kai zheng, phd, qiaozhu mei, phd, david a. hanauer, md university of michigan

2011 © University of Michigan 1

Developing an Intelligent and Socially Oriented Search Query

Recommendation Service for Facilitating Information Retrieval in

Electronic Health RecordsKai Zheng, PhD, Qiaozhu Mei, PhD, David A. Hanauer, MD

University of Michigan

- On Behalf of William Wilcox, Danny Wu, and Lei Yang

2011 © University of Michigan

Information Retrieval in EHR• Millions of patient records• Specialized language• Rich, implicit intra/inter

document structures• Deep NLP/Text Mining is

necessary• Complicated information

needs• Privacy is a big concern

2?


Problem Statement• Electronic health records (EHR), through its

capability of acquiring and storing vast volumes of data, provides great potential to help create a “rapid learning” healthcare system

• However, retrieving information from narrative documents stored in EHRs is extraordinarily challenging, e.g., due to frequent use of non-standard terminologies and acronyms

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Problem Statement (Cont.)• Similar to how Google has changed the way

people find information on the web, a Google-like, full-text search engine can be a viable solution to increasing the value of unstructured clinical narratives stored in EHRs

• However, average users are often unable to construct effective and inclusive search queries due to their lack of search expertise and/or domain knowledge

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Proposed Solution• An intelligent query recommendation service that

can be used by any EHR search engine to– Artificial Intelligence: augment human cognition so

that average users can quickly construct high quality queries in their EHR search

– Collective (social) Intelligence: engender a collaborative and participatory culture among users so that search queries can be socially formulated and refined, and search expertise can be preserved and diffused across people and domains

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A Typical IR System Architecture

query

Documents

results

QueryRep

DocRep

Ranking

INDEXING

SEARCHINGINTERFACE

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UsersFeedback

QUERY MODIFICATION


EMERSE• EMERSE - Electronic Medical Record Search Engine• Full-text search engine• Created by David Hanauer• Widely used in UMHS

since 2005 (and VA)• Boolean keyword queries• Routinely utilized by frontline clinicians, medical coding

personnel, quality officers, and researchers at the University of Michigan Health System

• The test platform for the solutions being built through this project

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Specific Aims of the Project• Aim #1: Developing AI-based Query

Recommendation Algorithms

• Aim #2: Leveraging Social Intelligence to Enhance EHR Search

• Aim #3: Defining a Flexible Service Architecture

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Aim #1: Developing AI-based Query Recommendation

Algorithms• Clinicians find great difficulty to formulate

queries to express their information needs• EMERSE provide “semi-automatic” query

suggestion (synonyms, spelling, etc.)• Example: uti uti "urinary tract infection" • 25% adoption rate! • Text mining/machine learning methods to

automatically select alternative query terms• Technical details left later in the talk

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Aim #2: Leveraging Social Intelligence to Enhance EHR

Search• Enhancing AI-based algorithms with social

intelligence:– Allow users to bundle search terms and share– Social appraisal– Classifying search terms bundles for easy retrieval– Other community features– Enhancing collaboration among user communities

across institutions

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Aim #3: Defining a Flexible Service Architecture

• A service-oriented architecture serving general search knowledge

• Locally implementable APIs• Implementation of the community features

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System Architecture


To Challenge Us – Why Bother?• Q1: Is this different from PubMed?

– EHRs have very different properties

• Q2: Is this different from Google?– Very different information needs in EHR search

• Q3: Could “social search” even work?

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Dictated Notes vs Typed Notes• Hypothesis: there exists a considerable amount of lexical

and structural differences. Such differences could have a significant impact on the performance of natural language processing tools, necessitating these two different types of documents being differentially treated

• Data: 30,000 dictated notes and 30,000 typed notes of deceased patients, randomly sampled

• Same genre: encounter notes that physicians composed to describe an outpatient encounter or to communicate with other clinicians regarding patient conditions

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Comparison: Vocabulary

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UMLS+: English dictionaries + commonly used medical terminologies + all concepts/terms in UMLS

OHSUMED: > 80% 172 64,487


Comparison: Acronym Usage

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Comparative Analysis: Perplexity

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Words repeat less

Higher perplexity/randomness

Less functional wordsSparser information!

