Healthcare Process Modelling by Rule Based Networks

1. Healthcare Process Modelling by Rule Based Networks Han Liu First Year PhD Student Alex Gegov, Jim Briggs, Mohammed Bader PhD Supervisors

2. Table of contents • Health status monitoring • Treatment recommendation

3. Health Status Monitoring If x1=1 and x2=1 then y=1 • A set of medical rules used to predict health status is generated by a rule generation algorithm learning historical data and then converted into network structure illustrated inFigure 1 • Each node in input layer represents a medical feature • Each node in middle layer represents a medical rule • The output node represents the classification of health status, e.g. in risk or health input conjunction output Figure 1

4. TreatmentRecommendation • To classify patients into a particular category based on similarity using K Nearest Neighbour. • To retrieve treatments that have been applied to previous patients classified into the same category as the currentpatient and find a list of candidate treatments by majority voting. • To classify these candidate treatments to one of rate scale of 1 to k and filter those treatments with negative classification. • To induce a list of association rules which have patient features on left hand side and medical features on right hand side and is represented by a network as illustrated inFigure 2. • To retrieve a list of most potential treatments that match the features represented by the right hand sides of association rules in order to recommend doctors a list of candidate choices.

5. If x1=1 and x2=1 then y1=1 Medical Rules Patient Features Medical Features Figure 2

6. Thank you