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Automatic computation of CHA2DS2-VASc score: Information extraction from clinical texts Cyril Grouin, MSc1, Louise Del´eger, PhD1, Arnaud Rosier, MD2,3,4, Lynda Temal, PhD2,3, Olivier Dameron, PhD2,3, Pascal Van Hille, MSc2,3, Anita Burgun, MD, PhD2,3, Pierre Zweigenbaum, PhD1 3 Universit´e de Rennes 1, U936, F-35000 Rennes, France l’Institut Catholique Lillois, Facult´e Libre de M´edecine, F-59000 Lille, France The CHA2DS2-VASc score is a 10-point scale which allows cardiologists to easily identify potential stroke risk forpatients with non-valvular fibrillation. In this article, we present a system based on natural language processing(lexicon and linguistic modules), including negation and speculation handling, which extracts medical concepts fromFrench clinical records and uses them as criteria to compute the CHA2DS2-VASc score. We evaluate this system bycomparing its computed criteria with those obtained by human reading of the same clinical texts, and by assessing theimpact of the observed differences on the resulting CHA2DS2-VASc scores. Given 21 patient records, 168 instancesof criteria were computed, with an accuracy of 97.6%, and the accuracy of the 21 CHA2DS2-VASc scores was 85.7%.
All differences in scores trigger the same alert, which means that system performance on this test set yields similarresults to human reading of the texts.
Unstructured clinical notes contain a wealth of information, some of which is absent from the structured part of theelectronic patient record. This has motivated a long-running stream of work on natural language processing applied toclinical texts. [1,2,3] The bottom line of that work is the detection of information elements in clinical texts, encompassingthe main types of concepts involved in clinical practice: diseases and other problems, [4] tests, treatments includingmedication, [5] and their component concepts such as anatomy.
A second level of target information aggregates this elementary information to compute clinical indicators for a patient,for instance their smoking status, [6] obesity status, [7] or the presence of a congestive heart failure (CHF). [8,9] Suchinformation is useful for a variety of applications such as triggering alerts or recruiting patients for clinical trials. [8] Project Akenaton [10] addresses the extraction of medical information from French free text patient reports in thedomain of cardiology, focusing on patients who have a pacemaker. In this domain, a key information element is theCHA2DS2-VASc score, [11] a new recommendation of the European Society of Cardiology. It has been proposed todetermine the stroke risk for patients with non-valvular fibrillation. [12] This score is computed from eight criteria.
Each criterion counts for 1 or 2 points in the final score: (i) Congestive heart failure or left ventricular dysfunction,1 pt, (ii) Hypertension, 1 pt, (iii) Age≥75, 2 pts, (iv) Diabetes mellitus, 1 pt, (v) Stroke, transient ischemic attack, orthromboembolism, 2 pts, (vi) Vascular disease (prior myocardial infarction, peripheral artery disease, aortic plaque),1 pt, (vii) 65≤Age<75, 1 pt, (viii) Sex category, 1 pt if female gender. The final computed score varies from 0 to 9.
In conclusion, the higher the score, the higher the risk of thromboembolism. This CHA2DS2-VASc score constitutesone of the elements taken into account when a clinician has to decide whether an anticoagulation therapy is requiredto prevent potential stroke. Some criteria such as age and sex can be extracted from the hospital information system,while others need a careful analysis of clinical documents. To the best of our knowledge, no previous work hasaddressed the automatic computation of this score based on clinical texts.
Furthermore, this task is harder than, e.g., computing a score such as the body mass index (BMI): whereas BMIonly needs to find the two numeric measures of body weight and height, the CHA2DS2-VASc score needs to assessthe presence or absence of concepts that have a more complex definition. For instance, the concept of peripheralartery disease (PAD) encompasses a range of diseases that are more specific than the generic description called PAD.