Fewer occurrences

* Typed notes have higher variance of almost all document measures


Lessons Learned• Clinical notes are much noisier than biomedical

literature• Among them, notes typed-in by physicians are

much noisier and sparser than notes dictated.• What about different genres of notes?• These differences of linguistic properties imply

potential difficulty in natural language processing

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Analysis of EMERSE Query Log• 202,905 queries collected over 4 years• 533 users (medical professionals in UMHS)• 35,928 user sessions (sequences of queries)

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Hours of a day Days of a week (Mon - Sun)


Query Distribution – Not a Power Law!

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Long tail –but no fat head


A Categorization of EHR Search Queries

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Using the top-level concepts of SNOMED CT

Almost no navigational queries; most queries are informational/transactional


Comparison to Web Search• Almost no navigational queries (Web: ~ 30%);• Average query length (Web: 2.3):

– User typed in: 1.7– All together (typed in + query suggestions + bundles): 5.0

• Queries with Acronym: 18.9% (Web: ~5%)• Dictionary coverage: 68% (Web: 85%-90%)• Average length of session: 5.64 queries (Web: 2.8)• Query suggestions adopted: 25.9% (Web: < 10%)

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Lessons Learned• Medical search is much more challenging than Web

search– More complicated information need– Longer queries, more noise

• Users have substantial difficulty to formulate their queries– Longer search sessions– High adoption rate of system generated suggestions

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Question: Can the users help each other to formulate queries?


“Social” (Collaborative) Search in EMERSE

• Changing a search experience into a social experience• Users create search bundles (bundled query)

– Collection of keywords that are found effective as a query– Reuse search bundles– Share them with other users

• Public sharing vs. private sharing• Search knowledge diffuses from bundle creators to

bundle users

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- Zheng, Mei, Hanauer. Collaborative search in electronic health records. JAMIA 2011


Example: a Search Bundle


Share a Bundle Publically/Privately


The Effectiveness of Collaborative Search

• Search bundles (as of Dec. 2009): – 702 bundles– 58.7% of active users– Almost half of the pageviews– 19.3% of all queries (as of Dec. 2010)– 27.7% search sessions ended with a search bundle

(as of Dec. 2010)– Bundle creator: 188– Bundle sharers: 91– Bundle leechers: 77

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Example Bundles

GVHD: "GVHD” "GVH” "Graft-Versus-Host-Disease” "Graft-Versus-Host Disease” "Graft Versus Host Disease” "Graft Versus Host” "Graft-Versus-Host” "Graft vs. Host Disease” "Graft vs Host Disease” "Graft vs. Host” "Graft vs Host"

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Example Bundle (cont.)

Myocardial infarction: NSTEMI STEMI ~AMI "non-st elevation” "non st elevation” "st

elevation MI” "st elevation” "acute myocardial infarction” "myocardial infarction” "myocardial infarct” "anterior infarction” "anterolateral infarction” "inferior infarction” "lateral infarction” "anteroseptal infarction” "anterior MI” "anterolateral MI” "inferior MI” "lateral MI” "anteroseptal MI” infarcted infarction infarct infract "Q wave MI” "Q-wave MI” "Q wave” "Q-wave” "st segment depression” "t wave inversion” "t-wave inversion” "acute coronary syndrome” "non-specific ST wave abnormality” "non specific ST wave abnormality” "ST wave abnormality” "ST-wave abnormality” "CPK-MB” "CPK MB” "troponin” ~^MI -$"MI \s*\d{5}” -systemic

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Bundle Sharing Across Departments

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Bundle Sharing Across Individual Users

Red links: cross department links


Bundle Sharing Facilitated Diffusion of Information

• Quantitative network analysis of search knoweldge diffusion networks

• Giant component exists• Small world (high clustering coefficient & short paths)• Publically shared bundles better facilitates knowledge

diffusion– Privately shared bundles adds on top of public bundles

• Users tends to share bundles to people in the same department; but specialty is a more natural representation of communities. (based on modularity)

