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Artificial Intelligence & Clinical Decision Support. Including fuzzy logic, neural nets, and genetic algorithms 

Kevin LopezComputer Science & Engineering Department

The University of Connecticut371 Fairfield Road,

Storrs, CT 06269-2155

kevin.lopez@uconn.edu

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What is Clinical Decision Support?What is Clinical Decision Support? Clinical Decision Support is:Clinical Decision Support is:

Knowledge provided to clinicians From Multiple Sources/Contexts, processed and

returned in a form that will assist a care giver. Involves processing via various artificial

intelligence and machine learning technologies CDS is Multi-DisciplinaryCDS is Multi-Disciplinary

Computing (Information Processing, Data Analysis)

Social Science (User Interactions) Clinical Decision is applicable to many domains:Clinical Decision is applicable to many domains:

Can be used in any type of medicine, including domains with weak domain theory.

Its underlying systems (AI) is used for any field

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What is Clinical Decision Support?What is Clinical Decision Support? Key people(s) affected:Key people(s) affected:

Patients Physicians, clinicians, care givers Hospitals/medical centers

Standards:: Arden Syntax (Syntax) GELLO (Common Expression Language) Infobutton (Context-aware Knowledge Retrieval)

Techniques:Techniques: These are still being worked on and researched. No set technique

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What is Clinical Decision Support?What is Clinical Decision Support? A combination of A combination of

different knowledge's.different knowledge's. Knowledgebase

(Textbook, etc) Clinicians

Knowledge/experience

Gained Experience from learning, and individual patients

Knowledgebase

Clinician Gained Experience

Clinical Decision Support System

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Why use a CDSS?Why use a CDSS? We use a CDSS because of:We use a CDSS because of:

It provides better quality of care Can provide the clinician with a second opinion Can guide a novice clinician to a solution,

diagnosis, or treatment. Can help reduce the number of errors It can help with the speed and quality of diagnosis It improves customer/patient satisfaction Can be interactive (with the clinician) to get the

best results. Can be nearly autonomous, some systems are

personal and can give a diagnosis.

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Functions of a CDSSFunctions of a CDSS A CDSS generally works by:A CDSS generally works by:

Taking in some data, normally it is some patient data

This data can be measurements, clinician data, or knowledgebase data.

The data then must be extrapolated and the most relevant parts used for processing.

The data is then processed with the method of choice (ANN, CBR, Fuzzy etc.) and may require clinician input as well.

The data is then post processed and outputted in a variety of fashions (can be numerical, binary, or even text).

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Designing a CDSSDesigning a CDSS Main problems these systems must solveMain problems these systems must solve

Structured These problems are routine and repetitive Solutions exist, and are standard and predefined

Unstructured Complex and fuzzy Lack Clear and straightforward solutions

Semi-structured This is a combination of the two previous catagories.

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Artificial intelligence's role in clinical decision support

Two types of CDSSTwo types of CDSS Work with Knowledgebase Work with Non-Knowledgebase

Knowledge based CDSS:Knowledge based CDSS: Use knowledge from sources such as textbooks,

and other resources. They have rules similar to if-then statements.

Components of a knowledge based CDSS:Components of a knowledge based CDSS: Knowledgebase: Some source where they get their

knowledge Inference engine: takes data and applies the rules

from the knowledgebase Communication: Allows system to communicate

with user and user input.

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Hybrid SystemsHybrid Systems Hybrid systems Knowledge and Non-Knowledge Hybrid systems Knowledge and Non-Knowledge

based systembased system These systems produce high quality results from

the merge of the two different systems. They have an already established knowledge base

but they also must learn from past experiences or from test results.

These systems often Produce results that are better than these systems individually.

These systems can be a combination of many of the different technologies that each system has.

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Artificial Neural NetworksArtificial Neural Networks Similar to real neural Similar to real neural

networksnetworks Take in data and

pass them through the network to the other neurons to get an output.

Many times used for pattern recognition

Several different algorithms can be used for threshold

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Case-Based ReasoningCase-Based Reasoning Case-based reasoning is:Case-based reasoning is:

A process of solving new problems based off of old problems.

Similar to how humans think and solve problems. Can take new solutions that have been solved and

add them to the database of solutions for future reference.

There are Four Steps (R’s) to case based reasoning:There are Four Steps (R’s) to case based reasoning: Retrieve: where the system retrieves the

knowledge Reuse: takes old experience and maps it to new

problem Revise: revise the solution Retain: put new solution into the system database

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Case-Based ReasoningCase-Based Reasoning The four R’s for Case based reasoningThe four R’s for Case based reasoning

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Fuzzy TechniquesFuzzy Techniques Fuzzy Logic is:Fuzzy Logic is:

Degrees of truth, 0 and 1 are extremes. Some types of data do not have what we consider a

full truth or false. An example of Fuzzy LogicAn example of Fuzzy Logic

An example of this is natural language processing. This is where truths are aggregated from partial

truths. This is to derive meaning from humans such as

notes a doctor put in or some other source of natural language.