Ontological knowledge is therefore useful to help link the specific disease names that can be found in patient reports tothe generic detection of presence or absence of a peripheral arterial disease. We use a module [13] which computes theCHA2DS2-VASc formula based on concepts in an ontology, [14] which allows us to detect the presence of the relevant concepts at the right level of abstraction. The present paper focuses on the natural language processing part of theCHA2DS2-VASc computation pipeline. More detail on the ontology-based module can be found in Dameron et al. [13] Since the score depends on the presence or absence of concepts for a given patient, a proper treatment of negation andother expressions of modality is important to avoid false detection of negated concepts. Negation and probability pro-cessing can be handled in English clinical texts by Chapman’s NegEx system. [15] A more complete detection of othermodalities was addressed in the i2b2/VA 2010 challenge [4] task on the detection of assertions on medical problems,which additionally included categories conditional, hypothetical and not associated with the patient. However, NegExworks on English text, and there is no equivalent system available for French clinical texts. We therefore designed aversion of NegEx extended to the i2b2 assertion categories and adapted to French.
In this paper, we present the system we designed and implemented to extract medical concepts from clinical records incardiology, within the framework of thromboembolism risk. We then present its evaluation through a comparison ofhuman reading vs. automatic extraction, focusing on the criteria that are used to compute the CHA2DS2-VASc score.
We conclude with a discussion of current results and perspectives for further work.
Previous work has addressed the classification of patients into predefined classes: such classes can be binary, e.g.
CHF vs non-CHF, [8,9] or involve a choice among a few disjoint categories, e.g. five categories of smoking status (pastsmoker, current smoker, etc.). [6] In such cases, the problem can be modeled as a supervised classification task wherethe system must predict the correct class based on features which represent the patient cases. These features areextracted from the texts by natural language processing methods with a varying degree of sophistication, includingsome handling of negation. [9] A characteristic of most work on the classification of patient information is the use of flat lists of terms, often obtainedfrom existing controlled vocabularies (possibly through the UMLS Metathesaurus [16]), aggregated in such a way as todetect each relevant feature. This was the case for instance in the i2b2 medication extraction challenge, [5] where mostsystems used lists of drug names to detect medications and lists of findings and disease names to find the reason for aprescription. It would be possible to adopt the same approach to detect, e.g., peripheral arterial diseases. However, weopted for a more principled approach in which the natural language processing component deals with the recognition ofspecific concepts in the texts, and a separate ontological component decides whether a specific concept (e.g., “art´eritedes membres inf´erieurs” (lower limb arteritis)) is an instance of a generic concept (e.g., peripheral arterial disease)which instantiates a criterion or not. This avoids the overspecialization of concept detection in the texts, and allowsthe NLP component to output a more versatile representation where concepts can be used for a larger variety of tasksbeyond the specific question of CHA2DS2-VASc score computation.
The supervised classification approach used by many recent systems also looses much relevance in the case of thecomputation of a formula. In the case of the CHA2DS2-VASc score, the number of possible values (10 values, from 0to 9) would make it more difficult for supervised classification to take these values as discrete target classes. Besides,the mathematical relation of the score to the underlying features would be lost by a straightforward application ofclassical feature-based supervised learning methods. We found no compelling argument not to rely on the existingformula to compute the CHA2DS2-VASc score once the basic information elements (or features) are determined,that is, to apply a knowledge-based approach to that task. Our knowledge-based approach to the computation of theCHA2DS2-VASc score therefore departs from other works based on supervised classification of patient reports.
Negation processing has been extensively studied for English-language medical texts. [15,4,17] Recent work transferredNegEx to Swedish by transposing its trigger phrases from English to Swedish. [18,19] Although Swedish and Englishare both Germanic languages, a simple translation was not enough, because of differences in grammatical features(e.g., gender and number agreement in Swedish), constructs (e.g., do auxiliary in English negations), or word order.
French is a Romance language, so transfer to French might raise other issues.
We need to determine the relevant information elements that will be used to compute the eight criteria on which theCHA2DS2-VASc score computation depends. We then need to design an information extraction system which willidentify medical concepts in French clinical documents in this purpose. We consider as a relevant information element a piece of information obtained from a text, more specifically a clinical record, that could be of interest within theframework of thromboembolism risk assessment. The final objective of this framework is to automatically identifypatients with risk of thromboembolism attack in case of atrial fibrillation.
