Professor Sun Moo Kang

Department of Computer Science & Engineering

Kyung Hee University, Korea

Intelligent Medical Platform for clinical decision making

Authors: Taqdir Ali, Prof. Sungyoung Lee, Prof. Sun Moo Kang, Jaehun Bang, Dr. Muhammad Bilal Amin

August 6th 2018

/ 2Contents

I. Introduction

II. Intelligent Medical Platform

III. Case Study: Cardiovascular Silo

IV. Summary

Introduction 3

/ 4Introduction: Big Data in Healthcare

Implication -Big data usage in healthcare is rapidly growing

- Each personal will provide various kinds of health data which average hospital will store

about 665TB of data by 2015 → Big data processing technique will be essential

1. http://www.dr4ward.com/dr4ward/2013/04/what-is-the-power-of-the-big-data-in-healthcare-infographic.html2. http://www.flickr.com/photos/ibm_media/8591808129/

3. http://www.govtech.com/infographics/Infographic-Big-Data-and-Your-Health.html4. http://www.apcoforum.com/health-care-and-big-data/Source

/ 5Introduction: AI in Healthcare System

/ 6

Medical Education

Diagnostics & Treatment

Physician and Patient Assistance

Notification & Reminders

Data Analysis

FoodRecommendation

Voice Notifications

Fitness Measurement

Laboratory Test Assistance

Activity Tracking

Salient Features

Healthcare Technologies

❑ Speech Processing❑ Image Processing❑ Data Analytics❑ IoT devices Interaction❑ Rule-base Reasoning❑ Knowledge Integration❑ Body Recognition❑ Telemedicine

Existing Wellness and Healthcare Systems

Introduction: Healthcare Technologies

/ 7

Mobi leHeal thcare

Compet i tors

Introduction: Mobile Healthcare Markets

/ 8

Source: Little, L. and Briggs, P. “Ubiquitous Healthcare: Do we want it?” Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity Interaction-Volume 2, 2008

Secure Personalization Credibility

Precision Transparency Context Awareness Easy to Use

Stability

Introduction: Requirements of Healthcare

/ 9Introduction: Our Research Goal

❖ Intelligent Medical Platform (Smart CDSS)

❖ Novel Knowledge Acquisition (Data Driven + Expert Driven)

❖ Knowledge Engineering Tools(V&V, Maintenance)

❖Medical Knowledgebase Silo Construction

/ 10Introduction: Definition of CDSS

/ 11Introduction: CDSS Market Trends

https://www.grandviewresearch.com/industry-analysis/clinical-decision-support-system-market

• The global clinical decision support systems (CDSS) market size was valued at USD 471.96 million in 2016. Rising demand for quality care and integrated reliable technical solutions is one of the key trends escalating market growth.

• Healthcare Artificial Intelligence Software, Hardware, and Services Market to Reach $19.3 Billion Worldwide by 2025

https://www.tractica.com/newsroom/press-releases/healthcare-artificial-intelligence-software-hardware-and-services-market-to-reach-19-3-billion-worldwide-by-2025/

/ 12Introduction: CDSS in General

/ 13Introduction: Decision Support in Medical Platform

https://www.researchgate.net/figure/Scheme-of-the-clinical-decision-support-system-CDSS-based-u-healthcare-service-The_fig1_275836182

Clinical Decision Support System

Healthcare Center

Knowledge base

✓Knowledge at the point of care✓Apply the best evidence✓Serve as a peripheral brain - assist✓Decision making – enhance

communication✓Improve Healthcare processes and

outcomes

Seoul National University Bundang Hospital, Korea

/ 14

o Medical Knowledge is used in the wood-grain, being sure to keep the freshness, adaptability, and always will be, to be able to be present in most commercial reliability medical expert system knowledge base does not have these characteristics

Integrity

Adaptability

Freshness

Reliability

IntelligentMedical Platform

There should be no Rule (knowledge generation) is flawed— Rule should reflect the complete medical knowledge— the Rule is an actual medical environment there should be no shortage of ships to

Rule must be customized according to hospital environment - It should be customizable according to the situation of available resources (medical equipment, inspection equipment, etc.) by medical environment

Easy to update new knowledge- Each time a new treatment, prescription, or surgical procedure are

derived, the rule should be updated on these matters.

