ABSTRACT
Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.4.2 Sensor Data Mining in Operating Rooms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.4.3 General Mining of Clinical Sensor Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.5 Nonclinical Healthcare Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.5.1 Chronic Disease and Wellness Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.5.2 Activity Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 4.5.3 Reality Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.6 Summary and Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
With progress in sensor technologies, the instrumentation of the world is offering unique opportu-
nities to obtain fine grain data on patients and their environment. Turning this data into information
is having a profound impact in healthcare. It not only facilitates design of sophisticated clinical de-
cision support systems capable of better observing patients’ physiological signals and helps provide
situational awareness to the bedside, but also promotes insight on the inefficiencies in the healthcare
system that may be the root cause of surging costs. To turn this data into information, it is essential
to be able to analyze patient data and turn it into actionable information using data mining. This
chapter surveys existing applications of sensor data mining technologies in healthcare. It starts with
a description of healthcare data mining challenges before presenting an overview of applications of
data mining in both clinical and nonclinical settings.
