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Rohit Kate

Computational Intelligence in Biomedical and Health Care Informatics HCA 590 (Topics in Health Sciences). Rohit Kate. Introduction and Overview. Course Information. Course Website: http://www.uwm.edu/~katerj/courses/cibhi D2L course site will be also used for: Posting some readings

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Rohit Kate

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  1. Computational Intelligence in Biomedical and Health Care InformaticsHCA 590 (Topics in Health Sciences) Rohit Kate Introduction and Overview

  2. Course Information • Course Website: http://www.uwm.edu/~katerj/courses/cibhi • D2L course site will be also used for: • Posting some readings • Submitting homeworks and assignments

  3. Course Information • Textbook: No single book covers all the topics we want to cover • Main text: From Patient Data to Medical Knowledge: The Principles and Practice of Health Informatics by Paul Taylor, BMJ Books, 2006. • Covers many topics that we want to cover (especially its Part 2) and is focused on their medical applications • We will not cover the entire book • Other readings will be made available through D2L site

  4. Course Information • Grading: • 3-4 assignments (60%) • Doing some specific task using tools or software that we will cover in class • Homeworks (10%) • Submit answers to questions if given at the end of the class (last slide) before the next class (through D2L) • Final project (30%) • Come up with your own project based on the topics covered in the class

  5. Introduction and Overview: Reading • Chapter 1, Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig • Paper: Ramesh et al. Artificial Intelligence in Medicine. Ann. R. Coll. Surg. Engl. 2004 Sep;86(5):334-8

  6. Slides Overview • Computational Intelligence • Computational Intelligence in Medicine • Course Information

  7. What is Computational Intelligence? • Intelligence, generally speaking, is the ability to do the right or the best thing in a given situation to achieve a goal • Humans and some animals exhibit intelligent behavior • ComputationalIntelligence is the capability of a computer to exhibit intelligent behavior • A chess playing machine • A robot or a car that finds its way around • A computer that answers natural language questions about a topic • A computer that diagnoses a patient from the symptoms • ...

  8. What is NOT Computational Intelligence? • Word processing softwares • Graphics design using computers • Entering or searching for an entry in a database • Computer networks, Internet protocols • Computer security These tasks require data processing but do not require the computer to be intelligent.

  9. Computational Intelligence as a Discipline • A traditional discipline of Computer Science • More commonly known in Computer Science as “Artificial Intelligence” or “AI” • Artificial Intelligence may seem to suggest that the intelligence is not real or is just a simulation of human intelligence • The term creates misunderstanding outside of Computer Science • “Computational Intelligence” is perhaps a better term and seems to be preferred in Biomedical disciplines • We will use both the terms in the course interchangeably

  10. Definitions of Computational Intelligence • Various definitions are around ranging from philosophical to engineering perspectives • Machines with minds… • Study of design of intelligent agents • A study of duplicating human faculties like creativity, self-improvement and language use. • A good working definition: Study of how to make computers do tasks at which currently humans are better (Rich and Knight, 1991)

  11. What Makes Humans Intelligent? • Knowledge (knowing facts about the world) • Reasoning (ability to derive conclusions) • Learning (improving through experience) • Language and Perception All these are reflected in the major sub-areas of Computational Intelligence or AI

  12. Different Sub-Areas of AI • Knowledge Representation and Reasoning • How to encode knowledge so that computer can reason about it • First-order logic, ontologies, rules, knowledge-bases • Handling uncertain knowledge, probabilistic reasoning • Example application in medicine: Encode knowledge about diseases, symptoms to automatically predict diagnosis

  13. Different Sub-Areas of AI • Machine Learning • Improve performance by learning from examples • Humans do it all the time (learn to walk etc., develop special skills) • Rule-based methods: e.g. decision-trees • Statistical methods: e.g. neural networks, support vector machines, maximum entropy models • Example application in medicine: Learn to diagnose a disease from previous examples of patient data

  14. Different Sub-Areas of AI • Computer Vision • “Eyes of computer/robot” • Recognize objects (e.g. face, human) from an image (e.g. face detection by cameras) • Reconstruct a 3D model from 2D images, e.g. track an object • Example application in medicine: Distinguish between a cancerous from a non-cancerous radiology image

  15. Different Sub-Areas of AI • Natural Language Processing • Understand and process natural languages like English, Chinese etc. • Natural language is the preferred medium of communication for humans • Follow natural language commands • Answer natural language questions • Find required information from several documents • Translate from one natural language to another • Example application in medicine: Answer clinical questions using a repository of research articles

  16. Different Sub-Areas of AI • Robotics • Physical agents that act in the physical world • Example application in medicine: Surgical robots • Planning • Coming up with a best sequence of inter-dependent tasks to perform (e.g. wear socks before shoes) • Example application in medicine: Planning and scheduling in a hospital environment

  17. Foundations of AI • AI is extremely inter-disciplinary; its foundations come from several older disciplines • Philosophy • Where does intelligence come from? • Mathematics • How to infer logically? How to reason under uncertainty? • Economics • How should we make (intelligent) decisions that maximize payoff ? • Neuroscience • How do brains process information? • Psychology • How do humans and animals think and act? • Linguistics • How does language relate to thought? How do we process language?

