Enhancing Clinical Decision Support with Artificial Intelligence and Machine Learning Techniques

1. Artificial Intelligence & Clinical Decision Support. Including fuzzy logic, neural nets, and genetic algorithms  Kevin Lopez Computer Science & Engineering Department The University of Connecticut 371 Fairfield Road, Storrs, CT 06269-2155 kevin.lopez@uconn.edu

2. What is Clinical Decision Support? • 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-Disciplinary • Computing (Information Processing, Data Analysis) • Social Science (User Interactions) • 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

3. What is Clinical Decision Support? • 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: • These are still being worked on and researched. • No set technique

4. What is Clinical Decision Support? Clinical Decision Support System Knowledgebase Clinician Gained Experience • A combination of different knowledge's. • Knowledgebase (Textbook, etc) • Clinicians Knowledge/experience • Gained Experience from learning, and individual patients

5. Why use a CDSS? • 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.

6. Functions of a CDSS • 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).

7. Designing a CDSS • Main 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.

8. Artificial intelligence's role in clinical decision support • Two types of CDSS • Work with Knowledgebase • Work with Non-Knowledgebase • 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: • 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.

9. Hybrid Systems • Hybrid systems Knowledge and Non-Knowledge based 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.

10. Artificial Neural Networks • Similar to real neural networks • 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

11. Case-Based Reasoning • 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: • 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

12. Case-Based Reasoning • The four R’s for Case based reasoning

13. Fuzzy Techniques • 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 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.

14. Genetic Algorithms • Based off of a simplified evolutionary process used to arrive at an optimal solution. • 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: • 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.

15. Feature Selection • 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 required. • Allows for less and unnecessary processing.

16. Personal Medicine • There are several apps that claim to assist with diagnosis. • 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

17. Personal Medicine

18. Effectiveness of CDSS • How 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.

19. Key Technical Problems • Some 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.

20. Key People Problems • There are problems that exist where the user may experience either due to lack of experience or 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).

21. Conclusion • These Systems Show: • Improvement in patient outcome • Higher Patient satisfaction • Guidance for inexperienced practitioners • Guidance for individuals • These systems cannot: • Replace a doctor/care giver • Are limited in how many different diseases each one can do • Be 100% accurate/fool proof

22. References • 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 (KensakuKawamoto, 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)