Must have sufficient credibility- Knowledge to be used should be sufficiently reliable by certified

papers, clinical trial data, or EMR inference data.

Rule DB built without sufficient medical

knowledgeNonsense

Depending on the equipment the hospital has,

the test method / treatment method /

operation method varies.

An update to a new rule

Easy to handle

It should be based on papers, clinical trial data

from pharmaceutical companies, and EMR

inference data

Medical staff directly Knowledge

be able to create and maintain

EvidenceBased

Knowledge base requirements Detail CASE Final requirements

Introduction: Requirements of Medical Knowledge Base

Intelligent Medical Platform (IMP) 15

/ 16

INFERENCE ENGINE

KNOWLEDGEBASE

CDSS

Hospitals

Caregivers

Laboratories

InsurancesPharmaceuticals

physicians

HOW TO CREATE / MAINTAIN

CREATE/UPDATE

Gets Knowledge from guidelines

Dialogue

Hard to Knowledge Maintenance

Difficult to Validate & Verification

Poor Knowledge Accuracy Issue

Difficult to know how to decision making

Games

BusinessIndustries

?

KNOWLEDGE ENGINEERING

KNOWLEDGEACQUISITION

KNOWLEDGE VALIDATION

KNOWLEDGE SHARABILITY

IMPROVE EFFICIENCY

REDUCE COST

REDUCE ERRORS

Limitations (Expert Driven)

Limitations (Data Driven)

Motivation of Intelligent Medical Platform (IMP)

/ 17

Clinical Decision Support Systems

Patient education

Clinical diagnosis

Intelligent communication

IntelligentMedical Platform

Restricted Knowledge Acquisition Methods

Difficult to Knowledge Construction by Domain Experts

Minimum level of Evidence Support

Lack of Interoperability for Heterogeneous HIS

Lack of User Interaction

Existing Medical Systems

Limitations(Data Driven +

Human Driven) Knowledge Acquisition

Support Knowledge Authoring Tools

Evidence Support from PubMed

Support UI/UX Tools

Standard based Interoperability

Approaches of IMP

/ 18

Intelligent Medical Platform

HeterogeneousInput Data

Lifelog

Knowledge base

Blood pressure device

Unstructured Text

Thyroid CancerSilo

Knowledge EngineeringTool

UI/UX Authoring Tool

Smart Watch

Sleep Monitoring

Device

Glucose Meter

Medical PACS

Patient Profile

EMR/EHRPatient Healthlog

UX Expert

Phycician

Patient

Analytics Tool

Physician

Physician

CardiovascularSilo

Head & Neck Cancer Silo

DiabetesSilo

EpilepsySilo

Lung CancerSilo

Public Health SilosEvidence Support Tool

8 Medical Services Silo

Big data Storage

Intelligent Medical Services

Intelligent Medical Platform Environments

ENT (Ear, Nose, Throat)

Silo

/ 19IMP Architecture

/ 20

Structured Data

Unstructured Data

ImageData

Multi-modal data sources

Knowledge Acquisition and Inferencing Layer

Descriptive Knowledge Acquisition

Ontology Manager

Terms Extractor

Semantic Analyzer

Text Preprocessor

Knowledge Integrator

Kn

ow

led

ge

Engi

ne

eri

ng

Too

l

Core 1

Core-1 : Component Architecture

Image-based Knowledge Acquisition

Deep Learning based Model Collation

ROI Allocation

Image Preprocessing

Defect detection

Segmentation

Medical Image Repository

Dialogue Data

Structured Knowledge Acquisition

Algorithm SelectionData Preprocessing

Rules Generator

Dialogue-based Knowledge Acquisition

Intent Manager

Sub-Dialogue Builder

Knowledge Coordinator

Input Handler

Output Handler

High Level Context Recognizer

Context Builder

Context Reasoner

Context Ontology Manager

Context Notifier

Low Level Context Recognizer

Data Router

Activity Unifier

Emotion Unifier

Context Notifier

Actionable Knowledge Evolution and Inferencing

Knowledge Controller

Knowledge Inference Engine

Knowledge Evolution Engine

RDR Rule Base

Visual documents

Structured data

Unstructured documents

voice

Rules / Patient Case

/ 21

Structured Data

ImageData

Suggested Technology

Posses Technology

Existing TechnologyCore-1 : Implementation Workflow

점진적 지식획득점진적 지식획득Image-based Knowledge Acquisition

Image Preprocessing

Deep Learning based Model Collation

ROI Allocation

2

3

Image X: CT (lung),MRI (Brain)

