E. Aramaki

Publications172
h-index20
Citations2,016
Highly Influential Citations111
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Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
TLDR
It is described that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.
DialBetics: A Novel Smartphone-based Self-management Support System for Type 2 Diabetes Patients.
TLDR
DialBetics was shown to be a feasible and an effective tool for improving HbA1c by providing patients with real-time support based on their measurements and inputs and BMI improvement-although not statistically significant because of the small sample size-was greater in the DialBetics group.
Overview of the NTCIR-10 MedNLP Task
TLDR
An NTCIR-10 pilot task for medical records comprises three tasks: (1) de-identification, (2) complaint and diagnosis, and (3) free, which represent elemental technologies used to develop computational systems supporting widely diverse medical services.
TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification
TLDR
Experimental results demonstrate empirically that syntactic information can contribute to the method's accuracy and an SVM-based classifier using syntactic Information is proposed.
Use trend analysis of twitter after the great east japan earthquake
TLDR
A case study of how people used Twitter after the Great East Japan Earthquake, which gathered tweets immediately after the earthquake and analyzed various factors, including locations, revealed two findings: (1) people in the disaster area tend to directly communicate with each other (reply-based tweet) and ( other area prefer spread the information from the disaster areas by using Re-tweet.
Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study
TLDR
The 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.
Overview of the NTCIR-11 MedNLP-2 Task
TLDR
Results are presented of groups with discussions that are to clarify the issues to be resolved in medical natural language processing fields to evaluate technologies to retrieve important information from medical reports written in Japanese.
Finding Structural Correspondences from Bilingual Parsed Corpus for Corpus-based Translation
TLDR
A system and methods for finding structural correspondences from the paired dependency structures of a source sentence and its translation in a target language and a GUI system with which a user can check and correct the correspondences retrieved by the system.
Overview of the NTCIR-12 MedNLPDoc Task
TLDR
The NTCIR12 MedNLPDoc task is described which is designed for more advanced and practical use for the medical fields and is considered as a multi-labeling task to a patient record.
Extraction of Adverse Drug Effects from Clinical Records
TLDR
Assessment of how much adverse-effect information is contained in records, and automatic extracting accuracy of the current standard Natural Language Processing (NLP) system revealed that 7.7% of records include adverse event information, and that 59% of them can be extracted automatically.
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