An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA)

RESEARCH ARTICLE Open Access An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA) Haitham Maarouf 1 , María Taboada 1* , Hadriana Rodriguez 1 , Manuel Arias 2 , Ángel Sesar 2 and María Jesús Sobrido 3 Abstract Background: Electronic rating scales represent an important resource for standardized data collection. However, the ability to exploit reasoning on rating scale data is still limited. The objective of this work is to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. We developed an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. In order to address the modeling challenges of the SARA, we propose to combine the best performances from OpenEHR clinical archetypes, guidelines and ontologies. Methods: A scaled-down version of the Human Phenotype Ontology (HPO) was built, extracting the terms that describe the SARA tests from free-text sources. This version of the HPO was then used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Each unit used the most appropriate technology to formally represent the required knowledge. Results: Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by Spinocerebellar Ataxia Type 36 (SCA36). When comparing the performance of our prototype with that of two independent experts, weighted kappa scores ranged from 0.62 to 0.86. Conclusions: The combination of archetypes, phenotype ontologies and electronic information-processing rules can be used to automate the extraction of relevant clinical knowledge from plain scores of rating scales. Our results reveal a substantial degree of agreement between the results achieved by an ontology-aware system and the human experts. Keywords: Rating scales, GDL, Human phenotype ontology, Clinical archetypes, SARA Background In the clinical domain, phenotypic abnormalities are defined as alterations in normal morphology (structural abnormalities such as cerebellar atrophy), physiology (functional abnormalities such as incoordination of movement), or behavior (such as difficulty in social interactions) [1]. Acquiring a better understanding of the phenotypic abnormalities associated with rare genetic diseases is crucial to improve the interpretation of the genetic tests, and the translation of genomic information into clinical practice [2]. Unlike genomic technology, collecting and analyzing phenotype data is not usually conducted following a standardized process. In clinical research, the process of collecting data ranges from determining the set of data to be gathered to decide the most appropriate computational representation. In general, this is a difficult, laborious and time-consuming task [3]. Phenotype annotation has a huge potential to automatically extract data * Correspondence: [email protected]s 1 Department of Electronics & Computer Science, Campus Vida, University of Santiago de Compostela, Santiago de Compostela, Spain Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 DOI 10.1186/s12911-017-0568-4 from large amounts of existing patient records or controlled trials. Recently, substantial progress has been achieved in encoding phenotypes using the Human Phenotype Ontology (HPO) [4]. This ontology supplies a standardized core of human phenotypic abnormalities and the relationships between them. It is accessible online [5] and contains over 12,000 classes and 16,000 hierarchical relationships. Electronic rating scales represent an important resource for standardized data collection, often providing primary and secondary outcome measures. While rating scales are used in all medical disciplines, they are especially relevant in specialties with a richness of complex phenotypic variables, such as neurology [6]. Clinical scales can measure the so-called latent variables, i.e., variables that can only be assessed indirectly through their manifestations. Examples of latent variables (or clinical dimensions) in neurological diseases include the quality and intensity of a tremor, the degree of gait imbalance or cognitive performance. These latent variables are assessed through a set of clinical questions (named statements or items) [7]. Each statement may have multiple ordered response options, for which an ordinal number (score) is assigned. The total score for the global clinical dimension is usually obtained adding up all individual scores for each statement. Wellknown examples are the Mini-Mental State Examination [8], a 30-point survey used to measure cognitive impairment, or the Glasgow Coma Scale [9] to assess coma and impairment of consciousness. These instruments improve data quality by reducing subjectivity in phenotype descriptions, and by simplifying the design of data collection protocols in research studies. Usually, rating scales grade several clinical dimensions through different items. For instance, in addition to the movement disorder (i.e., disease state), the Unified Parkinson’s Disease Rating Scale assesses other clinical sub-dimensions (such as mental state, complications of treatment and activities of daily life) via 42 questions that provide a total score [10]. Reducing all content of a rating scale to a unique number (total score) may lead to loss of useful clinical information about the dimensions implicitly collected by the scale. Hence, inferring the patient’s phenotype components from the sub-scores would facilitate medical evaluation, report writing, and clinical decision-making. In this work, we chose to address the Scale for the Assessment and Rating of Ataxia (SARA) [11], a well-validated instrument