PATIENT-BASED LITERATURE RETRIEVAL AND

INTEGRATION

A Use Case for Diabetes and Arterial Hypertension

Ana Jimenez-Castellanos

1,2

, Izaskun Fernández

, David Perez-Rey

, Elisa Viejo

Francisco Javier Díez

, Xabier García de Kortazar

, Miguel García-Remesal

, Víctor Maojo

Antonio Cobo

4,2

and Francisco del Pozo

2,4

Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid

Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain

Centre for Biomedical Technology, Technical University of Madrid (CTB – UPM)

Campus de Montegancedo – UPM Autopista M-40, km 38.Pozuelo de Alarcón, 28223, Madrid, Spain

Tekniker-IK4,Av Otaola, 20, 20600 Eibar, Guipuzcoa, Spain

Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER –BBN)

Madrid, Spain

Keywords: Electronic Health Record, Search engines, Literature retrieval, Integration, Federated search.

Abstract: Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the

visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers

instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the

lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve

too many information, with publications related to patients treated by the physician beyond the scope of the

results examined. In this work we present a new method to improve scientific article search using patient

information. Two pathologies have been used within the project to retrieve relevant literature to patient data

and to be integrated with other sources. Promising results suggest the suitability of the approach,

highlighting publications dealing with patient features and facilitating literature search to physicians.

1 INTRODUCTION

Physicians attending daily a high number of patients

hamper their availability to keep up to date with the

latest research news in their field. They frequently

lack the time to locate relevant information related

to the patient Electronic Health Record (EHR).

Physicians need specific information rather than

large amounts of information, however the amount

of data generated nowadays and stored both, within

EHR and research literature, is overwhelming. The

more data the more challenging is to find relevant

information (for research and training of the

physician) and to integrate it with current resources.

Search engines and other Web 2.0 technologies such

as RSS feeds facilitate these tasks, but advanced

methods are required to retrieve research

information related to, not only a pathology, but also

a specific demographic group.

Scientific research papers regarding clinical

practice deals with population groups rather than

specific patients. Physicians must generalize patient

data in order to find patient-related clinical

literature. They should analyze the EHR of the

patient to include relevant keywords in the query

that filter the results from several information

sources. After that, the physician reviews the

scientific articles retrieved and selects the relevant

information for the patient, discarding the majority

of the results. This is particularly interesting for

patients who do not respond to therapies or

treatments, who need special attention to avoid

further complications. These kind of patients are

those for whom physicians need more specific

research information about their disease.

Jimenez-Castellanos A., Fernández I., Perez-Rey D., Viejo E., Javier Díez F., García de Kortazar X., García-Remesal M., Maojo V., Cobo A. and del

Pozo F..

PATIENT-BASED LITERATURE RETRIEVAL AND INTEGRATION - A Use Case for Diabetes and Arterial Hypertension.

DOI: 10.5220/0003132900330041

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 33-41

ISBN: 978-989-8425-34-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

In this work we present a new method,

implemented by the authors, aiming to provide an

advanced method to retrieve biomedical literature

based on EHRs. Diabetes and arterial hypertension

are the two use cases that have been tested, within

the framework of “Treatment 2.0”, a research and

development project aiming to create a generic

middleware platform that serves as the basis for the

development of management services and intelligent

application for treatment for patients, especially

chronic (Tratamiento 2.0 2010 June).

2 BACKGROUND

Patient information inclusion in search queries for

biomedical literature, requires techniques and

technologies with a high research activity. Those

used to store the required information, such as

Electronic Health Records and Decision Support

Systems, and those used to extract information, such

as Natural Language Processing and Federated

Searching described in this section.

Electronic Health Records (EHR), defined as

“electronic objects that contain data, evaluations and

information of any kind, on the status and the

clinical course of one patient through the care

process” (Healthcare Information and Management

Systems Society 2010 June), have been a

fundamental advance in clinical practice. Electronic

information can be automatically analyzed and there

exist research efforts to generate knowledge from

EHRs such as (Weaver et al. 2005), where the data

generated from care delivery and captured in the

EHR systems, is used for being analyzed to discover

and then inform about best practice. In (Antolik

2005), how to transfer knowledge from a medical

record written in a free text form into a structured

format represented by the EHR is analyzed. Finally,

(Natarajan et al. 2010) is a study aiming to

understand user needs as captured by their search

queries in an EHR system.

EHR are the essential infrastructure to other

software elements in clinical practice such as

Decision Support Systems, a kind of information

systems that supports decision-making activities

(Keen, Morton 1978). Specifically, Clinical

Decision Support Systems (CDSS) are software

programs that assist the physicians in the decision

making process (Kawamoto et al. 2005, Sim et al.

