Introduction and Overview

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Dec 4, 2013 (4 years and 28 days ago)

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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


Submitting homeworks and assignments


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

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


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



Slides Overview


Computational Intelligence



Computational Intelligence in Medicine



Course Information

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


Computational

Intelligence
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


...

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.



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

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)



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



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

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





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


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



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


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?

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



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

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



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



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


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



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



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


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


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


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"


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






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)

State of the Art in AI


Deep Blue beats Kasparov (1997)

State of the Art in AI


Spirit, Opportunity (2003) and Curiosity (2012)
explore Mars

State of the Art in AI


DARPA grand challenge: Autonomous vehicle
navigates across desert and then urban environment
(2004
-
2007)

State of the Art in AI


IBM supercomputer “Watson” beats the best
human champions on Jeopardy!


Feb 14
-
16
th

2011

State of the Art in AI


Automated speech/language systems for
airline travel



Spam filters using machine learning



Usable machine translation through Google

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

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

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

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


AI in Medicine: Sub
-
Areas


Natural Language Processing


Unlock the value buried in text and narrative
records so that content can be used

for
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


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



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


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:
#@$%&^#


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



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?

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



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)

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

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/