to evaluate the presence and severity of cerebellar ataxia [12]. This scale is broadly used and has been applied by our group in a research on the spinocerebellar ataxia type 36 (SCA 36). A use case for the SARA scale is illustrated below. Formal description of rating scales using standard clinical information models promotes computational data standardization, which is crucial for comparing results across studies [13], to integrate information among different applications and medical records, and to implement decision support systems. International consortia have developed standardized data models for clinical research and electronic health records, such as ISO 13606 [14], HL7 CDA [15], openEHR [16], NINDS CDE [3] and Intermountain Healthcare [17]. These efforts have focused on providing computable and formal specifications of clinical content, known as clinical models or archetypes. Such models also provide mechanisms to link the clinical statements to classes of some standard terminology or ontology. Both clinical archetypes and ontologies seek to structure patient information, according to the needs of clinical research and practice; however their perspectives are different. Archetypes model the information to mirror patient records. For example, the items paraparesis and facial palsy were recorded together into the archetype Stroke Scale Neurological Assessment, which is available on the Clinical Knowledge Manager (CKM) provided by the OpenEHR Foundation. Ontologies, on the other hand, aim to represent the meaning and relationships between clinical terms. For example, both paraparesis and facial palsy are represented as abnormalities of the nervous system in the HPO. However, the former is represented as an abnormality of the physiology, whereas the second one as an abnormality of the morphology. This ontological distinction cannot be reflected into the clinical archetype and however it is valuable to interpret the patient status. Thus, integrating ontologies with clinical archetypes would not only provide a static knowledge store, but also a dynamic resource to automatically infer patient phenotype and standardize data collection. Modeling rating scales by clinical archetypes Braun et al. [18] modeled a rating scale for the assessment of multiple sclerosis patients, following a standard archetype development approach. On this basis, ontology mapping was delayed until the final stages of modeling. At this late stage, the effort required to map archetype terms to ontology entities is substantial [19], dueinparttothelargesizeoftheontologies[20].Furthermore, designing clinical archetypes separately from ontologies may lead to major discrepancies in the meaning of the clinical statements. As a result, ontology mappings –key to achieve semantic interoperability among different data sources - are not common in theopenlyaccessiblearchetypes. Exploiting reasoning on clinical archetypes Exploiting reasoning on clinical archetypes is another challenge. The Guideline Definition Language (GDL) is a formalism to express decision support logic by a rule- Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 2 of 17 based declarative strategy. Some researchers found GDL reliable for guideline compliance in acute stroke and chronic kidney disease [21, 22]. However, GDL does not provide much support for ontologies and related reasoning. An alternative is to transform clinical archetypes/models into OWL-DL (Ontology Web Language –Description Language) [23–26]. Following this approach, ontology reasoners, such as Pellet, Hermit or Fact++, can be used to check the OWL-based archetype consistency [24], and make automated reasoning over the OWL dataset. This structure provides support for interoperability of rule-based mechanisms [24, 27, 28]. However, having two independent versions of the same standard model - one in the language of the model itself (ADL-Archetype Definition Language) and the other in OWL - makes maintenance more difficult. Furthermore, procedural knowledge as the sum of the scores in a scale (or any complex calculation function) cannot be simply represented in terms of OWL. An interesting alternative proposed by Mugzach et al. [28] to perform a particular counting in OWL (named k-of-N counting by the authors) was to develop a plug-in meeting the specific requirements. However, different calculation functions would then require implementing specific plug-ins. Other researchers defined knowledgeintensive mappings from the data sources to open- EHR archetypes [29, 30]. They distinguished between data-level and knowledge-level processing tasks. The former included calculation functions specified in the mappings and directly run on archetype data. The latter covered classification tasks defined using OWL classes with sufficient conditions. An integrated Personal Health Record is an alternative option to simplify data integration and clinical decision-making [31]. Scale for the assessment and rating of ataxia The SARA assesses severity of cerebellar dysfunction through the evaluation of eight items reflecting motor performance (gait, stance, sitting, speech disturbance, finger-chase test, nose-finger test, fast alternating hand movements and heel-shin test) [32]. For the last four items, upper and lower extremities are evaluated bilaterally, the mean values of both sides are calculated and added up to the scores of the first four items. The total score ranges from 0 (no ataxia) to 40 (most severe ataxia). The SARA is used in clinical studies for a more accurate evaluation