Collaborative Project of the 7th Framework Programme. WP6: Tools for bio-researchers and clinicians

Transcription

1 G.A. nº Collaborative Project of the 7th Framework Programme WP6: Tools for bio-researchers and clinicians Deliverable 6.1: Design of Inference Engine v /10/2011

2 Document Information Project Number Acronym Synergy-COPD Full title Project URL EU Project officer Modelling and simulation environment for systems medicine (Chronic obstructive pulmonary disease -COPD- as a use case) Marta Lorens Deliverable Number 6.1 Title Design of Inference Engine Work package Number 6 Title Tools for bio-researchers and clinicians Date of delivery Contractual PM09 Actual PM09 Nature Dissemination Level Prototype Report Dissemination Other Restricted : Restricted: VPH partners and participants in VPH-related FP7 projects Document responsible Filip Velickovski fvelickovski@bdigital.org Partner BDIGITAL Phone Authors Name Partner Main author(s) Filip Velickovski BDIGITAL Luigi Ceccaroni BDIGITAL Co-author(s) Peter Somogy KI Isaac Cano Josep Roca LINKCARE IDIBAPS D 6 1 Inference Engine Design.2011_10_31_21 page 2 of 73

3 Abstract (for dissemination) Key words A CDSS can be defined as "software that is designed to be a direct aid to clinical decision-making in which the characteristics of an individual patient are matched to a computerized clinical knowledge base, and patientspecific assessments or recommendations are then presented to the clinician and/or the patient for a decision". One of the primary objectives in the Synergy-COPD project is to develop a CDSS so that medical staff (primary care clinicians, specialists, and/or nurses) can obtain fast, reliable and directly applicable advice when treating a patient with COPD. The Synergy-COPD CDSS will use mechanistic models, probabilistic models, and existing knowledge from specialists and other evidence-based sources in order to perform early diagnosis of the disease, prediction of disease progression, and personalized treatment-advice for patients. The purposes of this deliverable are to review the main types of CDSS that exist in research and in medical practice with a focus on the reasoning or inference methods, to specify the core requirements, assumptions and functionality of the CDSS to be developed in Synergy-COPD and to outline the main components that will form part of the Synergy-COPD CDSS design, and the plan to execute the work. clinical decision support, COPD, rules-based systems, inference engine, computer interpretable guidelines Version Log Issue Date Version Author Change 01/08/ Filip Velickovski Document skeleton started 15/08/ Filip Velickovski Bullet points of sections 27/09/ Filip Velickovski First draft released internally BDIGITAL 17/10/ Filip Velickovski Second draft released to consortium for comments 23/10/ Peter Somogy, Isaac Cano, Josep Roca Peter S typo fixes actualized, Isaac C suggestions taken into account, Josep R clinical clarifications actualized 27/10/ Filip Velickovski, Luigi Further updating of sections Ceccaroni 30/10/ Luigi Ceccaroni Luigi major revisions 31/10/ Filip Velickovski, Luigi Ceccaroni, Alba Grau Ready for submission D 6 1 Inference Engine Design.2011_10_31_21 page 3 of 73

4 Index 1 INTRODUCTION A NOTE OF CLARIFICATION A REVIEW OF CDSS CDSS CHARACTERIZATION REASONING METHODS Workflow driven Rule-based reasoning Probabilistic Reasoning (Bayesian networks) Pattern recognition and machine learning (PR and ML) Case based reasoning STATE OF THE ART OF CDSS IN COPD Literature search Analysis CDSS INFRASTRUCTURAL FEATURES Knowledge acquisition and authoring (KA) tools Electronic health records (EHRs) Communication interface between medical systems Medical vocabularies REQUIREMENTS SPECIFICATION FOR THE SYNERGY-COPD CDSS INITIAL USE CASE SCENARIO MOTIVATION CLINICAL CONTEXT CLINICAL KNOWLEDGE INFORMATION DELIVERY DECISION SUPPORT REQUIREMENTS Case finding Diagnosis D 6 1 Inference Engine Design.2011_10_31_21 page 4 of 73

5 3.6.3 Assessment Management Referral Exacerbation control SYNERGY-COPD CDSS DESIGN WORK STRATEGY CDSS ARCHITECTURE Internal components External systems CONCLUSIONS REFERENCES A. DEFINITIONS OF SPECIAL TERMS A.1 TERMINOLOGY CONFUSION: COPD, EMPHYSEMA, CHRONIC BRONCHITIS A.2 TERMINOLOGY CONFUSION: EMR, EHR, AND PHR A.3 COMORBIDITIES AND SYSTEMIC EFFECTS B. LIST OF ACRONYMS AND ABBREVIATIONS D 6 1 Inference Engine Design.2011_10_31_21 page 5 of 73