2001). Some methods to integrate CDSS and EHR

are: (van der Weijden et al. 2010) and (Seidling et

al. 2010), systems dealing with CDSS adaptation, to

facilitate the integration of individual patients

preferences and characteristics and improve decision

making.

To retrieve further clinical information, the EBM

(Evidence-Based Medicine Working Group 1992)

recommends physicians to formulate clinical

questions in terms of the problem/population,

intervention, comparison, and outcome for searching

clinical reports efficiently. These elements comprise

the PICO frame: P represents problem/population; I

intervention’s information; C the comparison; and

finally O the outcome. The construction of this kind

of query is not an easy task as it is shown in (Huang,

Lin & Demner-Fushman 2006). For instance,

defining P requires an exhaustive reading of the

patient EHR and the selection of the most relevant

characteristics for the current context.

Information extraction dealing with free text

instead of structured data is treated with Natural

Language Processing (NLP) techniques, that have

been applied to many tasks of Biomedicine such as

bio-entity recognition, protein/gene normalization,

interaction, extraction and many others. As there are

many fields where NLP techniques can be used,

various NLP tools are available. There are domain

specific tools resolving an specific task like

GoAnnotatorTool (Couto et al. 2006), but also we

can find more generic and flexible NLP tools, like

Freeling (Atserias et al. 2006) or GATE

(Cunningham 2002) which can be used for many

tasks and domains. The fundamental problem of

NLP analysis is the fact, that a particular meaning

may be expressed using different synonymous

expressions. Lexical repositories recording meaning

and lexical forms relations are frequently used in

NLP techniques for interpreting texts. MeSH (Díaz-

Galiano et al. 2008) or SNOMED (Spackman,

Reynoso 2004) are two well known lexical

repositories in the medicine domain which can be

exploited for scientific article interpretation.

Finally, heterogeneous data sources stored in

different locations require distributed or federated

methods to extract information. The federated search

paradigm was thus created, evolving in response to

the vast number of information resources. As

defined in (Jacso 2004), federated searching consists

of firstly transforming a query and broadcasting it to

a group of different sources; merging the results

obtained from the different asked sources;

presenting it in a unified format without

duplications; and providing the option of sorting the

result set. Search engines like Sphinx (Lee 1989)

help in the development of this kind of systems,

HEALTHINF 2011 - International Conference on Health Informatics

Figure 1: Patient-based literature retrieval architecture.

indexing the information and providing a fast

information retrieval.

These techniques have been used in projects such

as Parallel IE for bio-medical text mining and

indexation at Merck kGaA, Darmstadt and Medline

Analysis (Karopka et al. 2004) at Institute for

Medical Informatics and Biometry, University of

Rostock, Germany for extracting causal functional

relations on MedLine abstracts using GATE. There

are also tools like MeSHMap (Srinivasan 2001) and

MedMeSH Summarizer (Kankar et al. 2002) that

exploits MeSH ontology for document indexation

and summarization, respectively.

3 PATIENT-BASED LITERATURE

RETRIEVAL AND

INTEGRATION

The approach proposed in this paper aims to provide

an improved method to search biomedical literature

based on patient data. Data used to identify the

relevant information for literature retrieval is

collected from two sources: sensors on the patient’s

home — described elsewhere (Tratamiento 2.0 2010

June) — and data contained in the EHR of the

patient. Both sources are stored in the platform of

the project and then feed the services explained

below.

Figure 1 presents the two main phases performed

to retrieve relevant publications. Firstly, the system

should receive both data sources and locate relevant

characteristics of the patient and then looking for

and integrating the most relevant publications based

on these characteristics.

To automatically query current biomedical

literature search engines for relevant publications

based on EHRs and sensor info, a web services

architecture was proposed. Three main web services

were identified to cover the required functionality:

 patientCharacterictics, receiving a set of

parameters from the EHR and sensors to

identify the relevant characteristics.

 federatedSearch, receiving the relevant

characteristics, generates and launches queries

against different data sources.

 resultAggregation, that collect, integrates and

present the results to the user.

These web services are explained within the next

subsections, 3.1, 3.2 and 3.3 respectively.

3.1 Characteristics Extraction

The patientCharacteristics web service receives a

set of parameters in XML format from the platform

of the project. This platform stores data contained in

the patient's EHR in addition to other data resulting

from the monitoring of the patient at home — e.g.

certain data is needed in real time (like glucose

levels before and after eating). This was

implemented using sensors within the patient

environment. Figure 2 shows the structure of the

web service which extracts the characteristics of a

patient.

Two separated phases can be identified: (i)

parameter processing and (ii) curation. Within the

first phase, parameters are processed to extract the

relevant characteristics. Afterwards, during the

curation phase, an expert may evaluate the

characteristics extracted to filter those relevant for

the publication search.

PATIENT-BASED LITERATURE RETRIEVAL AND INTEGRATION - A Use Case for Diabetes and Arterial

Hypertension

Figure 2: Characteristics Extraction.