With the help of a cardiologist, we defined a list of nine topics the system must focus on to deal with this framework.
These are topics around which information elements needed to compute the eight CHA2DS2-VASc score criteria canbe found, although there is not a one-to-one correspondence between these topics and the criteria: one topic may con-tribute to several criteria, and vice-versa. These topics are the following: (i) age of patient, (ii) atrial arrythmia episode,(iii) blood clot or thrombus formation, (iv) arterial embolism, (v) cardiovascular risk factors (tobacco addiction, di-abetes, etc.), (vi) heart disease (aortic valve regurgitation, left ventricular ejection fraction (LVEF), left ventricularend diastolic diameter (LVEDD), mitral failure, etc.), (vii) atrial fibrillation duration and characteristics, (viii) rate-lowering drug/treatment and anticoagulation treatment, and (ix) pacemaker and defibrillator information. These topicsare general categories which we have to expand to be able to extract the specific types of medical information that arerelevant here. Example instances of information elements to extract are given in Table 1.
Table 1: Relevant information elements for thromboembolism risk identification in French clinical documents.
un tabagisme majeur, de l’ordre de 40 cigarettes/jour a major tobacco addiction, about 40 cigarettes/day poursuivi depuis l’ˆage de 12 ans.
un cholest´erol total `a 2,9 g/l et des triglyc´erides `a 1,82 a total cholesterol of 2.9g/l and triglycerides of 1.82g/l la fraction d’´ejection est retrouv´ee `a 44% avec un the left ventricule ejection fraction is 44% with a left diam`etre t´el´ediastolique du ventricule gauche `a 63 mm.
ventricular end diastolic diameter of 63mm.
il existe une insuffisance mitrale mod´er´ee 1,5/4 there exists a moderate mitral valve regurgitation 1.5/4.
Additionally, some drug prescriptions are also a precious clue of patient condition, such as hypertension or othercoronary diseases. Extraction of drug prescriptions thus brings additional help to compute the CHA2DS2-VASc score.
For instance, if hypertension is not explicitly mentioned but some antihypertensive medication is found in a patientreport, the hypertension point can be added to the score.
We created a system to extract these information elements using several NLP modules (see Figure 1). Each moduleof this system is based on two main characteristics: first, the use of a domain-restricted lexicon to identify importantterms as well as trigger words, and second, extraction rules to refine concept identification.
Figure 1: Global architecture of the system used to compute the CHA2DS2-VASc score. NLP modules are insiderectangle shape boxes.
The system first performs a basic sentence segmentation before applying the lexicon and extraction rules. This helpsto process the documents at a linguistically sound level of granularity so that syntactic and semantic processes areapplied to a controlled input. This system can be categorized as human-knowledge-based, in contrast to machine-learning-based systems, since it relies on human-defined lexicons and extraction rules.
We created a global lexicon composed of 106,639 entries we gathered from three distinct lexicons. Not all entries focus on thromboembolism risk assessment (e.g., we gathered all existing drug names, not only those used incardiology, assuming that ignoring information is easier than looking for missing information). The three lexicons arethe following: 1. a drug name lexicon we gathered from both professional and general public sources (Vidal,∗ Doctissimo,† etc.); 2. a list of medical problems extracted from the Unified Medical Language System (UMLS) Metathesaurus; [16] 3. and a list of specific cardiological terms provided by a cardiologist.
Each entry in our lexicon contains a medical term, a general category the term belongs to (anatomy, disease, drug,family, laboratory results, procedure), the corresponding concept in a home-made ontology, [14] and the parent of theconcept (as found in the ontology). Table 2 presents example medical concepts from each of these categories. Thefirst step of the system uses this global lexicon to identify these medical terms.
