=Paper=
{{Paper
|id=Vol-2042/paper17
|storemode=property
|title=Experiments to Create Ontology-based Disease Models for Diabetic Retinopathy from Different Biomedical Resources
|pdfUrl=https://ceur-ws.org/Vol-2042/paper17.pdf
|volume=Vol-2042
|authors=Mercedes Arguello Casteleiro,Catalina Martínez Costa,Julio Des-Diz,Maria Jesus Fernandez-Prieto,Chris Wroe,Diego Maseda-Fernandez,George Demetriou,Goran Nenadic,John Keane,Stefan Schulz,Robert Stevens
|dblpUrl=https://dblp.org/rec/conf/swat4ls/CasteleiroMDPWM17
}}
==Experiments to Create Ontology-based Disease Models for Diabetic Retinopathy from Different Biomedical Resources==
https://ceur-ws.org/Vol-2042/paper17.pdf
Experiments to create ontology-based disease models for
diabetic retinopathy from different biomedical resources
M. Arguello Casteleiro1, C. Martínez-Costa2, J. Des-Diz3, M.J. Fernandez-Prieto4, C.
Wroe5, D. Maseda-Fernandez6, G. Demetriou1, G. Nenadic1, J. Keane1, Stefan Schulz2,
and R. Stevens1
1
School of Computer Science, University of Manchester, UK
2
Institute of Medical Informatics Statistics and Documentation, Medical University of Graz
3
Hospital do Salnés, Villagarcía de Arousa, Spain
4
Salford Languages, University of Salford, UK
5
The BMJ, UK
6
Midcheshire Hospital Foundation Trust, NHS England, UK
Abstract. According to the World Health Organisation diabetic retinopathy
(DR) is a high priority eye disease. This paper investigates a method for creat-
ing disease models for DR using the ontologies BioTopLite2 and SNOMED CT
and different biomedical resources: 1) consultation notes from anonymised
electronic health records; 2) the clinical practice guideline for DR by the Amer-
ican Academy of Ophthalmology; 3) the BMJ Best Practice for DR; and 4) neu-
ral language models from Deep Learning (CBOW and Skip-gram) using a 14M
PubMed dataset. As SNOMED CT does not contain disease models, the novelty
of this study is twofold: a) evaluation of the utility of CBOW and Skip-gram for
obtaining DR disease models from the biomedical literature; and b) the pro-
posed method for building ontology-based disease models exploiting SNOMED
CT reference sets. In our method, we first propose a representation of
SNOMED CT reference sets for DR in OWL by extracting upper modules from
SNOMED CT. Secondly, we use content ontology design patterns with Bio-
TopLite2 and SNOMED CT that act as templates to semantically represent clin-
ical content in OWL. We report on the effectiveness of the method.
Keywords. Diabetic Retinopathy, SNOMED CT, ontologies, Deep Learning
1 Introduction
According to the World Health Organization (WHO), diabetic retinopathy (DR) is a
high priority eye disease. It can lead to permanent eye damage and its incidence
worldwide is expected to increase along with the incidence of diabetes [1]. Examples
of evidence-based resources that provide guidance for the diagnosis and management
of DR are the Clinical Practice Guidelines (CPG) developed by the American Acad-
emy of Ophthalmology (AAO) [2] or the BMJ’s Best Practice that covers DR [3].
Brassil et al. [4] acknowledge that “clinicians often encounter questions in their
work setting related to a challenging diagnosis, treatment decisions, or unexpected
complications, revealing a perceived knowledge gap”. This gap provides an oppor-
tunity for the use of Clinical Decision Support Systems (CDSS), e.g. evidence-based
decision support. There is currently, however, a lack of integration of CDSS into clin-
ical work and the Electronic Health Records (EHRs) [5].
Formal ontologies provide standardized, logic-based descriptions for classes of
domain entities in a computationally amenable form. This allows the meaning of enti-
ties referred to by data items and characterized by language-specific domain terms to
be both precisely known and manipulated, improving consistency. This constitutes a
basic requirement for semantic interoperability, i.e. the meaning-preserving commu-
nication of information across boundaries of systems, institutions and jurisdictions.
Hammond et al. [6] puts forward the notion of semantic interoperability as a “univer-
sal ontology that covers all aspects of health, health care, clinical research, manage-
ment, and evaluation”. Hence, semantic interoperability can aid the integration of
CDSS and the EHRs as well as the secondary use of clinical data within EHRs.
