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Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study.

Sofian BerrouiguetDavid RamírezMaría Luisa BarrigónPablo Moreno-MuñozRodrigo CarmonaEnrique Baca-GarciaAntonio Artés-Rodriguez
Published in: JMIR mHealth and uHealth (2018)
The proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.
Keyphrases
  • electronic health record
  • machine learning
  • artificial intelligence
  • deep learning
  • low cost