4

5Image E: CT (lung),

MRI (brain)

6Feature extracted

from E

Medical Image Repository

Segmentation

Image X: CT (lung), MRI (Brain)

Image X: CT (lung), MRI (Brain)

Defect detection

Data

점진적 지식획득점진적 지식획득Structured knowledge Acquisition

Data Preprocessing

Rules Generator

Classification Model

Processed Data

Rules SharingInterface

Algorithm SelectionAlgorithm Selection Model

2

Processed Data

3Processed Data

4

5b

5aAlgo.

6

Classification Model

7 Rules

점진적 지식획득점진적 지식획득Descriptive Knowledge Acquisition

Text Preprocessing

Ontology Manager

Terms Extractor Semantic Analyzer

Kn

ow

ledge

Inte

grator

2N-grams

3

Named Entities4Concept Hierarchy

5Ontology6

Changed Ontology

Dialogue

점진적 지식획득점진적 지식획득Dialogue knowledge Acquisition

Input Handler

Sub-Dialogue Builder

Intent ManagerKnowledge Coordinator

Output Hander

점진적 지식획득점진적 지식획득Actionable Knowledge Evolution & Inferencing

Knowledge Controller Knowledge Evolution Engine

Knowledge Inference Engine

(Refined:

Key, Value pair)

Mapping Key, Value

pair with rules

4

Expert7

Verified

Intervention

1

Intervention

Intervention

2 3

Intervention

Verified

Intervention

1

Image

1

점진적 지식획득점진적 지식획득Low Level Context Recognizer

Data Router

Context Notifier

Activity Unifier

Emotion Unifier

점진적 지식획득점진적 지식획득High Level Context Recognizer

Context Builder

Context Reasoner

Context Ontology Manager

Context Notifier

Expert

Kn

ow

led

ge

Engi

nee

rin

g To

ol

5

6

7

8

7

Unstructured Data

1

1

Dialogue

2Concepts 3

Ont. Models

4

Intent

5Text GenerationMulti turn Response 6

Sensory data

1

2a

2b

3b

3a 4

Sensory data

classificationclassification

Label

Label

LLC

1

2

3

Unclassified HLC

Classified HLC

SPARQLQueries

4

CW DialogueKey Value Pair

(Disease = Lung Cancer)