Parameters from EHR or sensors are integrated

into a single XML parameter list:

<patient_parameters>

<pathology>diabetes</pathology>

<parameters_list>

</parameter>

...

</parameters_list>

</patient_parameters>

For each parameter its name and its value are

required. The patientCharacteristics web service

extracts the significant characteristics focused on a

given pathology.

The set of parameters is not closed, the service

can receive any type of parameters, i.e. that the

application will analyze only the parameters that can

be treated according to certain rules, discarding

those that are not recognized.

After receiving the parameters, the web service

separates among structured and non-structured

parameters. Structured data are those which have a

numeric or enumerated value (E.g.: the numeric

value of the patient’s age, or the values “male” or

“female” identifying the sex of the patient). Not-

structured data are free text and they are directly

addressed to the curation phase as characteristics of

the patient.

Depending on the pathology, structured

parameters are selected for its relevance. For each

disease there is a knowledge base of rules that is

responsible for processing related parameters. Thus,

when the characteristics of a patient have to be

extracted based on a new disease, only the

corresponding rules are required. The knowledge

base of each pathology is built based on standards of

CDSSs.

Relevant data for the pathology is analyzed

following rules contained within the knowledge

bases, which are associated with the parameters.

Those rules deal with patient stratification. Most of

the rules are constructed based on expert approved

thresholds. Depending on parameters values,

characteristics are extracted and mapped to one or

several MeSH terms.

All parameters are evaluated to extract

significant characteristics related to one patient. In

the curation phase, the physician (using a simple

interface) may choose characteristics to be used to

build the search query. Relevant characteristics

selected by the physicians will be sent to the next

web service for retrieving the corresponding

literature.

HEALTHINF 2011 - International Conference on Health Informatics

Figure 3: Query Generation and Information Retrieval.

3.2 Query Generation

Search federation intends to aggregate information

units from heterogeneous sources, presenting the

results on a unified way, without duplicates and

ordered by relevance. This architecture enables a

high scalability and stability, since adding new

sources is only a matter of new connectors’

configuration, and system remains working even if

some sources are down.

So the federatedSearch web service gets as input

the XML characteristics generated by

patientCharacteristics service as context and a query

defined by the expert, which expresses the searching

topic. In order to represent efficiently those

characteristics and the free query, the service

exploits MeSH ontology combined with NLP tools,

trying to find corresponding MeSH terms both, for

the characteristics and on the input free text. The

MeSH lexicon as a flexible Gazetteer on GATE tool

has been integrated in the service. In this way MeSH

terms on any text are tagged, first parsing it with the

morphological module of GATE and then

identifying MeSH terms with the gazetteers module.

The characteristics are extracted from the patient

EHR, so it can be considered that the selected MeSH

terms define the problem (P) of the PICO query.

Intervention (I) and outcome (O) will be completed

with the identified and after selected terms on the

expert query. At this moment, the system does not

consider the comparison (C) parameter.

In order to get the federated search, the web

service queries different information sources. Each

registered expert has associated a collection of

information sources. For each new query the expert

selects a set of those sources to search. Each

information source encloses its own query format.

Before searching, it is necessary to transform the

PICO query to the each source query format. For the

transformation process, regardless of the source, the

system applies always the same methodology: taking

PICO query as input, it applies an xslt based

transformation. The xslt object is a source dependent

element where the source specific query syntax is

considered for the final query representation.

Together with query representations, the service

launches different queries, getting one result set

from each source in XML format. This procedure is

graphically represented at figure 3.

3.3 Integration and Presentation

The publication sets obtained from the previous web

service are the input of the resultAggregation

service. The aim of this service is to aggregate all

the result sets presenting to the expert the

publications as a unique collection. The final

collection should not have duplicated items and must

be sorted by relevance regardless of the source. The

web service processes the publications, tagging

MeSH terms and storing both the item itself and the

tagged MeSH terms in a MySQL database in order

to index the entire collection with Sphinx and

PATIENT-BASED LITERATURE RETRIEVAL AND INTEGRATION - A Use Case for Diabetes and Arterial

Hypertension

Figure 4: Information Aggregation.

improve the efficiency of the sorting search.

Once the collection is indexed, the service ranks

the publications based on the initial query using

Sphinx. The result is a sorted collection based on the

relevance of each publication respect the initial

query as it is shown in figure 4.

4 USE CASES: DIABETES AND

ARTERIAL HYPERTENSION

Two use cases were developed to test the proposed

method: (i) diabetes and (ii) arterial hypertension. A

knowledge base to analyze the parameters related

with them has been implemented to test the system.

Information needed to evaluate each parameter

received by the patientCharacteristics web service is

stored in different knowledge (one for each

pathology). Structured parameters were selected by

experts within the project using a focus group

methodology. These parameters are shown in table

Depending on the pathology, structured

parameters are selected by its relevance. Due to the

characteristics of the diseases, there are some

parameters that are common to both and others are

specific for each pathology (table 1).

Table 1: Structured data recognized.

Structured parameters

Common

parameters

Diabetes specific

parameters

Arterial

hypertension

specific

parameters

age

race

sex

body_mass_inde

diabetic_familiar

neuropathy

basal_glycemia

gestational_diabetes

retinopathy

nephropathy

cardiopathy

glucose_intolerance

glycated_hemoglobi

diabetic_foot

hypertensive_familia

total_cholesterol

triglycerides

systolic_pressure

diastolic_pressure

HDL

abdominal_wall_size

smoker

cLDL

Finally, for each structured parameter, the Web

Service applies several rules, extracting the related

characteristic. For example, treatment of the

glycated hemoglobin (HbA1c) and related

characteristics are:

HbA1c < 6.5 Æ "normal HbA1c"

HbA1c < 7 Æ "high HbA1c"

HbA1c < 8 Æ "high HbA1c AND glycemic

control"

HbA1c >= 8) Æ "high HbA1c AND

intensive glycemic control"

HEALTHINF 2011 - International Conference on Health Informatics

Figure 5: Interface to select the parameters and sources of

the search.

Once all the characteristics are extracted went

through the curation phase, the system constructs a

searching query with the pathology, the relevant

characteristics previously gathered and the MeSH

terms identified with the expert’s input query (with

the searching topic)

With queries close to the mentioned pathologies,

the system searches on expert defined sources using

their corresponding connectors to obtain the most

relevant publications and evidences. Nowadays,

sources covered by the system are PubMed,

Cochrane and MedScape, where the most relevant

publications about diabetes and hypertension are

published.

Finally, the results of each source are integrated,

indexed and presented to the expert as a unique

collection as shown in the following section.

5 RESULTS

Characteristics are extracted according to the

pathology and the parameters introduced. From this

output, the expert will select the most relevant

features to be launched to the search.

A preliminary test set of ten patient data and fifty

queries were used to check the functionality of the

system. The retrieved characteristics were correct for

all the cases. The results correctly identified the 90%

of the relevant papers, according to assessment of

experts of the project.

As it is shown in figure 5, the expert can choose

a more specific term for the search, i.e. optimizing

the query that will be launched against the selected

sources. The expert can also specify the number of

results obtained from the query.

With the optimized query, the system accesses

the different sources and aggregates the results,

filtering those fulfilling the initial requirements and

finally presenting them ordered by relevance, as it is

shown on figure 6.

Two filters were applied to the parameters in

order to extract the most relevant literature referred

to a patient: (i) a transformation of the parameters

into Mesh terms and (ii) an optimization of the

characteristics, using the MeSH ontology and the

medical knowledge of the expert. Through the final

query and the federated search the expert will

retrieve biomedical literature based on patient data.

6 CONCLUSIONS AND FUTURE

LINES

In this work, a new method for search clinical

literature has been proposed and implemented within

the framework of a research project for the

personalization of treatments and therapeutic

strategies. Promising results of the system suggest

that EHR-based literature retrieval may facilitate the

work of physicians obtaining biomedical patient-

related research publications. Also, within the

project, a set of tests with real patients and experts

from the “Hospital Universitario de Valencia” will

be conducted.

As work in progress, extensions of services

described within this paper are being implemented:

further non-structured data processing, clustering

techniques to group the results, open of generic

connectors and source weighting for final ranking.

First, the service will use Natural Language

Processing techniques to process non-structured data

of the patient for characteristic extraction.

Clustering techniques are being used to present

results not only as a sorted collection but also

grouped by their semantic similarity, providing a

more intuitive representation for the navigation.

Regarding information sources, connector

designing process is being generalized to facilitate

new source integration within the system. This

PATIENT-BASED LITERATURE RETRIEVAL AND INTEGRATION - A Use Case for Diabetes and Arterial

Hypertension

Figure 6: Aggregated Results.

service will add a weighting functionality for

sources, to adjust the final publication relevance not

only based on the initial query but also considering

the relevance of the source.

The system presented in this work has proven the

suitability of a patient-based literature retrieval

approach. And we are confident that current

extensions will improve the location of relevant

publications for physicians, facilitating more

relevant results when searching at main biomedical

literature search engines.

ACKNOWLEDGEMENTS

The present work has been funded by the Spanish

Ministry of Industry, Tourism and Trade in the

framework of the "Avanza I+D" programme under

the project “Tratamiento 2.0” (project number TSI-

020301-2008-15). We would like to express our

gratitude to the “Hospital Universitario de Valencia”

for their collaboration in the election of the

parameters used in this work.

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Hypertension