  18. How to Make Computers Intelligent? • Should we model human intelligence • A good idea but difficult to model • Human brain is different from computer processor • Humans are good at remembering and recognizing patterns; computers are good at crunching numbers • A compromising approach: Model human intelligence as much as possible but also utilize computer’s ability to crunch numbers • Airplanes have wings like birds but they don’t flap them, instead they use engine technology

  19. History of AI • Relatively brief history, only 50-60 years old • Interesting with many ups and downs • A look at its history helps to understand how the current focus and methodologies in AI have emerged

  20. History of AI • Beginnings • McCulloch and Pitts (1943) proposed a model of artificial neurons could compute any computable function • Marvin Minsky (1951) built the first neural network computer using vacuum tubes • Alan Turing (1950) introduced machine learning, genetic algorithms and reinforcement learning

  21. History of AI • Birth of AI • Dartmouth conference (1956) • Organized and attended by some of those who are now regarded as founders of AI: John McCarthy, Marvin Minsky, Allen Newell, Herb Simon • Coined the term “Artificial Intelligence” • Presentation of a reasoning program, "Logic Theorist" which could automatically prove many mathematical theorems

  22. History of AI • Early Years (1950s and 60s) • Several interesting and impressive AI work that people earlier did not believe that computers could ever do • General Problem Solver: Could solve limited classes of puzzles thinking like humans • Geometry Theorem Prover: Could prove theorem that were tricky for students • Checkers player: Disproved the idea that computers can only do what they are told to do, soon the program learned to play better than its creator

  23. History of AI • Early years (1950s and 60s) • SAINT: Solved freshman calculus problems • ANALOGY: Solved IQ test analogy problems • SIR: Answered simple questions in English • STUDENT: Solved algebra story problems • SHRDLU: Obeyed simple English commands in the blocks world

  24. History of AI • Limitations of Early Systems • Could only work on "toy" problems which were not at the scale of real-world problems, for two main reasons • Difficult to formalize and encode real-world knowledge • For example, they tried to build a machine translation system from Russian to English using dictionaries and syntactic transformations, but due to lack of world knowledge: "the spirit is willing but the flesh is weak" -> Russian -> "the vodka is good but the meat is rotten“ • The systems used simple search to find a solution out of all the potential solutions, this would not work with more complex problems which have a combinatorially large space of potential solutions

  25. History of AI • Knowledge-based Systems (1970s) • Realization that domain-specific knowledge could help finding the solution led to several expert systems for specific domains • Encoded rules that human experts would used and so these systems could perform like human domain experts • DENDRAL: First knowledge-intensive system to infer molecular structure from mass spectometer data

  26. History of AI • Knowledge-based Systems (1970s) • MYCIN: A medical expert system, could diagnose blood infections from symptoms • Encoded 450 rules • Could perform as well as some experts and better than junior doctors • But was never used in actual practice because of non-technical reasons

  27. History of AI • AI Industry (1980s) • Several expert systems built and deployed, every major U.S. company had its own AI group to use or to investigate expert system • R1: Helped configure orders for new computers, saved $40 million a year • Japanese started a project to build intelligent computers running Prolog (logic programming language) • In U.S. the company MCC was formed with the same goal

  28. History of AI • AI Industry (1980s) • However, limitations of expert systems became apparent • Brittleness (won't work with slightly different input), too domain-specific • Difficult to acquire all the knowledge even for a specific domain (knowledge-acquisition bottleneck) • A brief period of "AI Winter"

  29. Recent History of AI • Recent years • Focus on learning from examples to address the knowledge-acquisition bottleneck • To counter brittleness: shift of focus from rule-based and logical methods to probabilistic and statistical methods (e.g. Bayesian networks, Hidden Markov Models) • AI has become a science: • Show real-world applications and not success on toy problems • Base claims on hard experimental evidence and not on intuitions • Analyze results for statistical significance, make data and tools available to replicate experiments

  30. Recent History of AI • Recent years • Instead of remaining isolated like early years, AI has embraced other disciplines like statistics, optimization, formal methods etc., whichever areas are needed for success • Useful applications at the large scale of the Internet • search engines, recommender systems, Web site construction systems • data mining (find useful patterns in huge amounts of data)

  31. State of the Art in AI • Deep Blue beats Kasparov (1997)

  32. State of the Art in AI • Spirit, Opportunity (2003) and Curiosity (2012) explore Mars

  33. State of the Art in AI • DARPA grand challenge: Autonomous vehicle navigates across desert and then urban environment (2004-2007)

