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Lections № 5. Decision Support and Expert Systems in Medicine. Main Questions. Decision Support Systems Basics . Decision Support Systems in Medicine Expert systems Artificial neural network. 1. Decision Support Systems Basics. Information systems definition
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Lections №5 Decision Support and Expert Systems in Medicine
Main Questions • Decision Support Systems Basics. • Decision Support Systems in Medicine • Expert systems • Artificial neural network
1. Decision Support Systems Basics • Information systems definition • Decision support systems definition • DSSTaxonomies • DSS Architecture • Benefits of DSS
1.1. Information system definition An Information System (IS) is the system of persons, data records and activities that process the data and information in a given organization, including manual processes or automated processes; The computer-based information systems are the field of study for Information technologies (IT)
1.2. Decision support systems definition Decision support systems (DSS)are a class of computer-basedinformation systemsincluding knowledge based systemsthat supportdecision making activities. The term decision support system has been used in many different ways and has been defined in various ways depending upon the author's point of view:
1.2. Decision support systems definition • DSS is a model-based set of procedures for processing data and judgments to assist a manager in his decision-making. • Little, J.D.C.(1970, April). "Models and Managers:The Concept of a Decision Calculus. • DSS is an extendible systems capable of supporting ad hoc data analysis and decision modeling, oriented toward future planning, and used at irregular, unplanned intervals. • Moore, J.H.,and M.G.Chang.(1980,Fall)."Design of Decision Support Systems."
1.2. Decision support systems definition • DSS it isa computer-based system that aids the process ofdecision making. • Finlay, P. N. (1994). Introducing decision support systems. • DSS it an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights. • Turban, E. (1995). Decision support and expert systems: management support systems.
1.2. Decision support systems definition (current) A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making activities. • DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance. • DSS can be either fully computerized, human or a combination of both. • (Wikipedia, 2012.)
1.3. DSS Taxonomies Using the relationship with the user as the criterioncan be differentiate passive, active, and cooperative DSS: • Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. • A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. • An active DSS can bring out such decision suggestions or solutions.
1.3. DSS Taxonomies • A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated.
1.3. DSS Taxonomies Using the mode of assistance as the criterion, differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS: • Power, D. J. (2002). Decision support systems: concepts and resources for managers. • A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting
1.3. DSS Taxonomies • A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data. • A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive.
1.3. DSS Taxonomies • A document-driven DSS manages, retrieves and manipulates unstructured information in a variety of electronic formats. • A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures. • Moust important for medical applications.
1.3. DSS Taxonomies The DSS has been classified into the following six frameworks: • Text-oriented DSS; • Database-oriented DSS; • Spreadsheet-oriented DSS; • Solver-oriented DSS; • Rule-oriented DSS; • Compound DSS(hybrid system ). • Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach.
The DSS Data Management Component User Interface Management Component Model Management Component 1.4. DSS Architecture
1.4. DSS Architecture • The Data Management Component (DBMS) stores information. Information can be further subdivided into: • derived from an organization's traditional data repositories, • derived from external sources such as the Internet, • or derived from the personal insights and experiences of individual users;
1.4. DSS Architecture • the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); • the User Interface Management Component is of course the component that allows a user to interact with the system.
1.5.Benefits of DSS • Improving Personal Efficiency • Expediting Problem Solving • Facilitating Interpersonal Communication • Promoting Learning or Training • Increasing Organizational Control
2. Decision Support Systems in Medicine • Digital dashboard • Clinical decision support system • Medical logic module. Arden syntax
2.1. Digital dashboard • A digital dashboard(enterprise dashboard or executive dashboard) is a business management tool used to visually ascertain the status (or "health") of a business enterprise via key business indicators. • Digital dashboards use visual, at-a-glance displays of data pulled from disparate business systems to provide warnings, action notices, next steps, and summaries of business conditions.
2.1. Digital dashboard Some benefits to using digital dashboards include: • Visual presentation of performance measures • Elimination of duplicate data entry. • Ability to identify and correct negative trends. • Measure efficiencies/inefficiencies. • Ability to generate detailed reports showing new trends. • Increase overall revenues. • Ability to make more informed decisions based on collected BI (business intelligence) • Align strategies and organizational goals.
