HAP 752 - 11 - intelligent systemshi.gmu.edu/hap752/content/Lecture_11/HAP 752 - 11... · Title:...

HAP 752 Advanced Health Informa6on Systems Copyright © Janusz Wojtusiak, 2014

Intelligent Systems in Healthcare

Janusz Wojtusiak, PhD George Mason University

Spring 2014

HAP 752 Advanced Health Informa6on Systems


Any sufficiently advanced technology is indis6nguishable from magic.

-‐ Arthur C. Clarke


What is Intelligence?

•  IQ scores? •  GRE scores? •  Driving ability? •  General knowledge? •  Math skills? •  Playing chess?


What is Intelligence? •  Abstract thinking •  Reasoning •  Memorizing •  Learning •  Understanding •  Planning •  Problem-‐solving •  Communica6ng •  Self-‐awareness •  Emo6onal knowledge


What is Ar6ficial Intelligence?

•  AI concerns intelligent behaviors in machines – OYen machines in AI are called agents

•  Intersects mul6ple disciplines –  Computer science, philosophy, engineering, psychology, cogni6ve science, economics, mathema6cs, neuroscience, linguis6cs, ….


Intelligent Systems

•  Intelligent systems use AI methods to perform be[er than simple “algorithmic” approaches


Intelligent Systems

•  Perceive •  Reason •  Act •  Communicate •  Learn


Example: Infobu[ons

•  Somewhat idealized example of Infobu[ons


Example: Infobu[ons

•  Perceive the current clinical situa6on – Pa6ent characteris6cs – Clinician characteris6cs – Organiza6on characteris6cs – Clinical task –  Informa6on requested


Example: Infobu[ons

•  Reason about the current situa6on – What are user needs? – What exactly is useful? – How the clinician can be helped? – What are the best resources to display? – Where to find the resources?


Example: Infobu[ons

•  Act to obtain right informa6on – Construct query – Connect to Infobu[on broker or directly to resources

– Retrieve answers


Example: Infobu[ons

•  Communicate obtained informa6on to the clinician – Aggregate informa6on from mul6ple sources – Select the most relevant informa6on – Generate natural language descrip6ons, graphics, summaries, etc.

– Display results


Example: Infobu[ons

•  Learn from experience – How results can be improved? – Get feedback if the task was successful – What worked the best and what did not work – Find out clinician preferences –  Improve matching pa6ent characteris6cs to resources


Another Example

h[p://www.aaaivideos.org/2011/robo6c_secrets_revealed_002/


Is AI used in Healthcare?

•  Most modern soYware use methods developed in AI – EMR – CDSS – Pa6ent monitoring – Scheduling & logis6cs – Mobile devices


Why Intelligent Systems in Healthcare?

•  Tradi6onal computer systems are insufficient •  Need to automate/support – Tasks: Diagnoses, treatment selec6ons, … – Data: images, text, speech, …

•  Need for full collabora6on between computer systems and their users


PROBLEMS


Selected Problems •  Percep6on •  Knowledge representa6on •  Reasoning •  Planning/decision making/problem solving •  Coopera6on •  Learning •  Natural language/image processing •  Movement & coordina6on •  Crea6vity •  General intelligence


Selected Problems: Percep6on

•  The ability to sense environment and the current situa6on (“inputs” to the system)

•  Coded data – Databases, forms, inputs from other systems

•  Non-‐coded data (most work on percep6on) – Sensors, cameras, microphones – Object recogni6on, voice recogni6on


Percep6on: Image Recogni6on

•  The ability to recognize contents or specific features of images

•  Mul6ple applica6ons – Support radiology – Computer vision –  Iden6fica6on of people –  Image classifica6on



•  Image forma6on –  Lenses, light, shading, color, …

•  Image processing –  Edge detec6on, texture, segmenta6on, ….

•  Image/object recogni6on (very difficult) – Object appearance, seman6c recogni6on, ….

•  Reconstruc6on of 3D scenes •  Video processing – Object tracking, ….


h[p://spie.org/x8899.xml?pf=true&Ar6cleID=x8899



•  Image forma6on –  Lenses, light, shading, color, …

•  Image processing –  Edge detec6on, texture, segmenta6on, ….

•  Image/object recogni6on (very difficult) – Object appearance, seman6c recogni6on, ….