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Lessons Learned• Medical search is much more challenging than Web

search• Users have substantial difficulty to formulate their

queries– Longer search sessions– High adoption rate of system generated suggestions– High usage of search bundles

• Collaborative search has facilitated the sharing/diffusion of search knowledge– Public bundles are more effective than private– 30% bundle users are leechers; half of the bundle creators don’t

share

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Automatic Query Recommendation: Methods

• Similarity based (kNN)• Pseudo-feedback• Semantic term expansion• Network-based ranking• Learning to rank (much labeled training data

needed)

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Automatic Query Recommendation: Available

Information• Information to leverage:

– Co-occurrence within queries– Transition in query sessions– Co-occurrence within clinical documents– Annotation by ontological concepts– Ontology structures– Morphological closeness– Clickthrough

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A Network View

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Random Walk and Hitting Time

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i

kA

jP = 0.7

P = 0.3

• Hitting Time– TA: the first time that the random

walk is at a vertex in A• Mean Hitting Time

– hiA: expectation of TA given that

the walk starts from vertex i

0.3

0.7


Computing Hitting Time

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i

kA

j

TA: the first time that the random walk is at a vertex in A

}0,:min{ tAXtT tA

A ifor ,1)( Vj

Ajhjip

Aih

A ifor ,0

Iterative Computation

hiA: expectation of TA given that the

walk starting from vertex i

A i

h = 0

hiA = 0.7 hj

A + 0.3 hkA + 1

0.7

0.7

Apparently, hiA = 0 for those


Generate Query Suggestion

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uti

bacterial

D1

D2

D3…

4

2

Query

Notes/Concepts/sessions…

• Construct a (kNN) subgraph centered by the query term (s)

• Could be bipartite• Compute transition

probabilities (based on co-occurrence/similarity)

• Compute hitting time hiA

• Rank candidate queries using hi

A

urinary tract infection


Other Network-based Methods• Stationary distribution• Absorbing probability• Commute time• Other measures• More general: network regularization

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Ranking with Multiple Networks

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Ranking/Transductive Learning with Multiple Views (e.g., Zhou et al. 2007, Muthukrishnan et al. 2010)

C

BA D

A

B

D

C

A

C

BD

Distributional similarityOntology structures

Query transitions

……

Suggested Queries


Evaluation• Cranfield evaluation (adopted by TREC)

– Sample information needs queries– Fixed test document collection– Pool results of multiple candidate systems– Human annotation of relevance judgments– IR Evaluation (e.g., MAP, NDCG)

• Directly rating by users (bucket testing)

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Towards the Next Generation EHR Search Engine

• Better understanding of information needs by medical professionals – frontline clinicians, administrative personnel, and

clinical/translational researchers• Better natural language processing for patient records • Better mechanisms of automatic query recommendation

in the medical context• Better ways to facilitate collaborative search and

preserve search knowledge• Better ways to improve the comprehensibility of medical

data by patients and families (future)

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Publications to Date• Kai Zheng, Qiaozhu Mei, David A. Hanauer. Collaborative search

in electronic health records. JAMIA. 2011;18(3):282–91.

• Lei Yang, Qiaozhu Mei, Kai Zheng, David A. Hanauer. Query log analysis of an electronic health record search engine. AMIA Annual Symposium Proc. 2011. (forthcoming)

• Kai Zheng, Qiaozhu Mei, Lei Yang, Frank J. Manion, Balis UJ, David A. Hanauer. Voice-dictated versus typed-in clinician notes: Linguistic properties and the potential implications on natural language processing. AMIA Annual Symposium Proc. 2011. (forthcoming)

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Thanks!

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kai zheng, phd, qiaozhu mei, phd, david a. hanauer, md university of michigan

Documents

university of michigan

mduniversity of michigan

inclusive search queries

information retrieval

information needsemerse

ehr searchaim

average users

high quality queries