  34. State of the Art in AI • IBM supercomputer “Watson” beats the best human champions on Jeopardy! • Feb 14-16th 2011

  35. State of the Art in AI • Automated speech/language systems for airline travel • Spam filters using machine learning • Usable machine translation through Google

  36. Computational Intelligence in Medicine • Computational Intelligence involves systematizing and automating intellectual tasks and is therefore potentially relevant to any intellectual activity including Medicine • Modern medicine is faced with the challenge of acquiring, analyzing and applying large amounts of knowledge necessary to solve complex clinical problems, Computational Intelligence methods fit this need and will soon become indispensable

  37. Emergence of AI in Medicine • Over the last few years medicine has become a data-rich quantitative field because of various electronic data capturing methods and data management systems for both clinical care and biomedical research, this is transforming medicine from art to science • The availability of data in electronic form (documents, articles, clinical notes, electronic health records etc.) has increased the necessity and scope of their automatic intelligent processing using AI techniques

  38. Emergence of AI in Medicine • Diagnosis, treatment and predicting outcome depends on complex interactions of many clinical, biological and pathological variables, hence there is a growing need for analytic tools to analyze them • Note: Many AI in Medicine methods are becoming more and more integrated within medical applications often resulting into their loss of visibility, sometimes not even labeled as AI in Medicine methods; paradoxically, a sign of success

  39. AI in Medicine: Sub-Areas • Knowledge Representation • Design of good ontologies to enable data exchange, standardization, communication, e.g. UMLS, SNOMED-CT, Gene Ontology etc. • Encode rules obtained from domain experts to automate processing and reasoning • Enable discovering new and useful knowledge and refine existing knowledge

  40. AI in Medicine: Sub-Areas • Natural Language Processing • Unlock the value buried in text and narrative records so that content can be usedfor automated processing • Interact with computer in natural language, ask clinical questions in natural language to search research articles • Information Retrieval: Find the required information from a collection of documents, answer questions

  41. AI in Medicine: Sub-Areas • Decision Support Systems • Help in clinical diagnosis • Combine uncertain evidences from multiple sources and generate a probabilistic diagnosis • Machine Learning • Image analysis in radiology • Predicting diagnosis, treatment or outcomes • Pattern recognition from medical data • Data Mining • Knowledge discovery from medical databases • Automatically find useful patterns from patient records

  42. Adapting AI to Medicine • Medicine is a human endeavor, any AI system needs to take into account human issues like usability, user-friendliness, user-supportiveness, organizational change, workflow etc. • Human expertise in medicine developed over centuries cannot be discarded or replaced by re-discovering them by analyzing data; build models that integrates human expertise with machine learning methods • The methods that can use the existing knowledge and can refine or augment it are preferable over the methods that completely based on data analysis

  43. Adapting AI to Medicine • Doctor: We need to amputate your finger. • Patient: Why??? • Doctor: Because our expert system that uses the most advanced statistical machine learning technique says so. • Patient: #@$%&^#

  44. Adapting AI to Medicine • The output of an AI system must be human-interpretable, sometimes preference for rule-based machine learning techniques over more accurate but opaque statistical machine learning techniques • Reasoning in medicine is based on arguments, it is not the accuracy of predictions but their explanation and communication that matters 

  45. AI in Medicine: Issues • Although there has been a remarkable progress in AI in Medicine but adoption of these methods have been slow, mostly because of political, fiscal and cultural reasons • If an computer makes a wrong diagnosis leading to bad consequences, who should be held legally responsible? • Many learning methods need a lot of data to learn from, will that compromise medical data confidentiality? • All healthcare workers may not be computer savvy • How much will doctors trust computers?

  46. AI in Medicine: Issues • AI applications are most suited in medicine in the form of: • Supporting tools instead of a stand-alone systems, for example, in suggesting possible diagnoses and their probabilities • Covering human mental shortcomings/lapses • Forgetfulness: reminders of certain tests or medications • Detect possible errors • Searching and mining huge amounts of data which is not humanly possible and present results to humans

  47. Course Structure • In this course we will cover the following AI topics along with medical applications • Probability and Probabilistic Reasoning • Machine Learning • Data Mining • Knowledge Representation • Logic • Ontologies • Natural Language Processing (as time will permit)

  48. Course Structure • For each topic we will generally proceed as follows: • Theoretical understanding of the topic • Look at some medical applications from published research • Learn and use some available tool or software

  49. AI in Medicine: Some Resources • Artificial Intelligence in Medicine • Journal published by Elsevier, accessible online through library’s website • AIME: A European biannual conference of AI in MEdicine • OpenClinical.org • An online resource for knowledge management systems in healthcare includes AI in Medicine (http://www.openclinical.org/aiinmedicine.html) • Artificial Intelligence in Medicine, edited by Peter Szolovits • An old outdated book but still interesting, entirely available online • http://groups.csail.mit.edu/medg/ftp/psz/AIM82/

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