/

Data Analytics

22

Knowledge Authoring Studio

Rule Editor

MLM Augmented Maintenance

MLM Case Base

Executable Environment

Knowledge Engineering Tool

Ontology Manipulator

Compilation Module

MLM Generator

MLM Validator

MLM Validation Handler

MLM Augmented CBR

MLM Loading Interface

MLM Repository

Knowledge Base

MLM BrokerInference Engine

MLM Invoker

KnowledgeButton

Visualization Enabler

Data Classification

Data Transformation

Predictive Analytics

Communication Adapter

Query Formulator

Quality Recog. Model

Knowledge Extraction

Extracted RDR Rules

RDR Rules

Physician

Expert Heuristics

Verification & Validation

HMIS

Verified and Executable MLM

Executable Knowledge

StoragePatientService In

tegratio

n

Man

ager

EMR/EHR Data

Standard Format

Fetch related MLMs

Recommendation Request/Response

Evidence Request/Response

Evidence Request/Response

Knowledge Acquisition

Knowledge Execution

Core 2

Inference Controller

Descriptive Analytics

Physician/Expert

Data Acquisition and Persistence layer

Log Repository

Visualization Request/Response

Analytics Request/Response

Life log Request/Response

RDR Case Generator

DT Editor RDR Editor MLM Editor

Core-2 : Component Architecture

/ 23

점진적 지식획득점진적 지식획득Knowledge Authoring

StudioRule Editor

MLM Validator

Ontology Manipulator

MLM GeneratorCompilation Module

2

MLM Repository

점진적 지식획득점진적 지식획득Knowledge Button

Communication Adapter

Query Formulator

Quality RecognitionModel

점진적 지식획득점진적 지식획득Data Analytics

점진적 지식획득점진적 지식획득MLM Augmented Maintenance

MLM Loading InterfaceMLM Validation Handler

MLM Augmented CBRMLM Case Base

점진적 지식획득점진적 지식획득Executable Environment

Inference EngineMLM Invoker

MLM Broker

점진적 지식획득점진적 지식획득Actionable Knowledge Acquisition

1

RDR Rules

Physician

1Expert Heuristics /

V&V

KeyValue set3

4

Created

Rule6CodeConcepts5

7Generated MLM

8

Structured

MLM

9 MLM

10

11 MLM CaseMLM

Request 12 Related MLMs13Validated MLM

14

Validated

MLM

Knowledge Base

15

HMIS점진적 지식획득점진적 지식획득Service Integration Manager

8

Evidence Request

WWW

10 Key Words

11

PICO Query

12

Retrieved Articles

13

High Quality

Articles

1

Recommendation

Request / EMR

2

Standardized EMR Input

4Input

Parameters

5

Input Parameters

6

Related MLM

7

Related MLM

8

Recommendation

Evidence Request

Patient

1

1

Executable MLM

Differentiation• Semantic Reconciliation model• Automatic MLM Generation

Differentiation• MLM based knowledge

maintenance

Differentiation• Automatic Evidence Acquisition• Automatic Evidence Appraisal

Suggested Technology

PossesTechnology

ExistingTechnologyCore-2 : Implementation Workflow

Inference Controller

3

9

9

3 Recommendation

Recommendation

11

Recommendation

10

Statistical Analyzer

Visualization Enabler

Data Transformation

Trend Analyzer

Data

Classification

Retrieve

Parameters

2

3 4

4

5

Visualize/TransformClassify parameter

Analyze parameter Values

Identify trending para

meters

Visualization Request/Response

Analytics Request/Response

Uniqueness• Visualization for physician and patients• Statistical and trend analysis of lifelog

DT Editor RDR Editor

MLM Editor

/ 24

Multimodal Data Processing

Data Acquisition

Lifelog Analysis

Healthlog Monitoring

Documents Library

Security & Privacy

Big data Storage

Multimodal Data for persistence

Summarized Data for analytics

+

Smart Watch Raw sensory &

Environmental Variables

Observatory Data source(s)

ECG Glucose Level Blood Pressure

Interventional Data source(s)

ImageData

Structured Data Unstructured

Data

Multi-modal data sources

semanticallyenriched

Synchronous Interventional Data (EMR, EHR,Documents)

FHIR Enable Data Compliance

HealthlogPersistence

#2

Big Data StorageProcessing

Data Persistence

Query Writer Query Library

Passive Data Reader

Log Sync

Health log Archive

Health log Repository

Health Situation monitoring

Writing DataReading Data

Lifelog FetchActivation of

wellness service

Lifelog Accumulator

Data Acquisition & Persistence External Wellness Services

Send the request

Lifelog Repository

Core-3 : Component Architecture

/Core-3 : Implementation Workflow 25

점진적 지식획득점진적 지식획득Big Data Storage Processing

점진적 지식획득점진적 지식획득Multimodal Data Processing

Data Persistence

Query Writer

Big data Storage

점진적 지식획득점진적 지식획득Security & Privacy

점진적 지식획득Executable Environment

점진적 지식획득점진적 지식획득Knowledge Extraction

Uniqueness• soft real-time data read

from Big data storage

Query Library

2

1

37

6 5

8

8

Request to Read

& write of dataRead &

Write

request

Load the

query

Run Query

over HDFS

Retrieve streaming Data

Read

req

uest se

nd

to

Kn

ow

led

ge E

xtractio

n

Synchronous Interventional Data (EMR, EHR,Documents)

Health Situation

monitoring

Activation of wellness data

Structured /

Unstructured/

Non-textual data

Acquire Content

Asynchronous Observatory Data( semanticallyenriched )

Build Content

Index Content

Structured Data

Unstructured Data

ImageData

ECG

Glucose Level

Blood Pressure

Obse

rvato

ry D

ata

sourc

e(s

) In

terv

entional D

ata

sourc

e(s

)

