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 language | English |
|---|---|
| Title of host publication | Big Data Becomes Personal: Knowledge into Meaning - Papers from the AAAI Spring Symposium, Technical Report |
| Publisher | AI Access Foundation |
| Pages | 98-99 |
| Number of pages | 2 |
| Volume | SS-14-01 |
| ISBN (Print) | 9781577356547 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 2014 AAAI Spring Symposium - Palo Alto, CA, United States Duration: Mar 24 2014 → Mar 26 2014 |
Other
| Other | 2014 AAAI Spring Symposium |
|---|---|
| Country | United States |
| City | Palo Alto, CA |
| Period | 3/24/14 → 3/26/14 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
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 (Vol. SS-14-01, pp. 98-99). AI Access Foundation.