Knowledge Analytics Synergy in Clinical Decision Support · 1 Clinical Genomics IT 1 cli-g ® Knowledge Analytics Synergy in Clinical Decision Support MIE 2012 Noam Slonim, Boaz Carmeli,

1

Clinical Genomics IT 1

cli-g ®

Knowledge Analytics Synergy in Clinical Decision Support

MIE 2012

Noam Slonim, Boaz Carmeli, Abigail Goldsteen, Oliver Keller, Carmel Kent, Ruty Rinott

IBM Haifa Research Labs

Machine Learning and Data Mining (MLDM) group

Cli-G group

2


cli-g

Introduction

Ø Clinical Decision Support (CDS) have great poten6al o  Improving clinical care o  Reducing associated costs

Ø Most exis6ng tools are knowledge-‐based CDS o  Clinical trials à Clinical guidelines à simple CDS rules o  Well established

Ø Emerging alterna6ve paradigm – analy6cs-‐based CDS o  EHR data à Analy6cs (Machine Learning) à Sta6s6cally-‐based CDS o  No need to define/maintain long list of rules o  Allows to take new “omic” data into account quickly o  Address groups not par6cipa6ng in clinical trials (co-‐morbidi6es, elderly,

etc.)

Rapid pulse, sweating, shallow breathing. According to the

computer, you’ve got gallstones

3


cli-g

Both paradigms are complementary

Knowledge

Data

(EHR)

Decision

Support

Interface

Ø Analy6cs techniques may benefit from domain knowledge

Ø Domain knowledge may benefit from analy6cs

Ø CDS knowledge-‐Analy6cs Synergy (KAS) paradigm

Analytics

4


cli-g

The proposed CDS KAS Paradigm

Ø Synergis6cally combine relevant clinical knowledge with analy6cs applied to EHR data, improving overall CDS quality

Ø Knowledge and analy6cs components mutually feed and enhance each other to achieve bePer results.

Ø Implemented as part of a generic CDS system – Cli-‐G Ø  Cli-‐G underlying architecture explicitly supports the KAS paradigm

Ø  See also Evicase presenta6on (Wed. 1000, Fermi)

5


cli-g

Data examined to demonstrate KAS potential Ø  3 cohorts of hypertensive pa6ents:*

Ø  EPOGH Ø 1,149 pa6ents Ø 15 clinical features

Ø  IMA_Sardinia Ø 278 pa6ents Ø 45 clinical features Ø Genotype data for 1M SNPs

Ø  Immidiet Ø 258 pa6ents Ø 45 clinical features Ø Genotype data for 1M SNPs

* http://www.hypergenes.eu/

6


cli-g

The Knowledge-Management (KM) component Ø  Represents and streams relevant clinical knowledge to other components.

Ø  Declara6ve Knowledge (DK) Ø  Factual informa6on:

hierarchy of concepts with proper6es and rela6ons

Ø  Procedural Knowledge (PK) Ø  The knowledge on how to operate upon the DK concepts

Ø Clinical Guidelines Ø Rules defining Deduc6ve Concepts to be added to the DK model; e.g.,

Ø Risk Group based on Gender, Weight, Co-‐morbidi6es, etc

Ø  May support various CDS applica6ons Ø  Guidelines based treatment recommenda6ons

Ø  Rule-‐based alerts for adverse-‐drug events

Blood Pressure

Sitting Standing Laying

Has property

night day

Has property

7


cli-g

The Analytics component Ø  Encompasses an arsenal of ML algorithms for various CDS tasks

Ø  Feature ranking/selec6on Ø  Automa6cally highlight clinical features of poten6al interest to the clinician

Ø  Es6ma6ng pa6ent similarity Ø  Automa6cally reveal meaningful groups of similar pa6ents à

recommenda6ons at Point Of Care

Ø  Supervised Machine Learning algorithms Ø  Predic6ng most common treatment (IHI 2012) Ø  Predic6ng the outcome of candidate treatment (EuResist) Ø More…

8


cli-g

KAS: using KM component to enhance Analytics Ø  Procedural Knowledge rules for Data Cleansing:

Ø Outliers / non-‐valid data removal Ø  Remove Height measurements < 140 Ø  Remove Serum Cretanine > 2

Ø Unifying data reported in different scales Ø  Serum Insulin: μIU/mL or pmoI/L

Ø Quan6zing con6nuous data Ø  Systolic Blood Pressure:

Ø < 120 à Normal Ø 120-‐139 à Pre Hypertension Ø 140-‐159 à Stage-‐1 Ø > 160 à Stage-‐2

9


cli-g

KAS: using KM component to enhance Analytics Ø  Data enrichment

Ø Use domain knowledge to pinpoint relevant feature combina6ons to explore Ø BMI Ø [Serum-‐Glucose*Serum-‐Insulin]/25 à HOMAI Ø More...