Log sync

5

6

Request to write

data

Health-log

archive

Data Acquisition

2

Observatory DataRaw Data Buffer &

Synchronizer

Mu

lti-

mo

dal

Dat

a So

urc

es

+Medical Device

Manager

Interventional Data

FHIR Based API

Healthlog Monitoring

Lifelog Analysis

Mapping & Representation

FHIR Enable Data Compliance

1

Multi-modal data

(Observatory +

Interventional) for

persistence

2

Smart Watch

Raw sensory & Environmental

VariablesHealth-log

persistence

3

Req

uire

d B

atch

Data

Set

Uniqueness• Real time active health-log

monitoring

Passive Data Reader

4

Hive Metastore

9

Req

uire

d D

ata

4

Lifelog Repository

Health log Repository

Lifelog Fetch

Lifelog Accumulator

External Wellness Services

5Analysis report

/ 26

Knowledge Engineering ToolkitsClient Side

User Interface

UI IDE

UI Authoring Tools

Client Side Components

Client Side API

Feedback Monitor

Behavior Monitor

Context Monitor

UI Presenter

Client Side Caching Engine

Toolkit

Web

Ser

vice

s

Server Side

Data base

Behavior dataFeedback data Adaptation Rules User Interface Models• Task Model• Abstract Model• Concrete model

Decision Components

Adaptation Components

Adaptation Engine Trade Off Manager UIDL

Server Side Caching Engine

Feedback Evaluator Behavior Evaluator Context Evaluator

Load

AP

I

Req

ues

t A

dap

tive

UI

Physician

Core-4 : Component Architecture

/ 27Suggested Technology

PossesTechnology

ExistingTechnologyCore-4 : Implementation Workflow

점진적 지식획득점진적 지식획득Knowledge Engineering Tool

Request UI

End User

Client Side

User Interface

점진적 지식획득점진적 지식획득Client Side

UI exist incache

Client Side Cache Engine

Feedback Monitor

Context Monitor

UI Presenter

Dynamic Parameter values

Yes

3

1

4

6 Final UI7

Adaptive UI

Client-side Cache

5 Load cache2

2

Report change

Predefine parameter values

점진적 지식획득점진적 지식획득Server Side

Behavior data

Feedback data

Adaptation Rules

User Interface Models

점진적 지식획득점진적 지식획득UIDE

UI Authoring Tools

Behavior Data

feedback Data

Context Evaluator

No

UI exist incache

Server Side Cache Engine

Server-side Cache

YesLoad

cache

Final UI

Adaptation Engine

UIDL Converter

Trade Off Manager Load

Task Model, Abstract UI, Concrete UI, Feedback data, Behavior Data

Final UI

No

Evaluate

Change

DatabaseFeedback Evaluator

Behavior Evaluator

Behavior Monitor

7

89

10

11

12

13

14

15

17

20

16

18

19

21

/ 28

Service Integration Manager

Automatic Mapping Authoring

Interoperability Adapter

Mapping Authoring Mapping Evolution

Generalized Mapping Module

Customized Mapping Module

Change Detector

Change Collector

Change Formulator

EMR/Device Adapter

Query Generator Structural & Semantic

Transformation

Standardized Storage Converter

Mapping Repository

Offlin

e Pro

cessO

nlin

e Pro

cess

Standard Ontologies

Mappings

Legacy System A

Legacy System B

Mappings

Core 5

Core-5 : Component Architecture

점진적 지식획득점진적 지식획득Automatic Mapping Authoring

점진적 지식획득점진적 지식획득Interoperability Adapter

29

HMIS

LIS

Pharmacy

Pat. Charting

Data Exchange

Data Exchange WS

HL7 Exchange Service

Clinical Models

EMR/PHRMapping

Repository

Models

(Schema)Ontologies

EMR

Input

Legacy EMR

Recommendation

Concepts

Mappings

EMR Output Format

Mappings

OntologyEvolved ConceptsOntology

Evolved Mapping

Mappings

Mappings

1

2

9

8

1

2

3 4

5

Off

lin

e P

roce

ssO

n li

ne

Pro

cess

점진적 지식획득점진적 지식획득Core 2

Mapping Algorithms:▪ String Matching▪ Label Matching▪ Child Matching▪ Property Matching▪ Synonym Matching▪ Sibling Matching

