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HYGIA

HYGIA. Design and Application of new Artificial Intelligence techniques to the acquisition and use of medical knowledge represented as care pathways. Outline.

sean-francis
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HYGIA

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  1. HYGIA Design and Application of new Artificial Intelligence techniques to the acquisition and use of medical knowledge represented as care pathways.

  2. Outline “The use of Intelligent Systems in the processes of acquiring, formalizing, adapting, using and assessing knowledge models that describe Care Pathways”. • The HYGIA Project • HYGIA Groups and Members • General Objectives • Organization of the Project • Subproject 1: Santiago de Compostela University • Subproject 2: Jaume I University • Subproject 3: Rovira i Virgili University • Subproject 4: Clinical Hospital of Barcelona • Integration of Subprojects • Conclusions 2

  3. The HYGIA Project • Funded by Ministerio de Educación y Ciencia (Spain) • Plan Nacional de I+D+I (2004-2007) • Code: TIN2006-15453-C04 • Start date: October 1st 2006 • End data: September 30th 2009 • Webpage: www.etse.urv.es/~drianyo/TIN2006-15453/ 3

  4. Groups and Members 1. (David Riaño) 2. (María Taboada) 3. (Mar Marcos) 4. (Albert Alonso) 4

  5. Terminology • Clinical Practice Guideline (CPG): standard set of textual pathology-oriented recommendations for the clinical handling of patients with a certain pathology. • Electronic Clinical Practice Guideline: computerized version of a CPG, represented with a formal language. • Protocol: specific adaptation of a CPG that includes precise indications on the pathology-oriented health care actuations to follow under a concrete situation. • Electronic Protocol(EP): computerized version of a protocol, represented with a formal language. • Care Pathway (CP): operative version of a CPG that details the steps to follow for the care of a disease in a certain segment of patients (patient-oriented) and in a concrete healthcare context (health service oriented). 5

  6. General Objectives • Design and Implement a set of tools to automate, as far as possible, the acquisition of knowledge from textual CPGs. • Propose a methodological framework to develop electronic protocols from electronic CPGs. • Propose a methodological framework to elaborate CPs from electronic protocols and other additional resources, as hospital databases containing information about patient treatments. • Construct and use new inductive learning algorithms to generate health care knowledge from hospital databases with the use of ontologies that provide the semantic component of the medical domain of the guidelines. • Application of these knowledge structures (CPs) to support decision making and quality control by means of a multi-agent system that interprets the knowledge in the context it is used. • Identify and assess the adherence of health care professionals to comorbid CPs in a program of chronic patients. 6

  7. Project Organization • Subproject 1 (USC) • textual analysis • ontology management • Subproject 2 (UJI) • knowledge acquisition • knowledge engineering • Subproject 3 (URV) • knowledge induction from databases • tools for knowledge exploitation • Subproject 4 (HCB) • knowledge validation • knowledge exploitation 7

  8. CPG eCPG ONTO LOGIA Subproject 1: topics and technologies • (Semi)-automatic Acquisition of Knowledge from textual CPGs • Natural language processing to recognize words, to make syntactic analyses and to divide phrases into words (tokens). • Information extraction to extract pre-specified types of information from the text of the CPG. • All the processes supported by medical thesauri as the Medical Unified Language System (UMLS) . • Main phases • extract a domain ontology from textual documents • (semi)-automatically acquire the knowledge of CPGs • represent it in a language oriented to describe electronic CPGs. 8

  9. eCPG PROTO COL PROTO COL PROTO COL CP Subproject 2: topics and technologies • Propose a methodological frame for the transformation of eCPGs in protocols and the combination of protocols in CPs. • Knowledge Engineering: knowledge representation and acquisition • Software Engineering: “eCPGs and protocols are similar to programs” • Formal verification of eCPG directives in the electronic protocols. • Main phases • determine sort of eCPG directives to verify in the e-protocols. • propose an experience-based methodology to obtain e-protocols • formalization of patient and institution models to construct CPs. • propose a methodology to combine and adapt e-protocols 9

  10. DB CP Subproject 3: topics and technologies • Develop algorithms to obtain health care procedural knowledge from hospital databases • Data and Knowledge modelling and representation • Inductive Learning • Design and implement a software tool to apply health care procedural knowledge • Multi-agent systems • Software Engineering for MAS • Decision support systems • Quality control to measure adhesion indicators • Main phases: • Propose a data and a knowledge model to represent health care procedures • Develop inductive learning algorithms to obtain procedural knowledge form procedural data • Design and implement a Multi-agent system to make this knowledge actionable • Adapt the Multi-agent System to incorporate the screening of adhesion indicators TREATMENT MODEL MAS 10

  11. Subproject 4: topics and technologies • Provide recognized experience in health care of multi-pathology patients to validate and supervise the intermediate and final CPGs, eCPGs, protocols, e-protocols, and CPs • CPGs and protocols on obstructive pulmonary disease, cardiac insufficiency and diabetes • Patient models and institution models • Adhesion indicators • Provide data of the historical database of the hospital • Data bases and information systems • Data pre-processing and data mining • Test intermediate and final software in a program of chronic patients 11

  12. The Integration of Subprojects 12

  13. Conclusions • The project aims at • Establishing a framework on the problem of managing procedural knowledge in medicine. • Fostering the interaction of 4 groups with past experience in alternative approaches to the problem. • Integrating alternative Artificial Intelligence technologies in the setting of this problem. • Converging in a national reference in the field. • Attracting the interest of and incorporating the collaboration of groups with similar interests. • Disseminating the achievements of the group. • Extending the collaboration to new projects. • Trespassing the threshold of laboratory applications and pave a road to the real application of the results. • Interested people, groups, or associations, please contact David Riaño at david.riano@urv.net 13

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