Ø  Some Deduc6ve Concepts were later iden6fied by feature-‐ranking as associated with clinicians treatment decision; Ø  BMI in the EPOGH cohort à insights regarding treatment-‐selec6on

process*

* Rinott et. al IHI 2011

Trea

tmen

t Pro

babi

lity

Underweight Obese

10


cli-g

KAS: using KM component to enhance Analytics Ø  Data filtering

Ø Use domain knowledge to filter irrelevant features, focusing analy6cs on important features

Ø Find similar pa6ents based on gene6c SNP data

Ø Using all 1M SNPs available Ø Time costly Ø Low signal to noise ra6o

Ø Pre-‐select 5,717 SNPs according to domain knowledge -‐ poten6ally related to molecular pathways associated with hypertension Ø Reduce run 6me by more than 2 orders Ø Clustering pa6ent based on similarity results in meaningful clusters

11


cli-g

KAS: using Analytics to enhance KM component Ø  Enhancing Declara2ve Model

Ø  EHR ooen includes free-‐text data Ø  Analy6cs may reveal the importance of a par6cular free-‐text term; e.g.,

Ø Drug-‐name à added to the DK Model

Ø  Deduc6ve Concepts Ø  Exploring all feature pairs à Reveal pairs with synergis6c predic6ve power

à TBA as Deduc6ve Concepts to the DK model Ø  Waist circumference & Gender has synergis6c predic6ve power for selected

treatment in EPOGH data

12


cli-g

KAS: using Analytics to enhance KM component Ø  Enhancing Procedural Knowledge

Ø  Reveal clinicians “common prac6ces” à TBA to the PK model à suppor6ng rule-‐based CDS applica6ons

Ø  Feature ranking applied to iden6fy features associated with treatment selec6on

Ø  Gender found to be significantly associated with treatment selec6on Ø  Gender highlighted in PK model as related to “Common Prac6ces” in

“Treatment Selec6on”

Ø  Guidelines refinement Ø  Pinpoint SNPs of poten6al clinical value to enhance PK and DK à

guidelines refinement (Not implemented yet)

Female Male

13


cli-g

Summary

Ø  Presented the Knowledge Analy6cs Synergy paradigm for CDS Ø  Demonstrated via a generic tool (Cli-‐G) that relies on the KAS Architecture

Ø  Presented concrete examples over clinical and genomic data:

Ø  Knowledge can enhance Analy6cs

Ø  Data-‐driven analy6cs can enhance Knowledge

Ø  The synergisy6c approach holds great poten6al for CDS tools

14


cli-g

Thank You

15


cli-g

Introduction Ø Clinical Decision Support (CDS) have great poten6al

o  Improving clinical care o  Reducing associated costs

Ø Most exis6ng tools are knowledge-‐based CDS o  Clinical trials à Clinical guidelines à simple CDS rules

Ø Emerging alterna6ve paradigm – analy2cs-‐based CDS o  EHR data à Analy6cs (Machine Learning) à Sta6s6cally-‐based CDS rules

16


cli-g

Results – Analytics enhancement – Data Filtering Ø  Pa6ents were clustered using Pa6ent Similarity defined according to SNP data

Ø Emphasizing rela6ve similarity of pa6ents sharing iden6cal rare SNPs

Ø  Similarity es6mated from 5,717 SNPs (from 1M) selected by domain knowledge Ø Filtering reduced computa6on 6me by more than two orders of magnitude Ø Filtering focused analysis in advance on poten6ally most relevant SNPs

Ø  Sta6s6cal significance of clinical features was es6mated in each cluster Ø c10 (30 people)

Ø Avg. Systolic BP: 153, vs. 140 in remaining popula6on (P-‐val 0.004) Ø c5 (23 people)

Ø Avg. Triglyceride: 95.3, vs. 132 in remaining popula6on (P-‐val 0.0002) Ø More…

Ø  à Encouraging results that should be further explored …

17


cli-g

Both paradigms are potentially complementary

Ø Analy6cs-‐based Insights extracted from EHR data may bePer reflect the relevant popula6on

Ø Analy6cs-‐based CDS require no need to define/maintain long list of rules

Ø Inherently stochas6c sta6s6cal signals may bePer reflect clinical world than determinis6c rules

Ø Being blind to the relevant clinical knowledge is obviously hazardous Ø  Contraindica6ons


CDS knowledge-‐Analy6cs Synergy (KAS) paradigm

18


cli-g

Results – Knowledge enhancement Ø  Searched for feature-‐pairs with synergis6c predic6ve power for selected

treatment in EPOGH data Ø  Several pairs were detected as significantly associated with treatment

alloca6on: Ø Waist circumference & Gender à added to DK model as Deduc6ve Concepts

19


cli-g

Results – Knowledge enhancement Ø  Feature ranking applied to iden6fy features associated with treatment alloca6on

Ø  Gender found to be significantly associated with treatment selec6on Ø  à Gender highlighted in PK model as

Ø  related to “Common Prac6ces” in “Treatment Selec6on” Ø  Relevant reference exist

Ø  à Accompany analy6cs-‐based evidence with pointer to relevant literature…?

Female Male

20


cli-g

Both paradigms are complementary

Ø Being blind to the relevant clinical knowledge is obviously hazardous Ø  Contraindica6ons Ø  Rare condi6ons


Ø Analy6cs-‐based insights may highlight important features not taken into account by current guidelines

Ø Analy6cs-‐based Insights extracted from EHR data may bePer reflect popula6on Ø  Address groups not par6cipa6ng in clinical trials (co-‐morbidi6es, elderly, etc.) Ø  Consider new gene6c informa6on

Ø Domain knowledge may benefit from analy6cs

CDS knowledge-‐Analy6cs Synergy (KAS) paradigm

21


cli-g

KAS CDS High level architecture

Data

Integration EHR

Knowledge Management

Analytics

Decision Support

Interface

Patient Similarity

Feature Ranking

Unsupervised Learning Supervised

Learning More..

Clinical Guidelines

Best Practices

Domain Knowledge

More…

EHR EHR

Documents

Knowledge Analytics Synergy in Clinical Decision Support · 1 Clinical Genomics IT 1 cli-g ® Knowledge Analytics Synergy in Clinical Decision Support MIE 2012 Noam Slonim, Boaz Carmeli,