Mapping Authoring

Generalized Mappings

Customized Mappings

Mapping Evolution

Change Detector

Change Collector

Change Formulator

Device Adapter

Query Generator

EMR Adapter

Input Adapters

Structural & Semantic Transformation

Standardized Storage Converter

Concepts

Output Converters

3 4

5 EMR Output Format

점진적 지식획득점진적 지식획득Core 3

6

Uniqueness• Scalable mapping repository to

store alignments • Change management mechanism

for handling the evolution of standard models

Uniqueness• Dynamic interfaces for converting

legacy formats to standardized format• Big data persistence services in a

standardized format for interoperable communication

7FHIR format

Recommendation

Core-5 : Implementation Workflow

/ 30

Knowledge Acquisition

1

Dialog Manager

Patient MRI Image Analysis

Domain Expert

Image Data

Knowledge Base

Cloud Endpoint

Data Driven

Cloud Endpoint

Cloud Endpoint

▪ It provides way of communication only

▪ Extract knowledge from users dialogue Expert Driven

2

3Existing Systems Limitation

▪ Deep learning algorithm cannot provide reason of decision making (Black box)

▪ Using white box AI techniques to create knowledge base

Existing Systems Limitation

▪ lack of verification and validation for huge number of rules.

▪ Incremental Knowledge acquisition using MCRDR

Existing Systems Limitation

SolutionSolutions

Solutions

Machine Learning

KnowledgeAuthoring

Tool

StructuredData

UnstructuredData

Provide high quality medical knowledge service

Uniqueness: Knowledge acquisition in IMP

/ 31

01

02

03

04

UI/UX Authoring

Tool

KnowledgeAuthoring

Tool

Evidence Support

Tool

DataAnalytics

Tool

▪ Incremental Learning-based validation and verification

▪ Intelli-sense support

▪ Overall UX quantification over time▪ Adaptive UI based on UX

▪ Evidence support form PubMed▪ Quality assessment retrieved

documents

▪ Real time monitoring▪ Health-log visualization

UX Expert

PhysicianPhysician

Patient

Physician

Easy to commercialize by providing development environment

Uniqueness: Engineering Tool’s Support

/ 32SaaS implementation concept

/ 33Platform over Cloud

Case Study: Cardiovascular Silo 34

/ 35Silo: One of the CDSS Services

Published Research

Expert Heuristics

Clinical Guideline

EMR/EHR

Silos

Mind Map Silo Construction Process▪ Mind map creation▪ Decision Tree Transformation▪ Plain Rule Creation▪ Implementation▪ Knowledge Execution and Evaluation

Diagnosis

Treatment

Follow-up

Silo is a structure for storing bulk materials

/ 36Motivation of Silo

Thyroid CancerSilo

CardiovascularSilo

Head & Neck Cancer Silo

DiabetesSilo

Lung CancerSilo

Public Health Silos

ENT (Ear, Nose, Throat)

Silo

EpilepsySilo

Social Needs Expected Impacts

▪ Improve quality of healthcare delivery

▪ Save Medicare cost▪ Reduce medical malpractices

▪ Quality of Medical Services

▪ Reduce Medical Cost▪ Efficiency & Safety

Solutions: Medical Services Silo

/ 37Silo Development Process

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

Knowledge Acquisition:

Knowledge Modeling:

Rule Generation:

Implementation:

Evaluation

Expert heuristics, guidelines, published research and existing systems are sources of domain knowledge

The acquired knowledge is transformed in to a formal representation such as Decision Tree

Knowledge is transformed from formal representation to computer executable format i.e. Plain Rules

A standalone executable environment is developed to execute the created knowledge for providing recommendations.