Table 2: Example entries of the global lexicon.
oreillette gauche (left atrium), valve aortique (aortic valve), ventricule gauche insuffisance de la valve mitrale (mitral valve regurgitation), l´esion aortique (aor- tic lesion), myxome de l’oreillette gauche (left atrial myxoma), thrombose aor-tique (aortic thrombosis), etc.
atenolol (atenolol), avk (anti-vitamin k), coumadine (coumadin), h´eparine (hep- beau-p`ere (father in law), jumeaux monozygotes (monozygotic twins), m`ere d´ebit cardiaque (cardiac output), ventilation pulmonaire (pulmonary ventilation), pontage aortique (aortic bypass), valvulotomie mitrale (mitral valvulotomy), etc.
To refine medical concepts located in the documents, we defined specific extraction rules, which we implemented using regular expressions. We based these rules upon empirical observation of the clinical documentsin our corpus. As we are focusing on thromboembolism risk, we defined a set of rules to deal with 25 cardiologicalcases.‡ These rules allow us to take into account variant expressions (full word, abbreviation, etc.) and/or differentcases of precision (with adjectives or different formulations, etc.).
As medical information is written in natural language in clinical documents, a basic identifi- cation of clinical concepts is not sufficient. Indeed, a system can detect a medical concept in a negated expression (thepatient does not exhibit the mentioned problem); natural language can also express information with varying degreesof uncertainty; finally, within specific sections of the clinical documents, such as history of present illness or familyantecedents, medical information can involve someone else than the patient (in the case of a hereditary disease). Wecreated three modules to deal with these linguistic cases, as shown on Table 3.
∗Vidal is a medical reference in France for health care professionals, http://www.vidal.fr/ visited on 03/15/2011.
†Doctissimo is a general public health and wellness portal with articles written by clinicians, http://www.doctissimo.fr/ visited on 03/15/2011‡Aortic leak, aortic stenosis, atrial failure, atrium, hypertension, body mass index, bypass, crackles, left ventricule ejection fraction, fibrillation, flutter, hereditary antedecent, high blood pressure, internationalized normalized ratio, LDL cholesterol, left ventricular end diastolic diameter, mitralvalve regurgitation, mitral leak, mitral stenosis, overweight, pacemaker, prothrombin rate, ventricular septum, shortening fraction, tobacco addiction Table 3: Modules to handle negation and other modalities of the extracted information.
ne retrouve pas le moindre œd`eme des mem- does not find the slightest lower limb edema afin de rechercher une ´eventuelle isch´emie my- in order to find a potential myocardial ischemia In order to deal with negation, we used the NegEx algorithm. [15] To adapt this algorithm to French, we created a listof 318 negation triggers for French. This trigger list is based on a translation of the existing English triggers andempirical observation of the French cardiology corpus. We also used the 9 major categories of NegEx to categorizenegation triggers. Table 4 displays examples of the triggers used in our adapted NegEx system to identify negation inFrench clinical documents.
Table 4: French negation triggers used with the NegEx algorithm.
absence de (lack of), jamais eu (never had), aucun (no), pas de signes de (no sign of), etc.
est ´ecart´e (is ruled out), ont ´et´e ´elimin´es (have been eliminated), etc.
peut ˆetre ´ecart´e (can be ruled out), sera ´ecart´e (will be ruled out), etc.
cependant (nevertheless), sauf (except), etc.
pas de changement significatif (no significant change), pas sˆur de (not certain whether),ne cause pas (does not cause), etc.
A second module allows us to determine the uncertainty of the expressed information within a sentence. We created amodule based upon trigger words of two kinds: • Pre uncertainty trigger words: ´eventuel (eventual), hypoth`ese (hypothesis), possible (possible), probable (prob- able), risque de. (risk of.), d´epistage du. (screening of.), pr´evention du. (prevention of.), recherche du.
(search for.), etc.; • Post uncertainty trigger words: suspect´e (suspected), comme hypoth`ese (as hypothesis), etc.