This study addresses the topic of semantic interoperability by combining the bio-
medical top-level ontology BioTopLite2 [7] with the ontology underlying the clinical
terminology SNOMED CT [8] to formally represent: a) clinical content from EHRs
(e.g. anonymised consultation notes for DR), and b) evidence-based clinical content
that can be the foundation for building CDSS (e.g. [2] or [3]).
Disease models are formal or semi-formal descriptions that help understand how a
disease develops and which treatment approaches can be considered. SNOMED CT is
the leading clinical health terminology for use in EHRs and it can be formally repre-
sented in the Web Ontology Language (OWL) [9]. However, SNOMED CT does not
contain disease models per se. In order to develop ontology-based disease models for
DR based on the SNOMED CT January 2017 release, two questions need to be ad-
dressed: 1) how to acquire a set of SNOMED CT concepts relevant for DR when this
SNOMED CT release contained 325143 OWL Classes?; and 2) how to formally rep-
resent a disease model for DR?
We propose a method to obtain ontology-based disease models consisting of a two-
step process. To validate the proposal, this paper investigates to what extent we can
create ontology-based disease models for DR based on BioTopLite2 and SNOMED
CT using four different biomedical resources: 1) a small number of anonymised con-
sultation notes; 2) the CPG for DR by the AAO [2]; 3) the BMJ Best Practice for DR
[3]; and 4) the neural language models CBOW and Skip-gram of Mikolov et al. [10]
produced from Deep Learning using a large corpus of scientific literature (i.e. Pub-
Med [11]).
2 SNOMED CT simple Refsets in OWL with the OWL API
The first step of our method identifies suitable biomedical resources, such as EHRs or
evidence bases from where to acquire a set of terms relevant for DR. This step makes
use of three design features of SNOMED CT [8]:
1. SNOMED CT components (i.e. concepts, descriptions and relationships); in
brief, SNOMED CT descriptions are clinical terms in a given language, di-
vided into (unique) FSNs (Fully Specified Names), preferred terms and syn-
onyms. Each description refers to a SNOMED CT Concept, with Concepts
being related by one or more binary relationships to other concepts. Running
a Perl script, an OWL ontology can be created for a SNOMED CT release.
2. SNOMED CT expressions: either single (pre-coordinated) SNOMED CT
concepts or compositional (post-coordinated) expressions constrained by the
SNOMED CT Compositional Grammar.
3. The extensibility mechanism (Refsets): A reference set (Refset for short) is a
subset of SNOMED CT components. The most basic Refset is the Simple
Refset, which can fully enumerate a subset of SNOMED CT components.
In this first step, a set of relevant terms for DR from a biomedical resource is
mapped to SNOMED CT obtaining a set of pre-coordinated and post-coordinated
expressions (i.e. one or more focus concepts). Next, a set of SNOMED CT concept
identifiers is created; this is the basis to define a signature of the SNOMED CT ontol-
ogy that allows the extraction of an ontological module.
We propose to represent a SNOMED CT Simple Refset in OWL as a signature of
the SNOMED CT ontology, i.e. a set of OWL Classes. The main benefit of our pro-
posal is avoiding exhaustive enumeration (e.g. all subtypes/descendants of a
SNOMED CT concept) and relying on the OWL API [12] to create upper modules
(i.e. ModuleType BOT) that guarantee the inclusion of all axioms relevant to the
meaning of the OWL Classes included in the signature.
A locality-based module (upper module) contains at least all the (entailed) super-
classes of an OWL class included in the signature [13]. Hence, top-level concepts of
relevant hierarchies will appear in the upper module extracted [13]. This study uses
the FaCT++ reasoner implementation from http://code.google.com/p/factplusplus/.
To validate this step, a set of relevant terms from each of the four biomedical re-
sources is acquired (e.g. terms in the highlights of the BMJ Best Practice for DR).
Next, a SNOMED CT signature (one per biomedical resource) is created taking most-
ly the OWL Classes for the focus concepts of the relevant terms identified. Finally,
SNOMED CT Simple Refsets for DR in OWL are created using each signature and
applying the OWL API ModuleType BOT; this step is considered successful if small-
er subsets of SNOMED CT components are obtained.
3 Content Ontology Design Patterns (Content ODPs)
The second step of our method relies on Ontology Design Patterns (ODPs) that can
“encapsulate in a single named representation the semantics that require several
statements in low level ontology languages” [14]. There are different types of ODPs
[15]. This study focuses on Content ODPs, and more specifically, on domain-related
ontology patterns [15] to formally represent disease models. This study adopts the
ontology framework proposed within the EU SemanticHealthNet project [16] to cre-
ate Content ODPs. An advantage of the Content ODPs proposed by SemanticHealth-
Net is that they are independent of any particular EHR specification, such as the
openEHR [17] or the HL7 Clinical Document Architecture Release 2 (CDA R2) [18].
The incorporation of concepts from clinical coding systems such as SNOMED CT
into the clinical model (e.g. HL7 CDA R2) is known as terminology binding [6] and
is far from trivial. However, terminology binding becomes easier when the infor-
mation represented by clinical models is expressed by Content ODPs. Both evidence-
based resources (e.g. CPGs) and EHRs contain clinical statements. Hence, evidence-
based decisions and clinical information in EHRs share clinical notions with typically
complex semantics that can be formally represented as domain-specific ODPs.
The Content ODPs of this study are based on BioTopLite2 [7] and SNOMED CT
[8] in OWL. BioTopLite2 is a biomedical top-level ontology, which introduces basic
categories and relations that seek to improve semantic interoperability by clarifying
and disambiguating the meaning of domain ontology content and, in our case, the
clinical data that they describe. SNOMED CT has close-to-user views of expressions
(abbreviated here as common patterns) that represent single diagnostic statements [8].
Table 1. Exemplifying Content ODPs in OWL for SNOMED CT common patterns. Note that
the “clinical life phase” is a re-interpretation of the SNOMED CT “clinical finding”
HL7 CDA R2 SNOMED CT Content ODPs in OWL Manchester Syntax using
section common BioTopLite2 and a clinical model
patterns
Physical Clinical finding cm:DiagnosticStatement and (btl2:represents only
Examination present cm:ClinicalLifePhase)
OR Clinical finding cm:DiagnosticStatement and (not (btl2:represents
Assessment absent only cm:ClinicalLifePhase))
Past Medical History of cm:DiagnosticStatement and (btl2:isPartOf some
History cm:PastHistoryInformation) and (btl2:represents
only cm:ClinicalLifePhase)
Plan Procedure not btl2:Plan and (btl2:hasRealization only
done cm:ClinicalProcess)
Table 1 contains examples of Content ODPs in the Manchester OWL Syntax [19]
for SNOMED CT common patterns from [20]. As can be seen in Table 1, the Content
ODPs can refer to the same clinical model (e.g. HL7 CDA R2) with more than one
clinical coding system. All the Content ODPs in OWL within Table 1 refer to diag-
nostic statements represented as HL7 CDA entries within a HL7 CDA
document section, where the first column in the table indicates the section for HL7
CDA R2. The same Content ODP can appear in more than one section of a HL7 CDA
R2 consultation note (e.g. Physical Examination section or Assessment section) while
referring to the same common pattern. In other words, the same Content ODP can be
applied to represent diagnostic statements that refer to different processes of care to
which an evidence-based resources (e.g. CPGs) will refer.
In Table 1 for the Content ODPs, the prefix btl2 indicates OWL constructs (Classes
in italics and Object Properties in bold) from BioTopLite2 and the prefix cm indicates
OWL constructs from the clinical model (e.g. HL7 CDA R2 or openEHR). The OWL
Class cm:DiagnosticStatement is a sub-class of btl2:InformationObject.
For the eleven SNOMED CT common patterns listed in [20], it is easy to identify
the SNOMED CT hierarchy from which the focus concept will belong. For example,
the common pattern from Table 1 Procedure not done will have focus concepts from
the Procedure hierarchy.
To validate the suitability of this step, we obtain the diagnostic statements that un-
derpin the disease models created for DR from four biomedical resources. This step is
considered successful if Content ODPs are obtained for each biomedical resource.
4 Results from experiments with different biomedical resources
Each of the following subsections contain: a) examples of the terms from a biomedi-
cal resource and its correspondence to SNOMED CT expressions; b) basic statistics
of the ontological signature and the locality-based module created; and c) examples of
Content ODPs in the Manchester OWL Syntax. It should be noted that a) and b) are
obtained in the first step of our method while c) are gained in the second step.
4.1 The Clinical Practice Guideline for DR by the AAO
We start by obtaining terms that appear in the CPG for DR by the AAO [2] within:
two tables that gather data collected from clinical trials and epidemiological studies of
DR; and one table that summarise the evidence-based management recommendations
for patients with diabetes. A total of 18 relevant terms were acquired from [2] corre-
sponding to DR severity level, DR findings, and DR management recommendations.
Table 2. Examples of terms from the CPG for DR by the AAO [2]
Terms from the CPG for DR Focus concept of HL7 CDA R2
SNOMED CT expression section
No apparent retinopathy 390834004 (Clinical finding) Assessment
Mild NPDR 312903003 (Clinical finding) Assessment
PDR 59276001 (Clinical finding) Assessment
Microaneurisms 34037000 (Clinical finding) Physical examination
Intraretinal hemorrhages 28998008 (Clinical finding) Physical examination
Venous beading 247118003 (Clinical finding) Physical examination
Panretinal 312713003 (Procedure) Plan
Photocoagulation Laser
Following the first step of the method, the 18 terms from [2] are represented as 15
pre-coordinated and 3 post-coordinated SNOMED CT expressions. The signature has
18 focus concepts as OWL Classes, where 15 are SNOMED CT concepts from the
Clinical finding hierarchy and 3 from the Procedure hierarchy. The locality-based
module created has 9.0K OWL Classes and 42.5K axioms.
Following the second step of the method, the SNOMED CT expressions from [2]
are transformed into Content ODPs. Table 2 illustrates some terms acquired from [2]
where a row with a white background indicates a pre-coordinated expression, while a
row with a grey background indicates a post-coordinated expression. The second
column shows the SNOMED CT concept identifier for the focus concept of a
SNOMED CT expression and, in brackets, the SNOMED CT hierarchy to which the
focus concept belongs. Hence, the second column provides the key information to
relate a term from [2] to one of the eleven SNOMED CT common patterns listed in
[20]. The last column indicates the HL7 CDA R2 sections where the diagnostic
statement may appear.
Combining the information from Table 1 and 2, we can easily create Content ODPs
by instantiating the formal concept definitions in OWL from Table 1. Hence, the Con-
tent ODP for Microaneurisms (Table 2) when absent:
Individual: cs:AbsenceOfMicroaneurismas
Types: cm:ClinicalFindingAbsent and (not (btl2:represents only sct:34037000))
where the prefix sct indicates OWL constructs from SNOMED CT in OWL; and the
prefix cs indicates OWL constructs for diagnostic statements in OWL.
4.2 The BMJ Best Practice DR
We start by acquiring terms that appear in the BMJ Best Practice that covers DR [3].
A total of 29 relevant terms were obtained from [3] corresponding to DR history and
examination, DR diagnostic investigations, and DR severity level and management.
Following the first step of the method, the 29 terms from [3] are represented as 24
pre-coordinated and 5 post-coordinated SNOMED CT expressions. The signature has
29 focus concepts as OWL Classes, where 21 are SNOMED CT concepts from the
Clinical finding hierarchy and 8 from the Procedure hierarchy. The locality-based
module created has 16.1K OWL Classes and 75.1K axioms.
Following the second step of the method, the SNOMED CT expressions from [3]
are transformed into Content ODPs. Table 3 has the same format of Table 2. As
shown in Table 3, the BMJ Best Practice for DR [3] indicates if a term for DR history
and examination is common or uncommon. We represent formally a common diag-
nostic statement in OWL as:
Class: cm:CommonDiagnosticStatement
EquivalentTo:
cm:DiagnosticStatement and (btl2:hasPart some cm:CommonInformation)
where cm:CommonInformation is a sub-class of btl2:InformationObject. We can
likewise create the OWL Class cm:UncommonDiagnosticStatement.