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Genetic AlgorithmsGenetic Algorithms Based off of a simplified evolutionary process used to Based off of a simplified evolutionary process used to

arrive at an optimal solution.arrive at an optimal solution. It works in the following way:It works in the following way:

Children are made and try to solve the problem The top few children then are used to generate new

children This process continues until an optimal (or very

close to optimal) solution is found. In CDSS:In CDSS:

The selected algorithms evaluate the solution Of these solutions the best are chosen and they try

to evaluate the problem again until the solution is found.

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Feature SelectionFeature Selection Feature Selection is:Feature Selection is:

Selecting features or attributes from a set of data Useful for taking out certain data that is not needed

during processing Similar to how we process data, we do not need to

know all of the data but we extract key items from the data.

Data may have redundant features that provide no more information as the features previously selected.

Feature Selection is used in getting the data that is Feature Selection is used in getting the data that is required.required. Allows for less and unnecessary processing.

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Personal MedicinePersonal Medicine There are several apps that claim to assist with There are several apps that claim to assist with

diagnosis.diagnosis. In particular several skin cancer apps have surfaced.In particular several skin cancer apps have surfaced.

None of which are free Some of which incorporate sending the images to

a clinician for further diagnosis. Some of the apps have the ability to use the

camera to view the skin and take a picture With this picture the program checks for

symptoms, or “ugly duckling moles” Apps are still improving to give more quality care

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Personal MedicinePersonal Medicine

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Effectiveness of CDSSEffectiveness of CDSS How effective are these systemsHow effective are these systems

CDSS’s are becoming more and more effective and accurate at diagnosing diseases.

Many times these systems improve the outcome of both treatments and diagnosis of patients

Many times these systems are integrated into the clinicians workflow to provide superior satisfaction to both the patient and the clinician.

These systems give the clinician a recommendation not just an assessment, so that the clinician can actually follow through.

These systems many times outperform their clinician counterparts in diagnosing a patient.

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Key Technical ProblemsKey Technical Problems Some of the problems that are seen with CDSSSome of the problems that are seen with CDSS

Many different types of artificial intelligence that serve many different purposes

No one generic algorithm that can handle all of the data

Natural language can be very difficult to extract data from

Some domains have weak domain theory Many of the systems need time to train and much

of the training is computationally expensive Data preferred to be shortened (feature selection)

in order to take less time processing.

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Key People ProblemsKey People Problems There are problems that exist where the user may There are problems that exist where the user may

experience either due to lack of experience or experience either due to lack of experience or familiarity.familiarity. Ease of use: The system must be easy to use, and

work right out of the box. There should be minimal configuration if any done

by the clinician. The interface has to be user friendly. Many times

users of these systems have very little computer knowledge.

The user should not have to be trained on this system.

Data input: the data must be entered correctly (ie. switching systolic and diastolic).

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ConclusionConclusion These Systems Show:These Systems Show:

Improvement in patient outcome Higher Patient satisfaction Guidance for inexperienced practitioners Guidance for individuals

These systems cannot:These systems cannot: Replace a doctor/care giver Are limited in how many different diseases each

one can do Be 100% accurate/fool proof

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ReferencesReferences Application of Artificial Intelligence for Clinical Decision Making and Reasoning (Abdalla

S.A.Mohamed) Efficient Clinical Decision Making by Learning from Missing Clinical Data (Farooq, Yang, Hussain,

Huang, MacRae, Eckl, Slack) Developing Decision Support for Dialysis Treatment of Chronic Kidney Failure the researchers

explore and describe what goes into developing a CDS system for dialysis treatment. Hybrid Case-Based System in Clinical Diagnosis and Treatment. A Model to Predict Limb Salvage in Severe Combat-related Open Calcaneus Fractures Clinical Decision support system for fetal Delivery using Artificial Neural Networks the team are using

ANN’s to assist doctors with decisions at critical times of fetal deliveries. Implementing Decision Tree Fuzzy Rules in Clinical Decision Support System after Comparing with

Fuzzy based and Neural Network based systems Case Studies on the Clinical Applications using Case-Based Reasoning Improving clinical practice using clinical decision support systems: a systematic review of trials to identify

features critical to success (Kensaku Kawamoto, Caitlin A Houlihan, E Andrew Balas, David F Lobach) Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient

Outcomes E-Health towards Ecumenical Framework for Personalized Medicine via Decision Support System Standards in Clinical Decision Support: Activities in Health Level Seven And Beyond (

https://www.dchi.duke.edu/conferences/posters-presentations/amia/2011-amia/Kawamoto-StandardsInClinicalDecisionSupport_slides.pdf)

Kai Goebel from Rensselaer Polytechnic Institute (http://www.cs.rpi.edu/courses/fall01/soft-computing/pdf/cbr1to3.pdf)

HealthIT (http://www.healthit.gov/policy-researchers-implementers/clinical-decision-support-cds)