In case of uncertainty, we decided not to extract the medical concept, considering that a human interpretation of sucha phrase is needed to decide whether this concept must be considered or not.
Finally, we also designed a module which tries to identify who is the experiencer of a medical problem within awindow of 9 words before or after the studied medical problem. This module uses a list of 147 entries from our globallexicon to identify the subject of the disease. It detects whether the mentioned medical problem affects the patient orsomeone else from their family. If a problem affects someone else than the patient, we do not take this problem intoaccount.
Our medication extraction module is an adaptation to French of a system we designed for the 2009 i2b2 naturallanguage processing challenge. [5] This challenge was dedicated to medication prescription extraction from clinicaldocuments in English. It aimed at extracting drug names and all related information (dosage, mode of administra-tion, frequency, duration, and reason for prescription). We took advantage of this challenge to develop a medicationextraction system. [20] Using a rule-based system, we ranked 8th out of 22 participants with a 0.773 F-measure.
Our system relies on the use of lexicon and extraction rules based on trigger words (abbreviations and expressions forall related information classes). We built three lexicons: (i) a drug lexicon to detect drug names based upon drug namesfrom the UMLS Metathesaurus and therapeutic classes, (ii) signs and symptoms lexicons to identify the reason why agiven medication was prescribed, based upon the UMLS Metathesaurus using entries with the “Signs and Symptoms”semantic type and the “MetaMap NLP View” flagged terms, and (iii) lists of abbreviations and expressions to extractdrug-related information, where each entry has been associated with the type of information it denotes.
We performed an extraction in several steps, as follows: (i) we split the document into sentences, (ii) we then appliedthe lexicon to identify drug names within each sentence as an exact match, (iii) we split the sentences into parts, whereone part begins with a drug name, and (iv) we searched related information inside each part, considering that relatedinformation often follows a drug name, but we also extended the search to the sequence closely preceding the drugname.
We then adapted our system to the French language. [21] We kept the general architecture of the English system (sen-tence splitting, identification of drug names, and detection of associated information) using a lexicon and rules. Wemodified the lexicon (gathering drug names, pharmacological substances and abbreviations or spelling variants inFrench) and the rules (by adapting English rules and adding new rules designed by observation of the developmentcorpus). We also kept the same classes of target information (medication, dosage, mode of administration, frequency,duration, and reason for prescription). We evaluated our French medication extraction system over a test corpus com-posed of 50 French patient records that we manually annotated as a reference (257 drug names to identify with theirrelated information): it obtained a 0.867 F-measure.
Having identified medical concepts and prescriptions within clinical documents, the system builds a global XML filethat sums up all information extracted from the document; it also completes each kind of information with genericattributes that allow subsequent processing to easily access information items independently of the way informationwas given in the source clinical document.
For each medical concept, we add specific values denoting the type of concept and the corresponding concept inthe Akenaton ontology. Table 5 lists examples of extracted concepts from a clinical document with the additionalinformation we add for each medical concept.
Table 5: Examples of concepts extracted from a clinical document and additional information for this concept.
For each prescription, we also linked each drug name to the ATC (Anatomical Therapeutic Chemical) classificationsystem, indicating both ATC code and ATC general class. Table 6 provides examples of medication prescriptions weextracted from a clinical document. The resulting XML file summarizes the results of the natural language processingmodules of our text-based CHA2DS2-VASc computation pipeline.
Table 6: Examples of prescriptions extracted from a clinical document and additional information for their concepts.
This computation is performed by an ontological reasoning module based on OWL and SWRL. [13] This score com-putation module completes our full CHA2DS2-VASc computation pipeline. In order to process the output of the NLPmodules, the ontological reasoning module focuses on the concept in the ontology given for each extracted concept(see Table 5), and on the ATC code given for each drug name (see Table 6).
We give in Table 7 an example of medical information extracted by our system, and the computed CHA2DS2-VAScpoint obtained for each extracted concept.
Table 7: Clinical information extraction processed by our NLP system.
Ant´ec´edents m´edicaux : HTA. [.] Traitement `a l’entr´ee : PREVISCAN (0-0-1/2). [.] Al’examen clinique de ce jour, il n’y a aucun signe d’insuffisance cardiaque.
Medical antecedents: high blood pressure. [.] Treatment on admission: PREVISCAN (0-0-1/2). [.] On today’s physical examination, there is no sign of cardiac failure.
Extracted concept: Our global corpus is composed of 62 files for patients that attended a cardiology hospital department. Each patient fileincludes clinical reports (neuro facial radiology and surgical reports), diagnoses (with the corresponding code), and alist of all medical procedures with the corresponding codes. We created a reference corpus composed of 21 patientfiles. First, the CHA2DS2-VASc score was automatically computed through our complete pipeline for each of the 62patients. The computed scores ranged from 0 to 7. We then selected at least 2 patients for each value of this score forthis reference corpus. This reference corpus was then read by a cardiologist who studied the source patient files andmanually recorded all the CHA2DS2-VASc criteria necessary to compute the CHA2DS2-VASc score. These criteriawere fed to the CHA2DS2-VASc computation module which produced the CHA2DS2-VASc scores for these patients.
These scores constitute the reference we aim to reproduce.
We evaluated the natural language processing pipeline at two levels. First, by comparing its computed criteria withthose obtained by human reading of the same clinical texts. Second, by assessing the impact of the observed differenceson the resulting CHA2DS2-VASc scores.
For each patient file of the reference corpus, we evaluated the performance of the natural language processing subsetof the pipeline by comparing its results with human-based results.
Table 8 displays the differences between the human-based values and the automatically computed values for thecriteria: 0 means identity while –1 or 2 is the difference between the two values. It lists, for the three patient files forwhich a difference was found, its identifier, the number of documents it contains, and the two information elementsobtained from the structured part of the record: Age and Sex (although both are generally listed in the texts, theirsystematic presence in the structured record makes it less useful to use the values found in the texts). It also shows thetwo CHA2DS2-VASc scores obtained through human (Hum) and fully automatic processing (Auto).
Table 8: CHA2DS2-VASc computed measures for both human-reading (Hum) and automatic method (Auto). CHFstands for “Congestive Heart Failure”, HTA for “Hypertension”, A2 for “Age>=75”, DIA for “Diabetes mellitus”, S2for “Stroke”, PAD for “Peripheral Artery Disease”, A for “65<=Age<75, and Sc for “Sex category”.
These results show that the CHA2DS2-VASc score computed using the automatically extracted medical concepts isvery close to the scores computed through human reading of each document. At the level of criteria, 21 × 8 = 168criteria had to be computed, among which 164 were exact. This corresponds to an accuracy of 164/168 = 0.976.
Over a total of 21 patients, 18 obtained the same CHA2DS2-VASc score through the fully-automatic and human-based methods, an accuracy of 0.857. Besides, all three different assessments of the score were distant of only onepoint. Moreover, all involved scores greater than two. All computed CHA2DS2-VASc scores vary from 0 to 7 in ourcorpus. Traditionally, cardiologists rely on three categories defined over the CHA2DS2-VASc score: a score greaterthan 2 (where anticoagulation therapy is permanent), a score of one (anticoagulation therapy is recommended), and anull score (anticoagulation therapy is not indicated). The three score errors computed a final score of 6 instead of 7(patient #57), a score of 5 instead of 6 (patient #59), and a score of 7 instead of 6 (patient #72). Indeed, computationalerrors are always serious in medicine, but in these cases, the alert raised for these patients is similar; they must takea treatment and the pacemaker alert must be considered as serious. Therefore, system response for all 21 patientswould be adequate in this test set: this means that system performance on this test set yields results identical to humanreading of the texts.