of the patient’s motor performance, both globally and at the individual items. It is also useful for quantitative comparison of patients, ataxia types, disease stages and response to treatment, among other applications. One of the challenges of the SARA is the need to derive a qualitative description and patient classification with diagnostic implications from numerical scores. An automated system to solve this translation would greatly facilitate the use of the SARA –and, by extension, other scoring systemsby clinicians on both research and clinical routine settings. A use case description Let’s consider patients 1, 2 and 3, with the same total SARA score of 20 (Table 1). Just based on the total score, the three patients would be considered to be in similar clinical stage. However, their functional situation is notably different. While patient 1 scores very high for midline ataxia (which is concluded from the high values of the three first items) and can barely walk or sit unaided, patients 2 and 3 have compromised speech (item number 4) and limb coordination (from the values of the last four items). In turn, patients 2 and 3–with similar sitting, standing and walking performance –have different degree of speech impairment (mild in patient 2, while verbal communication is impossible for patient 3). The total –and even just partial scores for limbs –also do not help differentiate the actual phenotype of patients 2 and 3, who have very different performance with their limbs (appendicular ataxia derived from the last four items). While patient 2 has significantly impaired motor coordination on both sides, patient 3 has a more asymmetrical cerebellar syndrome, with severe impairment on his left side, but Table 1 An example scenario Patient 1 2 3 4 5-R 5-L 6-R 6-L 7-R 7-L 8-R 8-L Total Score 1 663100111122 Mean R-L 0112 20 2 221334343322 Mean R-L 3.5 3.5 3 2 20 3 221614141403 Mean R-L 2.5 2.5 2.5 1.5 20 Table shows three hypothetical patients with the same value for the total SARA score. Items are represented by numbers: 1-Gait, 2-Stance, 3-Sitting, 4-Speech disturbance, 5-Finger-chase test, 6-Nose-finger test, 7-Fast alternating hand movements and 8-Heel-shin test. Upper and lower extremities are evaluated bilaterally. R represents Right and L, left. Mean R-L is the mean value of both sides Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 3 of 17 only very mild involvement of his right side, which may be of enormous relevance to his functional ability if the patient is right-handed. Specific contribution The main goal of our work was to develop an electronic rating scale in the clinical domain of the neurology representing both the content and the interpretation of the SARA, using the Electronic Health Record (EHR) standards and taking advantage of semantic web technologies. In order to facilitate ontology mapping and prevent large semantic discrepancies between clinical archetypes and ontologies, we propose a novel method based on the assumption that archetype design should be supported by ontologies in those clinical situations where the archetype contents are logically organized. On the other hand, the interpretation of the SARA required different types of information: data to be recorded (i.e., the content of the rating scale), knowledge about the meaning of terms in the scale (i.e., terminological knowledge), procedural knowledge to understand the meaning of scores (that can be easily expressed by guidelines), and ontological knowledge to deduce patient phenotypes. We chose to use a combination of GDL, openEHR clinical archetypes and ontologies to address the challenges of modeling the rating scale. The research questions addressed in this work are: (1) is the combination of GDL, openEHR clinical archetypes and ontologies suitable for the description of all knowledge covered by the SARA?, and (2) is it possible to achieve a reasonable integration of these technologies to efficiently model the rating scale? Methods Our modeling approach is based on four main steps (Fig. 1): (1)buildingofascaled-downoftheHPO,throughextraction of the ontology modules relevant to the SARA, 2) annotation of the free-text descriptions of the rating scale with the ontology modules, 3) development of two clinical archetypes, Observation (for normalizing the scale content) and Evaluation (for recording the clinical interpretations), and 4) Definition of the information-processing units to express the clinical interpretation support system. Each step involves several activities, described below and summarized in Fig. 2. The modeling of SARA involved a data level - representation of the SARA items- and a knowledge levelreferred to the strategy to compute the total score and interpretation of phenotype. Archetypes were used to Fig. 1 The modeling approach. It includes: 1) Extraction of a scaled-down version of the HPO; 2) Annotation of the free-text descriptions of SARA items and scores; 3) Development of two archetypes (Observation and Evaluation); 4) Definition of the information-processing units in order to express the clinical interpretation of the SARA Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 4 of 17 model the data level, while GDL and OWL were used to model the knowledge level. This representation had restrictions, since openEHR models support GDL, but do not give much support for OWL and related reasoning. To bring the gap between the clinical archetypes and the ontology, mappings were defined. These mappings facilitated the translation of the archetype instances to the OWL dataset. Extracting the HPO ontology modules relevant to the SARA We reviewed and collected free-text sources describing the SARA, its component tests and application. For example, the following text describes the functional subdivisions of the cerebellum in order to explain the rationale of a coordination exam: “The cerebellum has 3 functional subdivisions, …. The first is the vestibulocerebellum. …Dysfunction of this system results in nystagmus, truncal instability (titubation), and truncal ataxia ... The following tests of the neuro exam can be divided according to which system of the cerebellum is being examined….”.We then annotated the sources to HPO terms, using the OBO Annotator [33], a phenotype concept recognition system. In total, 12 HPO classes were selected from the annotations (Additional file 1), which provided the set of seed terms required to extracting a self-contained portion of the HPO. Such scaled-down version of the HPO covered all the classes relevant to the SARA (in total, 45 subclasses of phenotypic abnormalities). In general, self-contained portions of an ontology are referred as ontology modules/segments [34], or slims in the context of the Gene Ontology. While these modules are subgroups of a base ontology, in our case the HPO, they are also equally valid on their own [35], but simpler and more manageable than the complete ontology. Annotating the rating scale SARA We used the scaled-down version of the HPO to annotate all items and scores of the SARA. First, we partially matched the SARA items to the ontology classes. Partial match happened when the item name was embedded inside a class name. Next, a neurologist (MJS) revised the candidate mappings, selected the most appropriate classes and proposed a minimal extension and reorganization of the scaled-down version of the HPO. Additional classes and relationships, as well as the mappings between items and classes are summarized in Fig. 3. In order to annotate the item scores, we reused the HPO classes describing different levels of severity: borderline, mild, moderate, severe and profound. We added new subclasses based on the severity levels of their superclasses. For example, moderate_dysarthria was defined as a subclass of dysarthria with a moderate severity. Two severity scores were identified as equivalent to existing HPO phenotypes: the score 8 for the item gait, which was mapped to the class abasia; the score 6 for the item speech disturbance, which was mapped to anarthria. We also added a new class (named astasia) to map the score 6 to the item stance, and two main superclasses: i) truncal ataxia, which subsumed the classes gait_ataxia and ataxic_postural_instability (which subsumed standing_instability and sitting_inst- ability); and ii) appendicular ataxia, which subsumed limb dysmetria,intention tremor and dysdiadochokinesis. Finally, the SARA-specific, scaled-down version of the HPO was translated to Protégé, its properties were manually modeled and the HermiT reasoner was used to check for consistency. In total, our scaled-down version of the HPO contains 109 subclasses of phenotypic abnormality (Additional file 2). Developing the archetypes for the SARA For archetype design, we chose two types of clinical archetypes: Observation and Evaluation. The first was used to normalize the scale content (i.e., clinical dimensions, items and scores), whereas the second was used to record the clinical interpretations and phenotypic abnormalities derived from the achieved numerical scores. Both clinical archetypes were developed using the archetype editor provided by OpenEHR (Additional files 3 and 4). Fig. 2 Summary of the main proposed activities for the modeling of electronic rating scales. They cover: extracting ontology modules, annotating the scale, building archetypes, and expressing interpretation Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 5 of 17 To model the observation archetype, after adding the metadata (e.g. purpose, keywords, definition, author, etc.), we structured the content of the SARA according to this archetype, i.e., by means of a tree structure with elements for the three clinical dimensions (the set of items, the total score and the date of the observations), and values for the individual scores. Then, we defined the elements with proper data types, descriptions, comments, details, occurrences, constraints and possible values. We mapped each element to the corresponding ontology class of the scaled-down version of the HPO, using the achieved annotations (see Annotating the rating scale SARA). Finally, we structured and organized the items in accordance with the ontology, by inserting three CLUSTER nodes in a hierarchical structure: i) gait and balance, which was linked to truncal ataxia; ii) Fig. 3 Excerpt from the set of mappings between the SARA and the ontology. Squares represent the SARA items. Gray and golden ellipses, respectively, are the original HPO classes and classes added to the ontology modules. Blue arrows are mappings between SARA items and HPO classes. Black and golden arrows, respectively, represent the original and the additional is_a relationships. Note that subsumptions in Electronic health record (Open Biological Ontologies) are represented using the is_a relationship, whereas in OWL using the subclassOf constructor Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 6 of 17 speech disturbance, which was linked to dysarthria; and iii) upper and lower limb coordination, which was associated with appendicular ataxia. This new structure