Medical Knowledge Representation System

Transcription

1 25-28 May st International Congress MIE 2008 in Göteborg, Sweden 1 Medical Knowledge Representation System David BUCHTELA, Jan PELESKA, Miroslav ZVOLSKY, Jana ZVAROVA Center and Center for Biomedical Informatics, Department of Medical Informatics, Institute of Computer Science AS CR v.v.i., Pod Vodarenskou vezi 2, Prague 8, Czech Republic

2 2 The aim of this article is to present a design of a Medical Knowledge Representation System The system automatically offers relevant formalized knowledge by extended (Guidelines( Interchange Format) ) models to participants (patient,physician,operator( patient,physician,operator, ) on the basis of acquired data This selection algorithm is based on key attributes and cooperation with knowledge ontologies or other registry of recognized attributes

3 3 specifies a process-oriented oriented model for a medical guidelines representation We use specification 3.5 with some changes and extension The is given as a flowchart representing an a ordered sequence of steps patient state step action (subgraph)) step branch step decision step synchronization step

4 Action steps specify clinical actions that are to be performed some therapy, carrying out some examination or measurement Action step also may mean sub-graph which provides a detail for the action 4

5 5 Decision steps are used for conditional branching The decision can be determined by evaluation of defined logical criteria based on data items When the decision cannot be precisely specified in guidelines the decision should be made by the user

6 Branch and synchronization steps Steps that follow the branch step can be performed concurrently The branches converge in a synchronization step after evaluation of synchronizing condition needed or possible parts of graph 6

7 7 State step characterizes a patient s clinical state after an application of some therapy (action step) or in the begin of the model diagnosis of hypertension, high level of blood pressure ure,

8 - criteria of conditions 8 α strict-in α strict-out α rule-in α rule-out β strict-in β strict-out β rule-in β rule-out The decision step specifies several criteria of condition for each decision option: The strict criteria are used to specify a decision condition that could be computed automatically If a strict-in in is true then the control flows to the guideline step that is specified by that option s s destination If a strict-out is true then the option s destination is forbidden = absolute contraindications

9 - criteria of conditions 9 α strict-in α strict-out α rule-in α rule-out β strict-in β strict-out β rule-in β rule-out The decision step specifies several criteria of condition for each decision option: The rule criteria are used to divide a choice into recommended and not recommended options: If a rule-in is true the option is recommended = indication If a rule-out is true the option is not recommended but not forbidden = (relative) contraindication

10 Knowledge Representation Model 10 Knowledge Representation Model of the whole system is based on a knowledge formalisation trough the use of the The formalisation process contains: a construction of the graphic of knowledge from a free text of guidelines a coding of the model into the formal language (XML) Knowledge (free text) XML + Parameters Construction Coding specialist informatic user specialist informatic user

11 Extension of 11 The resulting is extended by key attributes These key attributes are used in selection algorithm and they are coded in XML along with the - XML + key attributes extended - XML

12 Key attributes 12 hypertension physician blood pressure 0,9 diabetes 0,2

13 13 For every specific participant (user, patient, physician, operator, ) p and his attributes A p a set of all branches and keys which corresponds to participant state (attributes A p) is determined branch & keys (o(p)) SBP, DBP glycaemia (s) (G p ) data (A p ) KRES (select algorithm) data (A p ) User Register Ontology or ontology (G p,r g ) branch & keys (o(p)) diabetes hypert blood pr. diabetes

14 14 For each branch the models G p are chosen from a finite set of all s M where each contains attribute branch a finite set P of keys (attribute key_name) ) and their weights (attribute key_weight) branch & keys (o(p)) G 2 : Diabes G 1 : Hypertension (s) (G p ) data (A p ) KRES (select algorithm) data (A p ) User Register Ontology or ontology (G p,r g ) branch & keys (o(p)) G p { g M : g K( p, B) key _ weight( branch( g), P( )) 0} = g

15 15 For each model G p a general aggregate operator R g is defined for example R g = k P( g ) key _ weight( k) Register Ontology (s) or ontology branch & keys data branch & keys (o(p)) (G p ) (A p ) (o(p)) KRES (select algorithm) G 2 : R g2 = 0,8 G 1 : R g1 = 2,1 data (A p ) (G p,r g ) User

16 16 The with the highest relevance value is offered to participant R = max( Rg ) (s) branch & keys (o(p)) (G p ) Register Ontology or ontology data branch & keys (A p ) (o(p)) or a list of models ordered by the value of relevance R g is offered G 1 : R g1 = 2,1 KRES (select algorithm) data (A p ) (G p,r g ) User

17 17 Designed medical knowledge representation system is based on the which is the universal method of modelling of mainly process ss-oriented knowledge The designed system offers a relevant or an ordered list of models from a list of available formal models on the basis of participant attributes Nowadays the system is developed and tested on a several s of Czech and European cardiology guidelines

18 May st International Congress MIE 2008 in Göteborg, Sweden Thank you for your attention! : Ing. Center for Biomedical Informatics Prague, Czech Republic buchtela@euromise.cz www: