Identification of relations between risk factors and their pathologies or health conditions by mining scientific literature.

Abstract

Risk factors discovery and prevention is an active research field within the biomedical domain. Despite abundant existing information on risk factors, as found in bibliographical databases or on several websites, accessing this information may be difficult. Methods from Natural Language Processing and Information Extraction can be helpful to access it more easily. Specifically, we show a procedure for analyzing massive amounts of scientific literature and for detecting linguistically marked associations between pathologies and risk factors. This approach allowed us to extract over 22,000 risk factors and associated pathologies. The performed evaluations pointed out that (1) over 88% of risk factors for coronary heart disease are correct, (2) associated pathologies, when they could be compared to MeSH indexing, are correct in about 70%, and (3) in existing terminologies links between risk factors and their pathologies are seldom recorded.

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Identification of relations between risk factors and their pathologies or health conditions
by mining scientific literature
Thierry Hamona, Martin Grañab, Víctor Raggiob, Natalia Grabarc,d, Hugo Nayab
a LIMBIO (EA3969), UFR SMBH Léonard de Vinci, Université Paris 13, 93017 Bobigny France
b Unidad de Bioinformática, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
c Centre de Recherche des Cordeliers, Université Pierre et Marie Curie - Paris6, UMRS 872, Paris, F-75006; Université Paris
Descartes, UMRS 872, Paris, F-75006; INSERM, U872, Paris, F-75006 France
d HEGP AP-HP, Paris, F-75015 France


Abstract
Risk factors discovery and prevention is an active research
field within the biomedical domain. Despite abundant existing
information on risk factors, as found in bibliographical data-
bases or on several websites, accessing this information may
be difficult. Methods from Natural Language Processing and
Information Extraction can be helpful to access it more easily.
Specifically, we show a procedure for analyzing massive
amounts of scientific literature and for detecting linguistically
marked associations between pathologies and risk factors.
This approach allowed us to extract over 22,000 risk factors
and associated pathologies. The performed evaluations
pointed out that (1) over 88% of risk factors for coronary
heart disease are correct, (2) associated pathologies, when
they could be compared to MeSH indexing, are correct in
about 70%, and (3) in existing terminologies links between
risk factors and their pathologies are seldom recorded.
Keywords:
Natural language processing, Semantics, Medical informatics,
Risk factors, Public health informatics, Terminology.
Introduction
Risk factors jointly refer to behaviors, environmental condi-
tions, diseases or genetic backgrounds – considered in an am-
ple way – that actually increase people’s chance of manifesting
a given disease. Discovering risk factors to design prevention
strategies is an important biomedical challenge. Indeed, it is an
active research field, with major contributors coming from
biology, epidemiology and public health programs. Within
these programs, research proposes statistical studies of large
populations in order to reveal risk factors for many health
conditions and pathologies (i.e. cardiovascular diseases [1, 2],
hypertension [3], atherosclerosis [4, 5], cancers [6, 7], mortal-
ity within older people population [8-10], to cite but a few).
Despite this extensive activity, traditional risk factors for co-
ronary heart disease for instance actually account for only 50%
of the risk [11]. For biomedical professionals, this research
assumes pushing back the frontiers of medicine and biology.
Learning about risk factors not only benefits preventive health-
care; it may directly impact on patients. Indeed, accumulated
and organized clinical knowledge may help counteracting dis-
ease progression, hence improving life quality in patients. The
last few years have seen the proliferation of websites intended
to centralize and organize widely scattered medical and health-
care information. A major concern of health information col-
lected from the Internet, is the very quality of the data [12-
14]. The Medline database [15] provides biomedical research-
ers/practitioners with instant access to extensive and high qual-
ity scientific literature. Yet, the huge amount of indexed peer-
reviewed articles, currently over 18 million, hinders reliably
sampling and selecting all relevant papers. Our central goal is
to exploit the Medline repository and to extract risk factors
and their associations to health conditions.
Background
Currently, genetic and medical information in bioscience pa-
pers is often exploited in a manual, expert fashion. Hence,
increasing attention is being paid to the automation of natural
language processing (NLP) and information extraction (IE) in
biological and medical realms, as testified by the BioCreA-
tIvE1 and TREC Genomics2 challenges dedicated to extraction
of gene and protein names, their interactions and functions
from biomedical literature; or by I2B2 challenges3 dedicated
to extraction of various kinds of information (i.e., smoking
status, signs and symptoms, medication) from clinical records.
However, to our knowledge, little work has focused on risk
factor extraction.
As a matter of fact, existing approaches for risk factors study
are mainly issued from the data mining area. Thus, an impor-
tant issue faced by these works is the large number of variables
to be considered [10]: indeed, current state-of-the-art statistical
algorithms are often unable to manage them. For example, data

1 biocreative.sourceforge.net
2 ir.ohsu.edu/genomics
3 www.i2b2.org/NLP
MEDINFO 2010
C. Safran et al. (Eds.)
IOS Press, 2010
© 2010 IMIA and SAHIA. All rights reserved.
doi:10.3233/978-1-60750-588-4-964
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mining methods have been applied to ICD-9 codes to predict
groups of people with similar risk factors [16], or to data from
insurance companies to get estimators of claim costs [17]. Let
us mention also a KDD challenge4 held in 2004 which ad-
dressed data mining in biomedicine, albeit confined to social
subgroups of patients, the main goal being to identify athero-
sclerosis risk factors (and their combinations) and to monitor
the evolution of these risk factors as well as their impacts [18].
A rare work related to the processing of narrative biomedical
literature [19] combines manual and automatic meta-analysis-
like methods for extracting facts on risk factors related to
breast cancer. Findings are consistent with published studies:
there is a positive association with alcohol consumption, but a
negative association with former smoking.
In this paper, we attempt taking a further step from the above-
mentioned approaches. Our objectives are massive extraction
of risk factors from available literature and establishing their
relation(s) to the corresponding pathologies. Also, this will
allow providing comprehensive information on risk factors,
which seems to be a missing resource currently.
Material and Methods
Material
Bibliographical database Medline
Our rough working material is Medline database, which cur-
rently hosts over 18 million citations - mainly from life sci-
ences and biomedical research. We exploit the totality of these
data, focusing on titles, abstracts and MeSH indexing within
each citation.
Bibliographical database Medline
MeSH [20] thesaurus has been created for information storage
and retrieval, and is currently used for indexing Medline data-
base. Its descriptors are organized in a hierarchical structure,
whose most general level are very broad headings such as A
Anatomy, C Diseases, L Information Science, F Psychiatry
and Psychology, etc., with 16 such headings in total. Within
MeSH, we exploit headings and their notations, and thus can
rely on their semantic types.
Snomed CT
Snomed CT [21] is another terminological resource of the
biomedical area. The goal of the Snomed CT nomenclature is
to provide a conceptual basis for organizing and, more particu-
larly, for exchanging clinical data. This is a multiaxial termi-
nology. Its terms are organized within 15 hierarchies, such as:
Clinical finding/disorder, Procedure/intervention, Environ-
ment or geographical location, Social context, Organism,
Substance, Pharmaceutical/biologic product, etc. Snomed CT
terms are structured within a dense network of semantic rela-
tionships belonging to synonymy, hierarchy, or transversal
relationchips. Among this last category, we distinguished three
more precise relationships, which in our opinion may be linked
with the risk factor notion: has causative agent, due to and

4 lisp.vse.cz/challenge
associated with. Thus, associated with represents a clini-
cally relevant association between concepts without either
asserting or excluding a causal or sequential relationship be-
tween the two, while due to is used to relate a clinical finding
directly to its cause. In this example from Snomed CT: acute
pancreatitis due to infection is a acute pancreatitis due to
infectious disease, infectious disease belongs to the causative
agent axis and allows to identify a direct cause of a disease.
Other Snomed CT examples state that: (1) bacterial endocar-
ditis has causative agent bacterium; and that fentanyl al-
lergy has causative agent fentanyl. Within Snomed CT we
exploit these three relationships which, as far as we know, may
correspond to the only resource providing an explicit and con-
trolled information on risk factors and associated health condi-
tions.
Methods
Bibliographical database Medline
The aim of the preprocessing step is to annotate Medline cita-
tions with linguistic information and to prepare the step of
information extraction task (identification of risk factors, of
pathologies and of relations between them). We use the Og-
mios platform [22], suitable for the processing of large
amounts of data and tunable to specialized areas. The follow-
ing tasks are performed through the platform: segmentation
into words and sentences, POS-tagging and lemmatization
with the Genia tagger [23], term extraction and recognition5.
These tasks, and especially the term extraction task, are per-
formed in order to correctly tokenize and identify textual units
relevant to the searched information. Thus, the next step of
information extraction is performed on POS-tagged, lemma-
tized and term-tagged text.
Information extraction
The proposed method is designed for accessing several types
of information within Medline citations. First, we aim at de-
tecting and extracting risk factors, and as a matter of fact, this
implies dealing with specific syntactic structures, such as co-
ordination and enumeration. In the following examples, risk
factors are underlined, related pathologies and health condi-
tions are in bold, and pattern-related elements in normal font
letters:
1. Risk factors for survival were age and severity of aortic
stenosis ... (PMID 8705769)
2. ...a high intake of calcium and phosphorus is a risk fac-
tor for the development of metabolic acidosis. (PMID
1435825)
3. ...had more than one of the common risk factors for ce-
rebrovascular accidents, including hypertension, ad-
vanced age, hyperfibrinogenemia, diabetes mellitus, and
past history of cerebrovascular accident. (PMID
1560589)
We process such syntactic structures with specifically de-
signed syntactic patterns, where the trigger is a mention of risk

5 search.cpan.org/~thhamon/Lingua-YaTeA
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factor and the enumeration sequence (punctuation and coordi-
nation conjunctions).
Another aspect of the information extraction task consists into
establishing an explicit link between risk factors and the corre-
sponding pathologies and health conditions. We propose to
use two sources for accessing such information:
• Information extracted from abstracts and titles. In this
case, this information is accessed through another set of
dedicated lexico-syntactic patterns, such as risk factors for
in the previous examples.
• MeSH descriptors provided by the Medline indexing. In
this case, we analyze all the descriptors associated to a giv-
en citation, we then match them to the MeSH thesaurus and
select only those which belong to the heading of diseases
C.
Evaluation
We have to evaluate various aspects of the obtained results.
Our main concern is the quality of extracted information for
both risk factors and associated pathologies. We propose to
tackle this as follows: (1) For a given pathology, we evaluate
quality and exhaustiveness of the extracted risk factors. (2) As,
for certain risk factors, we have at our disposal the associated
pathologies provided by two sources (information extraction
and MeSH indexing), we propose to compare these two pa-
thology-related data. (3) Finally, we take advantage of the
causal and associative relations encoded within Snomed CT
and compare them with the information extracted from Med-
line titles and abstracts. We compute the precision, i.e. ratio of
correct extractions among all the results relevant to a given
evaluation. All these evaluations are performed manually, as
no dedicated and comprehensive gold standard is available.
Results and Discussion
Building and preparing the material
Within the Medline database, we selected those citations
which contain singular or plural forms of the terms risk factor
and factor of risk in abstracts or titles. This allows to reduce
the whole Medline material to a reliable and homogenous sub-
set of citations. The resulting corpus contains 187,544 cita-
tions (over 42 M word occurrences). This corpus of Medline
citations have been processed through the Ogmios platform.
Snomed CT tables were accessed through the UMLS resource
[24] version 2008AB, and we could extract 154,130 pairs of
registered relations between a pathology and its causative
agent or another pathology. 92,807 of these relations are pro-
vided by has causative agent, 25,309 by due to and 36,134
by associated with relationships. 120 of these relations are
provided by more then one relationship within Snomed CT.
Extraction of information on risk factors and pathologies
Patterns for information extraction from abstracts and titles
were built manually on few positive examples, and then gener-
alized and applied on the whole set of data. We distinguish
three kinds of patterns (in the examples below, <NP-RF> indi-
cates noun phrases corresponding to risk factors, <NP-P> to
pathologies, ? and * for optional and recurrent elements):
1. Patterns for extracting risk factors and pathologies (n=5).
This example of pattern: <NP-RF> as a risk factor for
<NP-P> allows to discriminate this sentence: Hypocal-
cemia at parturition as a risk factor for left displacement
of the abomasum in dairy cows, and to propose that hy-
pocalcemia is a risk factor of displacement of the abo-
masum.
2. Patterns for extracting risk factors (n=12), among which
we have a pattern for enumeration: (other)? risk factors?
including <NP-RF>(, <NP-RF>)* ((, )? and <NP-RF>)? .
From the sentence The relationships between impaired
fasting glucose, other risk factors including blood pres-
sure, and mortality have never been clearly investigated
it extracts that blood pressure and mortality are risk fac-
tors.
3. Patterns for extracting pathologies (n=3), among which
(potential)? risk factors? for <NP-P>, which detects
in the sentence A risk factor for poor pregnancy out-
come, a population-based screening study the health
condition poor pregnancy outcome.
In order to get complete information on associations between
risk factors and pathologies, elements extracted by patterns (2)
and (3) are combined. In this way, we assume that each cita-
tion corresponds to a semantically coherent unit and that ele-
ments from its different sentences are strongly related between
them. These patterns allowed us to extract information from
10,445 PMIDs. Pattern (1) extracted 313 pairs {risk factor;
pathology}. Combination of patterns (2) and (3) provided
15,398 pairs more. Finally, 5,873 risk factors extracted by
pattern (2) could not be associated with any pathology within
abstract or title. MeSH indexing was analyzed and allowed us
to extract 5,106 different pathologies and health conditions
within the axis C related to diseases. Exploitation of the pro-
posed approach generates triplets {risk factor, pathologytext,
pathologyMeSH}, where the first element is always informed,
the two other elements may remain empty.
We extracted 21,584 triplets, among which 17,620 pairs
(14,895 of which are unique) are provided only by information
extraction patterns, while 5,717 pairs (4,412 of which are
unique) contain MeSH descriptors as pathology.
Evaluation
Analysis of the extracted risk factors for coronary heart dis-
ease
Coronary heart disease (CHD) is the most common form of
disease affecting the heart and is an important cause of prema-
ture death all around the world. A medical doctor performed a
qualitative evaluation of results on this disease. This evalua-
tion appears to be encouraging: among 1,102 extractions, only
128 (11.62%) are rejected, while the remaining set is consid-
ered to provide helpful information. First of all, well known
risk factors (such as hypertension, smoking, diabetes, age,
obesity, hypercholesterolemia, hyperlipidemia, family history)
are frequently detected in the literature and extracted. Amus-
ingly, work was detected to be a risk factor for CHD in the
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following sentence: Passive smoking at work as a risk factor
for coronary heart disease in Chinese women who have never
smoked. Obviously, this is an error due to an insufficient anal-
ysis of syntactic dependencies (the right risk factor is passive
smoking). Such chunking and segmentation problems at sen-
tence, term or word levels can appear but correspond usually
to a minor problem. Another positive aspect of the method is
that for several risk factors, it detects also synonyms: {smok-
ing; cigarette smoking; smoking history; importance of total
life consumption of cigarettes}, {hyperhomocysteinemia; hy-
perhomocysteinaemia; homocysteine; plasma homocysteine}.
Comparison between MeSH-indexed and extracted patholo-
gies
In 291 cases, our approach generated a complete triplet {risk
factor, pathologytext, pathologyMeSH}. In order to evaluate pre-
cision of the extracted pathologies, we propose to compare
them with pathologies provided by MeSH indexing. This eval-
uation has been performed by a computer scientist and pointed
out the following cases (in the given examples, pathologies are
given in this order {pathologytext; pathologyMeSH}): 42 ex-
tracted pathologies are identical to MeSH indexing, 32 are
their synonyms ({breast cancer; breast neoplasms}, {coro-
nary artery disease; coronary disease}, {cataractogenesis;
cataract}, {postsurgical pain; pain, postoperative}), 28 are
lexically included ({alzheimer; alzheimer disease}, {unsus-
pected anaphylaxis; anaphylaxis}, {hemorrhagic stroke;
stroke}, {wound infection; surgical wound infection}), 101
have a close semantic relation ({poor pregnancy outcome;
fetal growth retardation}, {development of alcohol disorders;
alcoholism}, {stroke; cerebrovascular disorders}, {osteopo-
rosis; bone diseases, metabolic}, {central retinal vein occlu-
sion; vision disorders}), 7 have a broad semantic relation
({tardive dyskinesia; dyskinesia, drug-induced}, {squamous
cell carcinoma of the skin; carcinoma, squamous cell}) and
91 are not related semantically. Thus, among the set of 291
generated triplets, only 91 extracted pathologies (31%) ap-
pears to be not relevant, while nearly 70% are identical, or
have a close or broad semantic relation with the MeSH-
indexed pathologies.
Comparison of extracted risk factors with three Snomed CT
associative relations
The evaluation question is whether relations extracted from
abstracts and titles are already registered within Snomed CT
and related through three causative relationships: has causa-
tive agent, due to and associated with. In order to analyze
this aspect, we looked for those MeSH-indexed pathologies
which are also involved in these three Snomed CT relation-
ships. This evaluation is performed by a computer scientist.
We obtain a set of 22,730 propositions, related to 168 various
pathologies. We analyzed 20 pathologies (3,100 extractions,
about 25% of the whole set), such as: acquired immunodefi-
ciency syndrome, kidney diseases, heart diseases, alcoholic
intoxication, epilepsy, and cytomegalovirus infections. Only
19 extractions (0.6%) were considered as already recorded
within Snomed CT or very close to the recorded relations. For
instance, within the sentence:
...how patients with abundant alcohol consumption as a
risk factor develop the chronic alcohol abuse episode of
care... (PMID 10414608)
abundant alcohol consumption was extracted as risk factor for
alcoholism C0001973, which is very close to the relation reg-
istered in Snomed CT: drinking alcohol (C0589068) has
causative agent alcoholism (C0001973). Other comparable
extractions: {asbestos fibres
(C0003949)} in Snomed CT, while in citations we obtain {as-
bestos exposure; asbestosis (C0003949)}; or {cytomegalovi-
rus group
(C0010825);
(C0010823)} in Snomed CT and {cytomegalovirus; cytomega-
lovirus infections (C0010823)} in text. Remaining extractions
share little common aspects with the analyzed Snomed CT
associative relations. One reason is that the precision of these
extractions is not perfect; but a more specific reason is that
Snomed CT does not specifically record this type of informa-
tion, although risk factors may occur among the Snomed CT
relations. Thus, for acquired immunodeficiency syndrome we
extracted several risk factors, i.e.:
bisexuality (C0005639), bisexual (C0178515),
blood transfusion (C0005841),
intravenous drug abuse (C0086181), ...
which are Snomed CT concepts but have no associative rela-
tions to acquired immunodeficiency syndrome. The situation is
similar with other illnesses: family history, age, race, smoking,
hyperlipidemia, diabetes, hypertension, sedentary life style,
weight control, stress and many others are extracted as risk
factors for heart diseases but are not recorded as such in
Snomed CT. This implies, not surprisingly, that the creation
and maintenance of specific dedicated and comprehensive
resource for risk factors, would be most welcome.
(C0003947);
asbestosis
cytomegalovirus infections
Conclusion
We presented an experience in extracting information linked
to risk factors from Medline citations. The proposed approach
relies on NLP and IE methods and is based on lexico-syntactic
patterns. It allows extracting risk factors and the associated
pathologies. We perform several types of evaluation by medi-
cal and biological experts and computer scientists. Evaluation
of the precision of risk factors extracted for coronary heart
disease shows that they cover a large range of risks, and that
only 11.62% of them are incorrect, while the remaining
88.38% are correct. Comparison of the associated pathologies
extracted from abstracts with pathologies provided by the
MeSH indexing prove to be identical or semantically related in
about 70%. These two evaluations are very positive. Finally, a
comparison of pairs {risk factor; pathology} extracted from
citations and those proposed by associative relationships with-
in Snomed CT allowed us to observe that Snomed CT is not
dedicated to the recording of this type of information, although
some of the pathologies can be related to their risk factors in
this terminological resource.
We have several perspectives for this work. Methodologically,
we will design and apply other patterns for detecting and ex-
tracting information on risk factors. For instance, triggers like
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predictor, precursor are not taken into account currently. We
also plan to apply other methods, i.e. machine learning and
text mining. From a knowledge representation viewpoint, a
more precise categorization of risk factors within homogenous
groups will be performed. Thus, we can distinguish groups
related to environmental, social, clinical, behavioral, and other
risks. Besides, this categorization can even be mentioned in
abstracts:
Demographic risk factors (age, sex, and ethnicity), clini-
cal risk factors (diabetes mellitus, increased cholesterol,
antihypertensive medications, history of congestive heart
failure, myocardial infarction, hypertension, and neuro-
logical deficits), and behavioral risk factors (smoking
and heavy drinking) were controlled for statistically.
(PMID 11973166)
Another perspective is related to characterizing the extracted
information itself. Thus, some of the extracted elements may
occur in modal or negative contexts, which reduces their reli-
ability. Otherwise, geographical, demographic or other vari-
ables for risk factors exist. For instance, for a given pathology,
common risk factors in North America or Europe may be of
less (or no) relevance to other geographical areas. Such char-
acterization may well deserve more focus.
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Address for correspondence
Thierry Hamon, LIMBIO (EA3969)
UFR SMBH Leonard de Vinci, Universite Paris 13
74, rue Marcel Cachin, 93017 Bobigny Cedex France[.5ex]
e-mail: thierry.hamon@univ-paris13.fr
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