Closer analysis shows that for patient #59, the automatic approach missed a medical concept which counts for onepoint (peripheral artery disease): “ath´erome calcifi´e non st´enosant de la bifurcation” (non stenosing calcified atheromain the bifurcation) that does not exist in our lexicon; this problem can be easily solved by adding this concept to thelexicon. For patient #72, the automatic approach leads to one excess point in the score. Looking in more detail, wefind two errors: one error (linked to the criterion “congestive heart failure” that counts for one point in the score)concerns a medical concept that is not present in the lexicon: we have to add: “d´ecompensation cardiaque globale”(global cardiac decompensation); the second error concerns a concept that has been extracted as being present in thehistory of the patient “une avc qui n’a pas ´et´e confirm´ee” (a stroke that has not been confirmed) whereas this concept(stroke) is not present; this counts for 2 points in the computed CHA2DS2-VASc score. In consequence, we have toimprove the NegEx trigger list for French that we used to deal with this case of negation.
Looking at table columns, we can see that we missed one point twice in the criterion “Congestive Heart Failure” andone point in the criterion “Peripheral Artery Disease” due to medical concepts absent from our lexicon. We also addedtwo points through the criterion “Stroke” because of a problem in our negation processing module.
We performed an evaluation of the negation detection module on the corpus of 21 patients which represents 424 clinicalrecords to process. Over a total of 914 concepts, 59 are negated while 855 are not negated. Our system annotated 79concepts as negated (among which 53 are correct) and 835 as not negated (among which 809 are correct). We obtaineda global F-measure of 0.863.
Missing negations are due to non-existing trigger words in our lexicon (“non accompagn´ee de” not accompanied by)while false negations are due to an excessive factoring of the negation information from a concept to the followingone: “sans gradient intraventriculaire, insuffisance mitrale toujours minime” (without any intraventricular gradient,mitral insufficiency still negligible); in this case, the negation “sans” (without) only focuses on the first concept “gra-dient intraventriculaire” (intraventricular gradient) while our adaptation also tagged the second concept “insuffisancemitrale” (mitral insufficiency) as negated without taking into account the comma as a clue separating two phrases inthe sentence. Preventing the propagation of negation to several concepts is difficult because of the way some clinical records are written, especially when several successive concepts are listed without any punctuation mark to split theseentries.
Within the framework of the thromboembolism risk, the CHA2DS2-VASc score allows cardiologists to identify easilypotential stroke risk for patients with non-valvular fibrillation. We presented in this paper a system based on naturallanguage processing which automatically extracts medical concepts and prescriptions from French clinical records,taking into account the expressed negation for each concept. These are then used as a way to identify relevant criteriathat are part of the CHA2DS2-VASc score. When evaluating the NLP modules, the prescription extraction moduleobtained a global F-measure of 0.867 while the negation module obtained a global F-measure of 0.863.
We performed an overall evaluation on 21 patients files, based on a comparison of the computed CHA2DS2-VAScscore, one score being computed based on criteria extracted by a cardiologist reading the documents, the other scorebeing computed using the automatically extracted medical concepts. This evaluation showed similar results, with anaccuracy of 0.976 at the level of the 186 individual criteria and of 0.857 at the level of the CHA2DS2-VASc scores.
The observed differences are mainly due to two problems: first, a medical concept that is not present in a lexicon,and second, a lack of precision in negation handling. However, further consideration of practical implications of theobtained scores shows that the three differing scores remain in the same categories and that the same patients wouldraise alerts.
There is still room for improvement, especially for the adaptation of the NegEx algorithm and related linguistic re-sources to French language. This part of the work is crucial since a medical concept mentioned in the clinical documentcan be presented in a negative way (the problem is not present or the problem could occur under certain conditions),and could eventually change the patient results in a totally different way. We noticed that these assertion problems area key asset to access the meaning of clinical records; in 2010, one task from the i2b2/VA challenge focused on theassertion annotation in order to detect if a medical concept was present, absent, possible, hypothetical, conditional,or associated with someone else. [4] While participating in this challenge, [22] we obtained high results using machine-learning approaches; further work is needed to better adapt the method we used in this challenge from English toFrench.
This work has been funded by the Akenaton project under grant number ANR-07-TecSan-001.
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