Arresting treatment patterns for individual patients in clinical big data: An exploratory procedure

Mizuki Morita, Masanori Shiro, Shotaro Akaho, Toshihiro Kamishima, Hideki Asoh, Eiji Aramaki, Koiti Hasida, Takahide Kohro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data mining of clinical data that are stored continually in the course of daily medical practice will contribute to the advancement of healthcare. However, real-world clinical data are characteristically noisy, sparse, irregular, and biased, which makes it difficult to perform data mining. This study assesses an exploratory approach to ascertain how physicians tackle the worsening of a patient's condition using clinical data from a hospital. It yielded reasonable results.

Original languageEnglish
Title of host publicationBig Data Becomes Personal
Subtitle of host publicationKnowledge into Meaning - Papers from the AAAI Spring Symposium, Technical Report
PublisherAI Access Foundation
Pages98-99
Number of pages2
ISBN (Print)9781577356547
Publication statusPublished - Jan 1 2014
Externally publishedYes
Event2014 AAAI Spring Symposium - Palo Alto, CA, United States
Duration: Mar 24 2014Mar 26 2014

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-14-01

Other

Other2014 AAAI Spring Symposium
CountryUnited States
CityPalo Alto, CA
Period3/24/143/26/14

ASJC Scopus subject areas

  • Artificial Intelligence

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    Morita, M., Shiro, M., Akaho, S., Kamishima, T., Asoh, H., Aramaki, E., Hasida, K., & Kohro, T. (2014). Arresting treatment patterns for individual patients in clinical big data: An exploratory procedure. In Big Data Becomes Personal: Knowledge into Meaning - Papers from the AAAI Spring Symposium, Technical Report (pp. 98-99). (AAAI Spring Symposium - Technical Report; Vol. SS-14-01). AI Access Foundation.