6 List of figures FIGURE 1 A GENERAL MODEL OF A KNOWLEDGE-BASED CDSS FIGURE 2 A FLOWCHART SHOWING A DIFFERENTIAL DIAGNOSIS OF COPD AND ASTHMA FROM THE NEW ZEALAND BEST PRACTICE ADVOCACY CENTRE FIGURE 3 GENERIC MODEL OF A WORKFLOW-DRIVEN CDSS FIGURE 4 PROFORMA TASK ONTOLOGY FIGURE 5 GLIF REPRESENTATION OF THE STABLE ANGINA GUIDELINE [24] FIGURE 6 GUIDE WORKFLOW FOR STROKE DIAGNOSIS FIGURE 7 INFERENCE ENGINE COMPONENTS OF GUIDE FIGURE 8 ACTIVITY DIAGRAM OF AN IMMUNIZATION GUIDELINE FOR HEPATITIS B FIGURE 9 DECISION MAP OF A CHILD IMMUNIZATION GUIDELINE FIGURE 10 GENERIC MODEL OF A RULE-BASED REASONING CDSS FIGURE 11 FORWARD CHAINING RULES MODEL FIGURE 12 BACKWARD CHAINING RULES MODEL FIGURE 13 HIGH BLOOD PRESSURE PROTOCOL FIGURE 14 A KNN EXAMPLE FIGURE 15 A FEED FORWARD THREE-LAYERED ARTIFICIAL NEURAL NETWORK FIGURE 16 THE CBR PARADIGM FIGURE 17 PREDICTIVE NETWORK OF COPD FIGURE 18 COPD IDENTIFICATION ALGORITHM FIGURE 19 SEGMENTED IMAGE OF LUNG FIGURE 20 EXAMPLES OF SPIROMETRY FLOW-VOLUME CURVES. (A) RESTRICTIVE (B) OBSTRUCTIVE (C) MIXED (D) NORMAL FIGURE 21 COPD DIAGNOSIS RULE FOR FUZZY VALUES FIGURE 22 FEEDBACK FROM ASTHMACRITIC FIGURE 23 DECISION SUPPORT AREAS OF ASTHMACRITIC [60] FIGURE 24 DECISION CRITERIA FOR PATIENT REFERRAL [62] FIGURE 25 KNOWLEDGE AND DATA IN THE CDSS ENVIRONMENT D 6 1 Inference Engine Design.2011_10_31_21 page 6 of 73

7 FIGURE 26 GUI OF THE RULES AUTHORING TOOL [71] FIGURE 27 CDSS SYSTEM COMPONENTS AND EXTERNAL SYSTEMS List of tables TABLE 1: CATEGORIES AND FEATURES OF A CDSS TABLE 2: ATTRIBUTES OF PROFORMA GENERIC TASKS TABLE 3: AREZZO APPLICATIONS TABLE 4: TALLIS APPLICATIONS TABLE 5: MODELLING ELEMENTS OF GLIF TABLE 6: LITERATURE SEARCH OF CDSSS FOR COPD TABLE 7: RELEVANT PUBLICATIONS FOR CLINICAL DECISION SUPPORT APPLICATIONS45 TABLE 8: TELEHEALTH PATIENT PARAMETERS USED IMPLEMENTED BY SYSTEMS D 6 1 Inference Engine Design.2011_10_31_21 page 7 of 73

8 1 Introduction Synergy-COPD is an EU Seventh Framework Program project to research, design, implement and validate a platform and methodology which will ultimately support clinicians to take more informed decisions. The disease used as pilot case study is chronic obstructive pulmonary disease (COPD), which is a major cause of chronic morbidity and mortality throughout the world [1]. COPD is a preventable and treatable disease characterized by airflow limitation that is not fully reversible and is mainly caused by smoking. The airflow limitation is usually progressive and associated with an abnormal inflammatory response of the lung to noxious particles. Although primarily a pulmonary disease, COPD has some extrapulmonary effects that may contribute to the severity in individual patients. Primary symptoms include cough, sputum production and dyspnea (shortness of breath) on exertion; episodes of acute worsening (exacerbation) of these symptoms often occur. The presence of systemic inflammation in COPD has been linked to a variety of complications, including weight loss, cachexia (wasting syndrome), osteoporosis, and cardiovascular diseases. An important problem in healthcare is the significant gap between optimal evidencebased medical practice and the care actually applied. In a systematic review [2] of COPD guideline adherence, it was found that non-pharmacological treatment was infrequently explored for the in-hospital management of COPD exacerbations, and the assessment and therapy were suboptimal. This trend exists not only in COPD but across all chronic-disease care in general: in a multinational survey [3] of chronically ill adults, 14-23% of cases reported at least one medical error in the previous two years. Clinical decision support systems (CDSSs), which aim to provide clinicians, staff, and patients with advice and personalized information, have the potential to enhance healthcare and health, and to help close the gap between optimal practice and actual clinical care. A CDSS can be defined as "software that is designed to be a direct aid to clinical decision-making in which the characteristics of an individual patient are matched to a computerized clinical knowledge base, and patient-specific assessments or recommendations are then presented to the clinician and/or the patient for a decision" [4]. One of the primary objectives in the Synergy-COPD project is to develop a CDSS so that medical staff (primary care clinicians, specialists, and/or nurses) can obtain fast, reliable and directly applicable advice when treating a patient with COPD. The Synergy-COPD CDSS will use models described in Deliverable 4.1 [5], probabilistic modelling (Deliverable 5.1 [6]), existing knowledge from specialists and other evidence-based sources in early diagnosis of the disease, prediction of disease progression, and personalized treatment-advice for patients. The purposes of this deliverable are: to review the main types of CDSS that exist in research and in medical practice with a focus on the reasoning or inference methods; to specify the core requirements, assumptions and functionality of the CDSS to be developed in Synergy-COPD; D 6 1 Inference Engine Design.2011_10_31_21 page 8 of 73

9 to outline the main components that will form part of the Synergy-COPD CDSS design, and the plan to execute the work. 1.1 A note of clarification In the title of this document (Design of Inference Engine) and in the context of WP6 [7], by the expression inference engine we mean the core reasoning engine and non-gui part of the CDSS. That includes components such as a guideline execution engine (GEE), a rules engine, a Bayesian inference engine, a case-based reasoning (CBR) engine. Hence from now on we will use the general expression reasoning engine as it better captures this intention. D 6 1 Inference Engine Design.2011_10_31_21 page 9 of 73