did not alter the SARA, as it continued to be based on the same 8-items, but only arranged these items in a specific way (Fig. 4a). The new nodes can be viewed as clinical dimensions, but without any assigned score. Fig. 4b shows that the observation archetype meets the rating scale structure. The evaluation archetype (Fig. 5) recorded the clinical interpretations derived from the numerical scores (collected following the Observation archetype). We modeled this archetype following the same structure as the observation archetype - i.e., by means of a tree with three main clusters, one for each main clinical dimension: gait & balance, speech disturbance, and upper & lower limb coordination. Each cluster consisted of the interpretations directly or indirectly derived from the corresponding elements in the observation archetype. Additionally, since every element involved different levels of severity, so the “choice”data type was selected with Text/ Internal codes as constraints. Finally, we mapped the elements to ontology classes. Modeling the knowledge required to interpret the SARA data collection The SARA scale supplies standardized calculation functions, but it does not provide interpretation knowledge. Thus, we analyzed the information-processing units required to automatically interpret the data obtained through the application of SARA (Table 2). With the support of the ontology and acquiring the experience of the neurologist, we were able to express the interpretation of the SARA, although this is not standardized. The calculation functions expressed the mathematical operations to compute both the arithmetic means of the item scores and the total score. The input and output of these functions were elements defined in the observation archetype. In total, five GDL rules were modeled to cover these functions (Fig. 6). Cut-off levels are usually used to grade the severity of the scale items (i.e., elements in the observation archetype). Translating these cut-off levels to GDL required 61 rules. For example, if the score of the item sitting is 4, then the sitting instability is registered as severe (Fig. 7). Assessment of the cerebellar syndrome was based on the total score. The neurologist proposed several heuristic rules, which were modeled using GDL. Fig. 8 shows the rule inferring the absence of cerebellar syndrome. This knowledge was aligned with the results in [11], where the mean SARA score for controls was 0.4 ± 11. A clinical synopsis was built to outline the phenotypic features accompanying the cerebellar syndrome. One example of such features is midline ataxia (truncal ataxia) (Fig. 5), derived from the scores of the first three SARA items and leveraging the scaled-down version of the HPO. It required using elements and values from the ab Fig. 4 The structure of the content of the SARA. aThe new organization of the SARA, with three main patient’s phenotype components: gait & balance, speech disturbance, and upper and lower limb coordination; bThe clinical archetype developed for the SARA Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 7 of 17 evaluation archetype (such as, gait ataxia, sitting and standing instability) to infer other elements and values from the same archetype using the OWL ontology. During this modeling phase, we could only simulate ontology reasoning using the Protégé tool. From the values inferred by the GDL for elements of the evaluation archetype, and taking into account the mappings of this archetype, we manually entered the individuals of the linked OWL classes and run the reasoner. Additional file 5 provides the rules modeled in the GDL. Results System implemented in JAVA and MySQL To demonstrate the functionality of our approach, we developed a framework entitled “SMS”(SARA Management System), to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. We used JAVA as programming language, NetBeans as integrated development environment, JAVA Swing as user interface toolkit, and MySQL as database management system. The SMS architecture was structured in three layers: “Persistence”,“Operation”and “Interface”.Thepersistence layer used MySQL to store the clinical archetypes, thepatientinputdata,thedatainferredbythesystem, and additional information. Fig. 9 shows the relational model of the database with three types of tables. The first type included the class,subclass, scale, element and value tables. All of them modeled the attributes of the clinical archetypes (elements, values and mappings to ontology classes). To a greater extent, they were built based on the structure of the modeled archetypes. In order to populate the database with data from the archetypes, the ADL parser provided by the OpenEHR Foundation was used to generate a dependency tree of terms and then create an XML file, which was aimed at producing archetype instances when needed. The second type of tables stored the patient data, and the last type (test and test_value tables) recorded the set of SARA tests. The operation layer included all informationprocessing units (Table 2). The used version of GDL didnotprovideaJavaAPIoranymechanismformanipulating the rules defined using the editor. Hence, the only solution was to rewrite the rules in a rule engine(suchasDroolsorClips)ortoimplement Fig. 5 Structure of the evaluation archetype. It covers all the interpretations that can be derived from the SARA Table 2 Information-processing units. Each unit is defined by means of the used knowledge format, the inputs, outputs and the representation language Information-processing