=Paper=
{{Paper
|id=None
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-765/paper2.pdf
|volume=Vol-765
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-765/paper2.pdf</pdf>
<pre>
                 Disease Monitoring
                         and
              Clinical Decision Support

                              Peter Lucas

                     Radboud University Nijmegen
                          The Netherlands
                            peterl@cs.ru.nl



Abstract. With the recent availability of mobile, cheap, and sometimes
wearable sensors there are now new opportunities to monitor the progress
of diseases in patients, for example in the home environment rather than
in the hospital. Dealing with the data-streams coming from sensors that
measure physiological parameters is associated with different scientific
challenges, most of them due to the inherent complexity of biomedical
data. An important challenge is that both learning from data-streams
and interpreting incoming data-streams cannot be done without taking
into account all the other patient data characterising a disease process.
In addition, the various data elements will typically have different tem-
poral granularities. For example, the body temperature of the patient is
measured every day, whereas oxygen saturation may be measured on a
continuous basis. Other patient data, such as signs and symptoms may
be recorded on an even coarser time scale. Furthermore, all the collected
data are in the end collected to assistant in making decisions about a
patient, where external influences of the measurements cannot always
be excluded. These, and other properties of biomedical data impose con-
straints on how collected data can be exploited. In the talk we will review
some of these requirements and illustrate these by various real-world ap-
plications.

</pre>