A common ground theory of medical decision-making 2: The knowledge ladder John Fox Department of Engineering Science University of Oxford and OpenClinical

Knowledge based systems and theories of knowledge Expert systems – “a piece of software which uses databases of expert knowledge to offer advice or make decisions in such areas as medical diagnosis” Knowledge based systems – “A KBS is a computer program that reasons and uses knowledge to solve complex problems” Knowledge representation – “use of symbols … in a conceptual model of the world; symbols are arranged in order to form semantic constructions and express relations between concepts”

Knowledge engineering Representation and formalisation of knowledge – Domain specific and general knowledge – Theory and semantics of concepts and ontologies Knowledge acquisition – Elicitation of knowledge from human experts – Semi-automatic methods (e.g. machine learning) Symbolic processing techniques – Computational architectures for knowledge processing – Practical tools for creating and maintaining knowledge bases

PROforma: A knowledge representation language for decision engineering A general notation for modelling clinical processes Grounded in a logical theory of decisions, plans and knowledge Applications are composed out of a minimal set of generalised task models Fox et al, Proc. MIE 1996; Fox and Das, MIT Press, 2000 Sutton and Fox JAMIA, 2003

An analogy from music

OpenClinical.net: a repertoire of medical compositions

Formalising and sharing knowledge Source content Trials, systematic reviews, guidelines, evidence Source content Trials, systematic reviews, guidelines, evidence Point of care e.g. Routine care, clinical research, patient services Point of care e.g. Routine care, clinical research, patient services Machine executable models of practice PROforma + Machine executable models of practice PROforma + Open access, open source repository Repertoire Open access, open source repository Repertoire

The CREDO stack

The medical knowledge ladder Concepts Terms Descriptions Rules Decisions Plans Agents

Concepts Terms Descriptions Rules Decisions Plans The medical knowledge ladder Agents Class hierarchies, semantic networks Diseases, Symptoms, Findings, Drugs Medical facts, Clinical notes Alerts, reminders, interpretations Options, reasons, evidence, preferences Care pathways, workflows Terminologies, coding systems Expert systems, Personal care agents ICD-10, Read, LOINC, MeSH HSCIC Headings Arden Syntax; GDL, SPARQL, XBN PROforma OWL/RDF, OpenEHR, PROforma SNOMED-CT, HL7, OpenEHR ASBRU, GLIF, BPMN PROforma, GEM, PROforma PRS, Agent languages PROforma

OpenClinical.net

WHO International classification of diseases Terms Primary healthcare Information Support Other healthcare related classifications 3-character core Diagnoses Symptoms Abnormal Lab findings Injuries and poisonings External causes of morbidity and mortality Factors influencing health status Speciality codes oncology dentistry dermatology psychology neurology obstetrics & gynaecology rheumatology & orthopaedics general medical practice International Nomenclature of Diseases

SNOMED Systematized NOmenclature of MEDicine A general purpose, comprehensive medical terminology Computer-readable standard Representing and indexing “virtually all of the events found in the medical record” SNOMED CT (Clinical Terms) is being widely promoted as a global language (English)

SNOMED code for tuberculosis DE X-referencing Tuberculosis Bacterial infections E = Infections or parasitic diseases D = Disease or diagnosis X-ref e.g. living organism, morphology, function

Problems with coding systems Terms are subjective, often vague, ambiguous Codes often ad hoc – no systematic relationship between code and medical concepts – Context dependent (e.g. “normal BP”) – Evolve over time There are a lot of coding systems/standards Mapping between systems difficult The goal of modern informatics must be to capture meaningful concepts rather than use ad hoc codes -> SNOMED CT

Concepts Terms Concepts Formalisms for organising and formalising the meaning of concepts in a domain Domain concepts linked together in a network using relations characteristic of the domain Class hierarchies – Inheritance of attributes over classes

Ontologies and descriptions Capture common understanding of the meaning of information – Between people, computers, both Enable sound techniques for reasoning, decision-making, planning, learning … by computers Introduce standards across medical specialties Enable trustworthy use and reuse of domain knowledge

Concepts Terms Ontologies and descriptions (compare semantic networks, dependency graphs, BNs) Descriptions Cancer Breast cancer IS_A Chemotherapy Radiotherapy Treatments IS_A Symptoms Lump Weight loss IS_A causes caused_by Patient1 Patient2 instance

Concepts Terms Descriptions Rules Rule-based decision support if last_creatinine is not present then alert_text := "No recent creatinine available. Consider ordering creatinine before giving IV contrast."; conclude true; elseif last_creatinine > 1.5 then alert_text := ”This patient has an elevated creatinine. Giving IV contrast may worsen renal function." ; conclude true; else conclude false; endif;

Reasoning over ontologies if Subclass is a kind of Class and Property is a property of Class then Property is a property of SubClass If Disease is a cause of Symptom and Patient has Symptom then possible diagnosis of Symptom in Patient is Disease

Rule-based decision support systems Rules “engines” widely adopted Simple technique, easy to implement in software Practical and appealing for enhancing EHR with alerts, reminders, automating clinical orders … But Only address a single hypothesis/action Does not (usually) capture uncertainty or time Cannot capture dependencies between decisions … No formal foundations

Description logics Descriptions of medical facts and clinical data stated in propositional sentences in some L First order predicate calculus is a commonly used and versatile/powerful tool for reasoning over ontologies and descriptions Known to be computationally intractable in worst case – do not need all the power of FOPC; – adopt simpler axioms with more favourable computational properties –

PROforma “task” ontology

Concepts Terms Descriptions Terms to descriptions

Concepts Terms Descriptions Rules Rules in PROforma

Concepts Terms Descriptions Rules Decisions PROforma decision engineering

Concepts Terms Descriptions Rules Decisions PROforma decision model

Concepts Terms Descriptions Rules Decisions Plans Decisions in context: workflows, care plans and pathways

Kinds of decision-making and expertise Concepts Terms Descriptions Rules Decisions Plans Agents

Concepts Symbols Descriptions Rules Decisions Plans Class hierarchies, semantic networks Diseases, Symptoms, Findings, Drugs Medical facts, Clinical notes Alerts, reminders, interpretations Options, reasons, evidence, preferences Care pathways, workflows Terminologies, coding systems Towards a second common-ground theory: understanding knowledge AgentsExpert systems, Personal care agents ICD-10, Read, LOINC, MeSH HSCICHeadings Arden Syntax; Health eDecisions GDL, SPARQL, XBN OWL, Gello OpenEHR, RDF SNOMED-CT, HL7, OpenEHR ASBRU, GLIF PROforma,, BPMN? GEM, PROforma PRS, Prolog, Agent languages PROforma

Conclusions Understanding medical expertise requires general models for reasoning, decision-making, planning etc (of course) … but a key (the largest?) component of medical expertise is what the expert knows AI and knowledge representation research have shown that knowledge is semantically complex but systematically structured Theories of knowledge and techniques of knowledge engineering open up exciting possibilities for formalising and sharing medical expertise and exploring new techniques for improving decision-making and the quality and safety of common medical practice

Conversations are two way… OpenClinical.net is based on a kind of publishing model (compare Wikipedia) Question to behavioural scientists - how can we engage the stakeholders to use it? – Professionals – Patients – Providers “The problem is in the implementation” … incentives, bonuses, payoffs, …?