko Shimamoto, Shiro Tanaka, Koji Kawakami

JMIR Cancer 2015;1(1):e5

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Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese

Denecke et al [29] collected data from multiple media sites with keyword lists and classified texts as relevant/irrelevant using support vector machines.Although no previous studies have been completed in Japanese, Aramaki et al [30] reported on a system to

Misa Usui, Eiji Aramaki, Tomohide Iwao, Shoko Wakamiya, Tohru Sakamoto, Mayumi Mochizuki

JMIR Med Inform 2018;6(3):e11021

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Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services

Eiji Aramaki, Shuko Shikata, Satsuki Ayaya, Shin-Ichiro Kumagaya

JMIR Res Protoc 2017;6(5):e83

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Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations

Shoko Wakamiya, Mizuki Morita, Yoshinobu Kano, Tomoko Ohkuma, Eiji Aramaki

J Med Internet Res 2019;21(2):e12783

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Comparing Medical Term Usage Patterns of Professionals and Search Engine and Community Question Answering Service Users in Japan: Log Analysis

Kazuya Taira, Taichi Murayama, Sumio Fujita, Mikiko Ito, Kei Kamide, Eiji Aramaki

J Med Internet Res 2020;22(4):e13369

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Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Aramaki et al discovered that a Twitter-based model outperformed a Google-based model during periods of normal news coverage, although the Twitter model performed less optimally during the periods of excessive media coverage [23].

J Danielle Sharpe, Richard S Hopkins, Robert L Cook, Catherine W Striley

JMIR Public Health Surveill 2016;2(2):e161

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Long-Term Effectiveness and Cost-Effectiveness of Videoconference-Delivered Cognitive Behavioral Therapy for Obsessive-Compulsive Disorder, Panic Disorder, and Social Anxiety Disorder in Japan: One-Year Follow-Up of a Single-Arm Trial

Kazuki Matsumoto, Sayo Hamatani, Kazue Nagai, Chihiro Sutoh, Akiko Nakagawa, Eiji Shimizu

JMIR Ment Health 2020;7(4):e17157

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