The accuracy of the system is evaluated on the real patient data of the corresponding sub medical domain

/ 38

Clinical Knowledge Model

Medical Experts

Creates Mind Map

Decision Tree

Enterprise Architect

Decision Tree Construction

Knowledge Engineer

Production Rule

Knowledge Builder

Medical Experts

MLM creation using Semantic

Reconciliation Model (SRM)

Executable Knowledge Base

Compilation

Recommendation

Knowledge Execution

MCRDR

MLM creation using SRM

Rule Creation

RDR to Production Rule Transformation

1

23

4a

5b6

7

Evidence SupportRDR Tree

Construction

1

2b

2a

Knowledge Modeling

Knowledge Execution

MLM Creation

Production Rule Creation

Production RuleGeneration

4b

5a

Storage Knowledge Creation

Knowledge Acquisition Process

/ 39Knowledge Acquisition for Cardiovascular

Physician

Cardiology Guideline

BTaking help from

published guideline

CReal practice with existing systems

APhysician experience

and heuristics

A + B + C

eCRF

/ 40Knowledge Modeling

Enterprise Architect

Medical Experts Knowledge Engineer

Mind MapDecision Tree

/ 41Initial Knowledge Rule Generation

Intelligent Knowledge Authoring Tool (I-KAT)

Medical Experts

Decision Tree Production Rules

Total Rules: 1309Total Patient Data : 300Initial Accuracy : 90%

/ 42

• Analyzed 600 patients with new set of Rules (15409) based on Modified Decision Tree• Correct Decision = 590• Incorrect Decision = 10• No Decision = 0• Overall Accuracy = 98.3%

Final Knowledge Rule Generation

/ 43Provided Data for Knowledge Modeling (for Data-Driven)

Medical Experts’ Heuristics

+

Contributing Factors:(Heart Failure Diagnosis)

• Signs & Symptoms• Breathlessness• Exercise tolerance• Tiredness• Ankle swelling• Nocturnal cough

• Clinical History• coronary artery disease (CAD)• Arterial Hypertension• Exposition to cardio toxic

drug/radiation• Use of diuretics• Orthopnea

• Physical Examination• Rales• Bilateral ankle edema• Heart murmur• Jugular venous dilatation• laterally displaced apical beat

P1

P2

P3

P4 ECG Result

P5 NT-proBNP Result

P6 BNP Result

P7 LVEF (Left Ventricular Ejection Fraction)

P8 LAVI(Left Atrial Volume Index)

P9 E/e'

P10 e‘ Septal

P11 Longitudinal strain

P12 TRV(Tricuspid Regurgitation Velocity)

P13 LVMI(Left Ventricular Mass Index)

P14 Gender

/ 44Provided Data for Knowledge Modeling (Data-Driven)

Contributing Factors:(Heart Failure Diagnosis)

Patient Dataset: 1000

EMR/HMIS

Machine Learning Algorithms

CRT with 88.55% Random Forest with 86% Decision Tree with 82%

• Naïve Bayes = 77%• Generalized Linear Model = 74%• Deep Learning = 80%

3 features out of 147 features out of 14 1 feature out of 14

Black Box ML AlgorithmsWhite Box ML Algorithms

/ 45Implementation Login Screen

Login Screen: Only authorized physicians can be logged in to the system

/ 46Implementation Dashboard

Dashboard: Shows all the patient data from EMR and EHR systems

Add New Patient

Update Existing Patient

Delete Existing Patient

Search Patient

Patient abstract

information

/ 47Implementation Patient Detail

Patient Detail Screen: Shows all detail of patient to add new patient of update existing patient

Patient Information

AndCardio

Information

Patient clinical History

Patient Symptoms

Patient Physical exam

information

/ 48Implementation of CDSS Intervention

CDSS Intervention: Shows the recommendation of a patient based on patient profile and symptomsThe decision comes from knowledge base

Final Decision

Knowledge Rule

triggered

Summary 49

/ 50Benefits – Technical Expected Effect

Domain expert can create, validate, and manage knowledge with

minimal involvement of knowledge engineers

Improved accessibility and communication among users

and system

Decrease knowledge engineers dependency

▪ SaaS based open knowledge for medical platforms

/ 51

▪ What problems?

▪ How to solve?

⚫ Intelligent Medical Platform to provide decision making services based on state-of-the-art knowledge acquisition environment.

⚫ Grand medical platform to integrate other medical services into a uniform platform and share with medical community.

⚫ Development of intelligent medical platform and construction of knowledge engineering environment, it opens the medical knowledge and shares with community

⚫ SaaS-based healthcare system

▪ What to develop?

⚫ Lack of commercialization due to difficulty in acquiring, establishing, verifying and managing knowledge base of medical experts

⚫ Knowledge acquisition dependency on knowledge engineers

Summary

Comprehensive Pediatric Epilepsy Center at Florida