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| Content Provider | ACM Digital Library |
|---|---|
| Author | Syed, Ali Raza Akhlaghi, Arash Robinson, William N. Deng, Tianjie |
| Copyright Year | 2012 |
| Abstract | Users with cognitive impairments use assistive technology (AT) as part of a clinical treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, real-time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When the quality of some data-mined models varies significantly from nearby models—as defined by quality metrics—the user's behavior is then flagged as a significant behavioral change. The specific changes in user behavior are then characterized by differencing the data-mined decision tree models. This article describes how model quality monitoring and decision tree differencing can aid in recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other real-time data-intensive analysis problems. |
| Starting Page | 1 |
| Ending Page | 28 |
| Page Count | 28 |
| File Format | |
| ISSN | 2158656X |
| DOI | 10.1145/2151163.2151167 |
| Volume Number | 3 |
| Issue Number | 1 |
| Journal | ACM Transactions on Management Information Systems (TMIS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-04-10 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Patient monitoring Behavioral rehabilitation Clinical treatment plans Decision trees Real-time data mining Stream mining |
| Content Type | Text |
| Resource Type | Article |
| Subject | Management Information Systems Computer Science |
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