In Table 3, “macular thickening” does not have a SNOMED CT pre-coordinated
expression (row with a grey background) and can be represented as the post-
coordinated expression 312999006|Disorder of macula of retina|: 42752001|Due
to|=89977008|Increased thickness|. In this study, SNOMED CT postcoordinated ex-
pressions in OWL have the prefix sctpost. The Content ODP for “macular thickening”
in Table 3 when present (instantiation of the Content ODP from Table 1) is:
Individual: cs:PresenceOfMacularThickening
Types: cm:ClinicalFindingPresent and (btl2:hasPart some cm:CommonInformation)
and (btl2:represents only sctpost:312999006_42752001_89977008)
Table 3. Examples of terms from the BMJ Best Practice for DR [3]
Terms from the BMJ Best Focus concept of HL7 CDA R2
Practice DR SNOMED CT expression section
diabetes (common) 73211009 (Clinical finding) Past Medical History
severe NPDR 312905005 (Clinical finding) Assessment
microaneurysms (common) 34037000 (Clinical finding) Physical examination
lipid exudates (common) 247131008 (Clinical finding) Physical examination
macular thickening (common) 312999006 (Clinical finding) Physical examination
venous beading (uncommon) 247118003 (Clinical finding) Physical examination
Vitrectomy 75732000 (Procedure) Plan
4.3 Anonymised HL7 CDA R2 Consultation Notes for DR
We start by retrieving SNOMED CT coded entries (i.e. terms) that appear in 5 anon-
ymised HL7 CDA R2 consultation notes for DR. A total of 80 HL7
CDA entries were obtained from 3 HL7 CDA document sections (i.e. Past Medical
history; Physical examination; and Assessment and Plan) of the 5 consultation notes.
Following the first step of the method, the 80 HL7 CDA entries re-
fer to 28 unique SNOMED CT focus concepts (no repetitions) that can be represented
as 27 pre-coordinated and 1 post-coordinated SNOMED CT expressions. The signa-
ture has 28 focus concepts as OWL Classes, where 22 are SNOMED CT concepts
from the Clinical finding hierarchy and 6 from the Procedure hierarchy. The locality-
based module created has 1.6K OWL Classes and 7.8K axioms.
Table 4. Examples of terms (coded entries) from HL7 CDA R2 consultation notes for DR
Terms from HL7 CDA R2 Focus concept of HL7 CDA R2
consultation notes for DR SNOMED CT expression section
Moderate NPDR 312904009 (Clinical finding) Assessment
Incipient cataract 52421005 (Clinical finding) Physical examination
OR Assessment
Retinal microaneurysm 34037000 (Clinical finding) Physical examination
Retinal haemorrhage 28998008 (Clinical finding) Physical examination
Diabetic retinal venous beading 399866003 (Clinical finding) Physical examination
Posterior segment fluorescein 252822006 (Procedure) Plan
angiography
Following the second step of the method, the SNOMED CT expressions from the
HL7 CDA R2 consultation notes are transformed into Content ODPs. Table 4 has the
format of Table 2 and illustrates some of the coded entries retrieved. “Incipient cata-
ract” appears in Table 4 and it is found in two consultation notes. Cataract does not
appear in the main summary tables of the CPG for DR by AAO [2] or in the high-
lights of the BMJ Best Practice for DR [3]. However, [2] acknowledges cataract as a
side effect/complication of vitrectomy as well as intravitreal injections (i.e. DR treat-
ment) and [3] acknowledges cataract as a complication of patients with diabetes.
Hence, EHRs can provide clinical findings that are pertinent for DR while not being
within the main summaries or highlights of evidence-based resources.
Combining the information from Table 1 and 4, we can create Content ODPs. The
coded entry (i.e. the term) “incipient cataract” that appears in Table 4 when present
has the following Content ODP in the Manchester OWL Syntax:
Individual: cs:PresenceOfIncipientCataract
Types: cm:ClinicalFindingPresent and (btl2:represents only sct:52421005)
4.4 CBOW and Skip-gram word embeddings from 14M PubMed dataset
PubMed contains important scientific discoveries and findings that can aid healthcare
professionals to provide better care. However, the large volume and rapid growth of
PubMed makes it difficult to acquire a list of relevant terms for DR directly from
PubMed. In this study, we use CBOW and Skip-gram from Deep Learning to get a list
of relevant terms for DR using a large-scale dataset of PubMed publications.
The neural language models CBOW and Skip-gram make it feasible to obtain word
embeddings (i.e. distributed word representations) from corpora of billions of words.
Using similarity measures (i.e. cosine value) we can build up a list with the n top-
ranked candidate terms for a target term (e.g. diabetic retinopathy). The experimental
set-up (e.g. hyperparameter configuration) to create word embeddings from CBOW
and Skip-gram using a 14M PubMed dataset is the same as the one described in [21].
