Healthcare process modelling by rule based networks
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Healthcare Process Modelling by Rule Based Networks. Han Liu First Year PhD Student Alex Gegov , Jim Briggs, Mohammed Bader PhD Supervisors. Table of contents. Health status monitoring Treatment recommendation. Health Status Monitoring. If x1=1 and x2=1 then y=1.

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Healthcare process modelling by rule based networks

Healthcare Process Modelling by Rule Based Networks

Han Liu

First Year PhD Student

Alex Gegov, Jim Briggs, Mohammed Bader

PhD Supervisors


Table of contents
Table of contents

  • Health status monitoring

  • Treatment recommendation


Health status monitoring
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


Treatment recommendation
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.


If x1 1 and x2 1 then y1 1
If x1=1 and x2=1 then y1=1

Medical Rules

Patient Features

Medical Features

Figure 2



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