units Knowledge format Input Output Representation language Calculation functions Mathematical expressions Observation archetype items Observation archetype items GDL Assessment of the degree of impairment Cut-off level based severity rules Observation archetype item Evaluation archetype items GDL Assessment of cerebellar syndrome Heuristic rules Observation archetype items Evaluation archetype items GDL Clinical synopsis Scaled-down version of the HPO Evaluation archetype items Evaluation archetype items OWL Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 8 of 17 directly in Java (we decided this second solution). However, deriving phenotype information was reasonably implemented using the OWL API [36] and the mappings linking the archetype elements and values to OWL classes. The mappings were useful to automatically create OWL individuals from the archetype instances stored into the database. The interface layer consisted of several input and output forms. The main form of the tool provided three menus. The first one allowed users to modify the structure of the ontology (Fig. 10), by adding, updating or deleting classes. Additionally, it provided an option to automatically generate a hierarchical graph that displayed all the available classes and their is_a relationships. The second menu automatically generated entry forms based on the defined archetypes (Fig. 11). These forms were used to collect patient data from the user and run the tool. The third menu displayed the outcomes (Fig. 12): A table with all phenotypic abnormalities inferred from the collected patient data. A textual report summarizing the patient status. The report included the assessment of cerebellar syndrome and a brief synopsis of the phenotypic abnormalities accompanying the syndrome. An evaluation graph visualizing all the patient phenotypic abnormalities (Fig. 13). To facilitate semantic interoperable data exchange, the approach was designed to deliver the XML data collected by the SARA and also inferred by the application. Additionally, the system provided the facility to send the SARA results and the patient report by e-mail. The doctor could attach it to the patient medical record. Validation of the system For validation purposes, we used anonymized data records from 28 subjects with Spinocerebellar Ataxia Type 36 (SCA36), who had been examined with the SARA scale [37]. The institutional research ethics committee approved the recruitment and study protocol, and all participants gave their written informed consent. Only the scores for each SARA item were used for this evaluation, by two independent neurologists. The validation was carried out in three steps: 1) Automatic total score calculation and interpretation: We filled out the score data for each patient to get the interpretation. The system automatically inferred 1) severity for each item in the scale, 2) assessment of cerebellar syndrome, and 3) clinical synopsis. 2) Interpretation by two independent experts in movement disorders. These neurologists analyzed the same data set blindly and independently. They Fig. 6 Example of a calculation function. It is expressed by a GDL rule. The function calculates the mean of an “element”defined in the observation archetype (specifically, the element finger chase). The output of the rule refers to another element of the same observation archetype Fig. 7 GDL rule to assess the degree of sitting instability. The rule links the score 4 for the “sitting”item to the “severe sitting instability”class Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 9 of 17 modules, and validated the feasibility of our approach. All authors revised the article critically and approved the final manuscript. Ethics approval and consent to participate Ethical approval was obtained from the regional ethics review committee (Comité Autonómico de Ética de la Investigación de Galicia). All patients included in the study provided written informed consent. Data access was granted in the context of project OntoNeurophen (FIS2012-PI12/00373), where the authors’institutions participate. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1 Department of Electronics & Computer Science, Campus Vida, University of Santiago de Compostela, Santiago de Compostela, Spain. 2 Department of Neurology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain. 3 Instituto de Investigación Sanitaria (IDIS), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain. Received: 29 May 2017 Accepted: 24 November 2017 References 1. Robinson PN, Mungall CJ, Haendel M. Capturing phenotypes for precision medicine. Mol Case Stud. 2015;1(1):a000372. 2. Baynam G, Walters M, Claes P, Kung S, LeSouef P, Dawkins H, et al. Phenotyping: targeting genotype's rich cousin for diagnosis. J Paediatr Child Health. 2015;51(4):381–6. 3. Grinnon ST, Miller K, Marler JR, Lu Y, Stout A, Odenkirchen J, Kunitz S. National institute of neurological disorders and stroke common data element project–approach and methods. Clin Trials. 2012;9(3):322–9. 4. 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Neuroinformatics. 2013;2(2):291–305. • We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal • We provide round the clock customer support • Convenient online submission • Thorough peer review • Inclusion in PubMed and all major indexing services • Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit Submit your next manuscript to BioMed Central and we will help you at every step: Maarouf et al. BMC Medical Informatics and Decision Making (2017) 17:159 Page 17 of 17