Intelligent Diagnosis System

Advanced Knowledge Based System

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How do we solve problems?

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Different ways to solve problems?

By knowing the steps to apply from symptoms to a plausible diagnosis

But not always applying causal knowledge diseases cause symptoms symptoms do not cause diseases!

How does an expert solve problems? uses same “book learning” as a novice but quickly selects the right knowledge to apply

Heuristic knowledge (“rules of thumb”) “I don’t know why this works but it does and so I’ll use it again!”

difficult to elicit

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Continue..

By remembering how we solved a similar problem in the past memory-based problem-solving re-using past experiences

Doctor remembers previous patients especially for rare combinations of symptoms

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R4 Cycle–(Essential components for CBR based software solutions)

REUSEREUSEpropose solutions from retrieved cases

REVISEREVISEadapt and repair

proposed solution

CBRCBR

RETAINRETAINintegrate in

case-base

RETRIEVERETRIEVEfind similar problems

CBR – Case based resolution

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What is Medical Diagnosis?

An attempt at classification of an individual's condition into separate and distinct categories that allow medical decisions about treatment and prognosis to be made.

A diagnostic opinion is often described in terms of a disease or other condition.

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Diagnosis Problem statement

Given: Symptoms, physical evidence Past history Existing medication

Knowing: symptoms of all diseases Past Cases

Goal: choose the best diagnosis which matches with the users condition

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Diagnosis Knowledge

Get-possible-disease

IF: Symptom is same as that of diseaseAnd physical evidence also matchAnd there is no past historyAnd is in line with seasonal diseaseTHEN provide diagnosis of the disease

HeuristicsSeasonal diseases,

Break out of disease in an area

Symptoms, physical evidences

Allergies, Side effect of existing disease

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Retain Review

Adapt

Retrieve

Database

Symptoms,Physical evidencePast History

Similar

SolutionSolution

Disease which have similar symptoms

Data base of existing symptoms

Seasonal disease,Disease outbreak

Diagnosis Flow

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Approach Assumption

New problem can be solved by retrieving similar problems adapting retrieved solutions

Similar problems have similar solutions

Interface

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Initial Problem Description

Answer to First Question

Answer to next Question

First Question

Next Question

New Case

Knowledge Base

Retrieve previous

case

Question Generation

And Ranking

DisplayQuestion Save

Question

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System Components

Retain EngineDatabaseRetrieval Engine

Similarity Matching

Index

Case-base

Reuse & Revise Engine

Profiles

Adaptationrules

Natural Language Processing Engine

Technical Architecture

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Web Server

Web Container

Integration Infrastructure

Search Web Services Adapters Content

Services

Data Sources

Enterprise Portals

Multi-Channel Access

Content Management

Business Logic

Identity Management

Content Gathering

Define template(s) to capture disease symptoms and other parameters

Physical Evidence like temperature, running nose etc. Allergies, Side effect of existing disease Heuristics Seasonal diseases, Break out of disease in an

area Data needs to be captured for all the diseases in

specified templates The data captured in template will have approval

work flow before updating the knowledge database The Approver needs to be proficient in the area of

medicine (Doctor) to review and approve the content.

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Content Storage

Once the content in Filled in Templates is approved the same will be stored in Database tables

Data base tables will be indexed for fast and efficient retrieval

Data base design will depend on the templates defined for data gathering

There will be separate set of database tables for capturing Past History of registered users

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Content Retrieval

Past history of authenticated users will automatically get added to the database.

Content retrieval involves Natural Language Processing Engine Retrieval Engine – Similarity Matching, Indexes Adaptation Engine – Ranking the retrieved data to

identify most relevant Storage Engine – Store the cases Display Engine – Display the most relevant Content User inputs can also be used for Ranking

where feedback form user can be captured and processed for ranking

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Next Steps

Deep dive of the scope current and future Basis the scope identify best approach for each

of the solution components. Components that need to be finalized are:

Stack – Java/.Net, Database – MsSQL/Oracle, App Server, Web Server (Tomcat ,Weblogic, WebSphere)

NLP Engine, Retrieval Engine, Adaptation Engine Storage Engine, Display Engine (Mostly Be-spoke)

Possible approaches can be be-spoke development, Buy cots tools, Use freeware etc.

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Incremental Services..

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Create a portal where user can upload medial history For example upload all reports, vaccinations, any

time you visit the Doc upload the prescription reports Every user will a have an account controlled by

username and password. The website can process the data uploaded and give

alerts/reminders on upcoming vaccinations/check-ups.

This will become one place see all medical history. Available online for even to view by the doc, Insurance agency, Pharma manufacturers….

Backup

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System Components

Case-base database of previous cases (experience) episodic memory

Retrieval of relevant cases index for cases in library matching most similar case(s) retrieving the solution(s) from these case(s)

Adaptation of solution alter the retrieved solution(s) to reflect differences

between new case and retrieved case(s)

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Knowledge Containers

Cases lesson to be learned context in which lesson applies

Description Language features and values of problem/solution

Retrieval Knowledge features used to index cases relative importance of features used for similarity

Adaptation Knowledge circumstances when adaptation is needed alteration to apply

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Why do we want an index?

Efficiency if similarity matching

is computationally expensive

Pre-selection of relevant cases some features of new

problem may make certain cases irrelevant . . .

despite being very similar High Low

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100

300

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Retrieval Parameters

Selection of features inducing decision tree index

Parameters to induce decision tree index

Number of best-matches retrieved by similarity

measure

Weights for features similarity matching

Thank You

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