I have setup OpenMRS on my system and have started familiarizing with it's architecture and functionalities. I have also done a Sencha touch FirstApp for initial exposure.
Since i have been interested for the project "Intelligent Scheduler ", i have been spending time reading up papers and studying other approaches online.
What i figured was that since we are focused on making an an application which effectively schedules appointments for doctors, we could take two approaches:
1. This can be implemented using a future ailment prediction model
for the patients (of the doctor). Here, using extensive data of medical
records, for a patient’s sequence of symptoms we can try predict the rest of
the sequence (and ailment), from other patient’s data. A bayesian probabilistic model can be used to implement this.
2. Another approach can be to study the patient's appointment pattern and his current diagnosis, and hence predicting his next visit. A logistic regression approach can be taken here.
The papers I found, and the research that has been done is a
lot on predicting ailments, rather than detecting pattern for visits. There is
some correlation but a different approach is required, we might not want to be focusing on the giant "ailment prediction" problem, when we're trying to make a scheduler app.
BAYESIAN HIERARCHICAL RULE MODELING FOR PREDICTING MEDICAL
Predicting A Patient's Future Medical Visits: A Comparison
Of Quality Of Life And Clinical Variables http://www.valueinhealthjournal.com/article/S1098-3015%2813%2901381-8/abstract
Can seniors please guide on the approach i am taking!