•  Reconstruc6on of 3D scenes •  Video processing – Object tracking, …. h[p://www.youtube.com/watch?

v=1GhNXHCQGsM


Percep6on: Natural Language Processing (NLP)

•  Es6mated 70% of EHR informa6on is in notes •  Different problems are being addressed – Search – Coding, informa6on retrieval – Document classifica6on – Wri6ng summaries – Transla6on – Ques6on answering


Percep6on: Speech Recogni6on

•  Transla6on of spoken words to text •  Healthcare applica6ons – Automa6ng clinical documenta6on –  Interac6on with computers

•  NLP can be then used to code the recognized text


Selected Problems: Knowledge Representa6on

•  The ability to store knowledge/models – Symbolic (rules, trees, ontologies) – Sub-‐symbolic (neural networks, computa6onal intelligence, evolu6onary computa6on, control theory)

– Sta6s6cal (Bayesian, HMM, SVM) – Hybrid (combina6on of the above)


Selected Problems: Reasoning

•  Logic(s) •  Algorithms (forward/backward chaining, probability upda6ng, case-‐based, …)


Selected Problems: Problem-‐solving

•  Select the most op6mal ac6on or a sequence of ac6ons

•  Search •  Op6miza6on •  Measuring performance – Completeness – Op6mality – Complexity (6me/space)


Search Example: Tic-‐tac-‐toe

Spurce: wikipedia


Planning Example •  Dynamic Vehicle Rou6ng Problem (DVRP)

–  Deliver goods from sources to des6na6ons

–  Availability of haulage requests is not known in advance

–  Global planning is infeasible

•  Distributed planning approach –  Agents ac6ng on behalf of single trucks in the forwarder’s transport fleet decide autonomously about handling of requests

–  Agents ac6ng on behalf of single requests con6nuously valuate their own transport for the forwarder, thus seeking to ensure 6mely delivery


Selected Problems: Coopera6on

•  Single specialized computer system is oYen not sufficient to solve complex problems

•  Similarly to people, autonomous agents cooperate in solving tasks

•  Examples include robo6cs, mul6agent systems, but also decision support systems, planning systems, and so on.


Selected Problems: Coopera6on

h[p://www.aaaivideos.org/2011/swarmanoid_the_movie


Selected Problems: Learning

•  Learning is one of key abili6es of intelligent systems

•  When interac6ng with environment intelligent systems can find ways to improve their performance



•  Supervised learning – Teacher shows how to do things, or shows examples

•  Unsupervised learning – No teacher, the learner figures out that some things are similar and categorizes them

•  Reinforcement learning – Learner explores to find a be[er way of doing something



h[p://www.aaaivideos.org/2010/case_based_imita6on_sequel


Selected Problems: Movement

•  Significant progress in AI has been made in the field of robo6cs

•  Intelligent agents may take physical form, not only act as soYware agents

•  Some healthcare applica6ons include – Surgery – Logis6cs & delivery – Automated pharmacy


Selected Problems: Movement

h[p://www.youtube.com/watch?v=vcD2leYbhxk


Selected Problems: Crea6vity

•  What is crea6vity? •  How machines can be crea6ve? •  Ar6fact genera6on – Wri6ng poems, pain6ng pictures, inven6ng theories, composing music

•  Three forms of crea6vity – Combinatorial – Exploratory – Transforma6onal


Selected Problems: Ar6ficial General Intelligence

•  Building of general-‐purpose “thinking machines”

•  Long-‐term goal of building human-‐level intelligence

•  Several projects – CAM Brain Machine (CBM)


Strong AI vs. Weak AI

•  Strong AI – building human-‐level intelligence

•  Weak AI – applica6ons of AI methods


SELECTED METHODS/AREAS


Mul6agent Systems

•  Used to solve very large problems •  Independent agents performing their tasks – Autonomy, decentraliza6on, self-‐organiza6on

•  Communica6on between agents •  Real-‐6me or simula6on models •  Applica6ons – Transporta6on, logis6cs, planning


Mul6agent Systems

h[p://www.youtube.com/watch?v=ilLylU1u0iQ&feature=related


Evolu6onary Computa6on

•  Class of op6miza6on methods – The goal is to find the best solu6on according to some criteria

•  Used for very complex problems –  In one applica6on to complex systems op6miza6on, the search space was 2.6 x 1021

–  It would take 8 x 1011 years to search through all possible solu6ons (assuming 1 sec per evalua6on)



•  The idea is to mimic evolu6onary process •  Combina6on of good solu6ons is likely to produce new good solu6ons

•  Uses terminology borrowed from biology – Popula6on, individual, survival, fitness, ….



•  Many applica6ons – Management, resource alloca6on – Radiology – Treatment selec6on – Design


Next Week

•  We will con6nue with intelligent systems and go into more of – Learning – Data Mining

•  More reading on AI


HAP 752

Janusz Wojtusiak

George Mason University [email protected]

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