2.2. Clinical decision support system • Clinical (or diagnostic) decision support systems (CDSS) are interactive computer programs, which are designed to assist physicians and other health professionals with decision making tasks. • "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care". • Dr. Robert Hayward of the Centre for Health Evidence
2.1. Clinical decision support system The basic components of a CDSS include (additionally in compare to simple DSS): • a dynamic (medical) knowledge base • an inferencing mechanism (usually a set of rules derived from the experts and evidence-based medicine and implemented through medical logic modules based on a language such as Arden syntax). It could be based on Expert systems or artificial neural networks or both (connectionist expert systems).
2.3. Medical logic module A medical logic module (MLM) is an independent unit in a health knowledge base that combines the knowledge required and the definition of the way it should be applied for a single health decision. An event monitor program in an electronic medical record (EMR) uses it, on occurrence of defined conditions. A grammar - the Arden syntax has been defined which would make MLMs swappable between different hardware and software platforms.
2.3. MLM. Arden syntax • The Arden syntax is a grammar for describing medical conditions and recommendations, used in Medical algorithms. • MLM are written in Arden syntax, and are called by a program - an event monitor - when the condition they are written to help with occurs. • Arden syntax was formerly a standard under ASTM, and is now part of HL7.
2.3. MLM Example maintenance: title: Creatinine clearance;; version: 1.09;; author: George Hripcsak, M.D.;; library: purpose: To calculate the creatinine clearance for every timed urine collection;; explanation: When a timed urine collection is stored, the MLM checks for;; knowledge: data: let urine_creat_storage be event {'32506','1762'}; let (urine_creat, collect_time) be read last {'evoking', 'dam'="PDQRES1";'1762'; '1537'};;; evoke: starting time of urine_creat_storage;; logic: let serum_creat be nearest (time of urine_creat) from (serum_creat_list where it is number); let creat_clear be 0.07 * (24 / collect_time) * (urine_creat / serum_creat); conclude true; ;; action: write "The creatinine clearance is " ||int(0.5+creat_clear)|| " ml/min based upon a " ||collect_time|| " hour urine creatinine of " ||urine_creat||.....; ;; end:
3. Expert systems • Expert systems definition • Architecture of the ES • ES Advantages and disadvantages
3.1. Expert system definition • An expert system, also known as a knowledge based system, is a computer program that contains the knowledge and analytical skills of one or more human experts, related to a specific subject. • This class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s.
3.1. Expert system definition • Expert systems provide expert-quality advice, diagnoses and recommendations on real world problems • Designed to perform function of a human expert • Examples: • Medical diagnosis - program takes place of a doctor; given a set of symptoms the system suggests a diagnosis and treatment
3.1. Prominent medical ES • Mycin - Diagnose infectious blood diseases and recommend antibiotics (mid-1970s, Stanford University) • CADUCEUS - Blood-borne infectious bacteria (mid-1980s, University of Pittsburgh). • STD Wizard - Expert system for recommending medical screening testshttp://www.stdwizard.org/
Knowledge Base Production rules Inference Engine Recognise-act cycle User Interface Compared to production rules Working Memory 3.2. Architecture of the ES
3.2. ES - Introduction to Rules • The knowledge base of an expert system is often rule based – the system has a list of rules which determine what should be done in different situations • These rules are initially designed by human experts • The rules are called production rules • Each rule has two parts, the condition-action pair • Condition – what must be true for the rule to fire • Action – what happens when the condition is met • Can also be thought of as IF-THEN rules
3.2. ES - Conditions example • Conditions are made up of two parts: • Objects – eg the weather • The objects’ value – eg sunny • IF sunny(weather) THEN print “wear sunglasses” • May also be an operator, such as greater than: • IF >30(temperature) THEN print “take some water” • Conditions may also be joined together using AND, OR, NOT: • IF sunny(weather) AND outdoors(x) print “take your sunglasses x”
3.2. ES - The Working Memory • The contents of the working memory are constantly compared to the production rules • When the contents match the condition of a rule, that rule is fired, and its action is executed • More than one production rule may match the working memory
3.2. ES - The Working Memory • The system cycles around in the recognise-act cycle • Whenever a condition is matched, it is added to the conflict set– all the rules which are currently matched • The system must then decide which rule within the conflict set to fire – conflict resolution
3.2. ES - Knowledge acquisition • The system must liaise with people in order to gain knowledge and the people must be specialised in the appropriate area of activity. • The knowledge engineer (KE) acts as an intermediary between the specialist and the expert system. The KE is also responsible for the self consistency of the data loaded. • Typical of the information that must be gleaned is vocabulary or jargon, general concepts and facts, problems that commonly arise, the solutions to the problems that occur and skills for solving particular problems.