In this study we use three target terms that appear in Table 5. According to the
BMJ Best Practice DR, both microaneurysms and lipid exudates are common findings
for DR. For each target term, we limit the list to the fifty candidate terms with the
highest cosine value (i.e. the top fifty ranked). A total of 300 candidate terms was
obtained. After removing duplicates, the list contains 124 unique candidate terms. Not
all the candidate terms are relevant for DR (i.e. false positives). Using the BMJ Best
Practice for DR, a medical consultant determined that 113 terms were true positives
(tp) and 11 were false positives (fp), which gives 91% of overall precision. The 113
candidate terms considered relevant (tp) relate to the DR history and examination, DR
diagnostic investigations, and DR severity level and management.
Table 5. Precision calculated as tp/(tp+fp) per neural language model and target term
Target terms
Model diabetic_retinopathy diabetic_retinopathy diabetic_retinopathy
microaneurysms exudates
CBOW 86% 86% 88%
Skip-gram 100% 100% 98%
Table 5 shows the precision for each model and target term. Skip-gram outper-
forms CBOW. The target term in the last column improves CBOW precision.
Following the first step of the method, the 113 tp terms are represented as 35 pre-
coordinated (7 of these can act as focus refinement and are not focus concepts) and 5
post-coordinated SNOMED CT expressions. The signature has 34 concepts as OWL
Classes: 28 from the Clinical finding hierarchy; 4 from the Procedure hierarchy; 1
from the Observable entity hierarchy; and 1 descendant of the Morphologically al-
tered structure OWL Class. Hence, considering the clinicians’ views, neovasculariza-
tion (morphologic abnormality) was added to the signature despite of not being a
focus concept. The upper module has 9.6K OWL Classes and 45.6K axioms.
Following the second step of the method, the SNOMED CT expressions for the tp
terms are transformed into Content ODPs. Some tp terms correspond to SNOMED
CT expressions, which have the focus concepts in Table 3 – with the exception of the
focus concept for “venous beading” (not in the candidate terms list). The Content
ODP for planned “vitrectomy”:
Individual: cs:PlanOfVitrectomy
Types: btl2:Plan and (btl2:hasRealization only sct:75732000)
5 Discussion and Conclusion
The SNOMED CT Refset mechanism is a method for filtering and arranging
SNOMED CT concepts for specific domains or use cases [22]. SNOMED CT con-
cepts belonging to a Refset are portable and can be reused among organisations with
similar needs. This study has investigated the creation of Simple Refsets for DR in
OWL by creating upper modules from ontological signatures using four biomedical
resources. The biggest upper module created with the OWL API ModuleType BOT
from the SNOMED CT ontology is the one for the BMJ Best Practice DR, which has
5% of the OWL Classes in the SNOMED CT ontology. The smallest upper module is
the one from the anonymised HL7 CDA R2 consultation notes, which has 0.5% of the
OWL Classes in the SNOMED CT ontology. There are two potential reasons: 1) the
very limited number of consultation notes; and 2) the coded entries refer to more spe-
cific SNOMED CT concepts. For example “Diabetic retinal venous beading” (Table
4) is more specific than “venous beading” (Table 2 and 3). Overall, module extraction
seems to be an effective means to obtain small subsets of SNOMED CT components.
Although the Content ODPs illustrated in this paper only refer to the OWL Classes
included in the ontological signatures created for the SNOMED CT ontology, there is
no impediment to roll out the approach presented to the OWL Classes from the upper
modules obtained (e.g. descendants of an OWL Class included in the signature). What
is essential for the second step of our method to work is complying with the underly-
ing mapping from Table 1 that implies considering the SNOMED CT common pat-
terns from [20] based on the SNOMED CT expression at hand.
Arguably, the best resources to obtain disease models for DR are the CPG for DR
by the AAO [2] and the BMJ Best Practice for DR [3]. However, both the CPG for
DR by AAO and the BMJ Best Practice for DR need periodic updates from the ever-
growing scientific biomedical literature. In 2017, PubMed contains references to more
than 27M scientific publications [11], with an average of two papers added per minute
in 2016 [23]. This paper has investigated to what extent CBOW and Skip-gram from
Deep Learning can derive key clinical content that appears in evidence-based re-
sources like the BMJ Best Practice for DR. The higher precision for Skip-gram (98%
to 100%) with the three target terms for DR indicates the potential of the neural lan-
guage models from Deep Learning to aid periodic updates of evidence-based re-
sources like the BMJ Best Practice.
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