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Extraction of Adverse Drug Effects from Medical Case Reports
Harsha Gurulingappa1 , Abdul-Mateen Rajput 2, and Luca Toldo 2∗ 1Molecular Connections Pvt. Ltd., Basavanagudi, Bangalore 560004, India 2Merck KGaA, Frankfurterstraße 250, Darmstadt 64293, Germany ABSTRACT
adverse drug reactions for automated signal generation in pharma- A sheer amount of information about adverse effects of drugs are covigilance has already been proposed (Henegar et al., 2006) and published in medical case reports that pose major challenges for drug its application to information retrieval has been exploited by the safety experts to perform timely monitoring. Efficient strategies for same group few years later, in the VIGITERMES project (Dela- identification and extraction of information about adverse drug effects marre et al., 2010), where the OntoEIM adverse event ontology from free-text resources are needed to support pharmacovigilance have been used to extend the dictionary of adverse event entities, research and decision making. Therefore, this work focusses on the normalize queries, and consolidate annotations, delivering 29% pre- adaptation of a machine learning-based relation extraction system for cision and 67% recall of MEDLINE abstracts. Automatic extraction the identification and extraction of drug-related adverse effects from of adverse drug effects from clinical records is an active area of rese- MEDLINE case reports. It relies on a high quality corpus that was arch (Aramaki et al., 2010). Mining social internet message boards manually annotated, using ontology-driven methodology. Qualitative to identify adverse drug reactions has been reported (Benton et al., evaluation of the system show robust results.
2011), whereby in that work the extraction of event - drug pairs wasdetermined only using co-occurrence of terms within a window of INTRODUCTION
20 tokens apart, and the use of machine learning systems was only Adverse effects of drugs is a bothersome issue that confronts drug focused on deidentification for privacy protection. This work reports manufacturers, healthcare providers, and regulatory authorities.
on the adaptation of a machine learning-based system for identifying Stringent measures for capturing the risks associated with drug the relations between drugs and adverse effects in MEDLINE case usage are established in forms of spontaneous reporting systems that reports, that relies on an ontology-driven manually annotated cor- are timely analyzed to ensure safe use of drugs (Hauben and Bate, pus, that strictly follows semantic annotation guidelines developed 2009). Amongst various data sources used by drug safety experts to for clinical text (Roberts et al., 2009). The system has been qua- perform the safety monitoring, case reports published in the sci- litatively evaluated and studied for its ability of support real time entific biomedical literature represent an important resource due to their abundant existence, rapid rate of generation, and valuableinformation enclosed (Vandenbroucke, 2001). Due to their unstru-ctured nature, however, manual analysis of the scientific literature is challenging, cumbersome, and labor intensive.
In recent years, development of automatic natural language pro- cessing (NLP) and information extraction (IE) techniques have gai- The data set used for training and validation of the relation extra- ned immense popularity. They include identification of biomedical ction system is the ADE corpus (Gurulingappa et al., 2012). The named entities, relations between the entities, or events associa- ADE corpus contains 2972 MEDLINE case reports that are manu- ted with them. Noticeable efforts have been invested on mining the ally annotated and harmonized by three annotators. The corpus adverse effects in different forms of free-text data. Examples include contains annotations of 5063 drugs, 5776 conditions (e.g. diseases, Wang et al., 2009 who applied the MedLEE system on discharge signs, symptoms), and 6821 relations between drugs and conditi- summaries to identify medication events and entities that could be ons representing clear adverse effect implications. All annotations potential adverse entities that were detected using the strength of are confined to sentence level i.e. drugs and conditions represen- statistical association based on their co-occurrences. Leaman et al., ting adverse effects co-occurring only within individual sentences 2010 proposed a lenient NLP model for extracting adverse effects are annotated. Drugs and conditions that do not fall into adverse of drugs from social media such as blogs. Gurulingappa et al., 2011 effect relations are not annotated. This was done in accordance to developed a machine learning-based system for classifying the sen- tences in MEDLINE case reports that assert adverse effects of drugs.
The ADE corpus contains annotations of relations between drugs However, according to the author’s knowledge, there is a limited and conditions that represent True relations. This represents a spar- focus on identification of semantic relationships between drugs and sely annotated dataset. For training a supervised classifier, it was adverse effects in text. This is partly due to the unavailability of sui- essential to generate False relations i.e. drugs and conditions that do table public corpora that could be used for technology development not fall into adverse effect relations. For this purpose, ProMiner, a and benchmarking. Extracting relations between drugs and adverse dictionary-based named entity recognition system (Hanisch et al., effects can facilitate appropriate indexing, precise searching, visu- 2005) was employed. ProMiner was incorporated with DrugBank alization, and faster information tracing. The use of ontology of (Knox et al., 2011) and MedDRA (Merrill, 2008) dictionaries for theidentification of drugs and conditions respectively in the ADE corpus ∗Corresponding author: [email protected]; Gurulingappa and that were previously not annotated by human annotators. As a result of named entity recognition, new instances encompassing 2269 Gurulingappa et al
Table 1. Counts of entities and relations in ADE-EXT corpus subsets.
drugs and 3437 conditions were automatically annotated. Drug-condition pairs co-occurring within sentences that were previouslynot annotated by humans formed False relations. Altogether, 5968False relations were automatically generated. The corpus enrichedwith machine annotated drugs, conditions, and relations betweenthem is referred as ADE-EXT (indicating extended ADE corpus).
Figure 1 shows an illustration of True and False relations between Fig. 2. Ontologies discussed in this work (from (Roberts et al., 2009) and drug and conditions co-occurring within a sentence.
Mapping annotation ontology against Ontology ofAdverse Events The CLEF initiative (Roberts et al., 2007) investigated how togenerate semantically annotated medical corpora for information Fig. 1. Example of a sentence annotated with drug, conditions, and extraction. As described (Gurulingappa et al., 2012) we adopted relations between them. True indicates presence of adverse effect relation the standard established by the CLEF framework for the annotation and False indicates absence of adverse effect relation.
workflow (Roberts et al., 2009) however we reshaped the annota-tion schema by using only two of the original entities (CONDITION, DRUG) and extended it with a third one (DOSAGE). None of the In the ADE-EXT corpus, 120 manually annotated True relations relationships used by the CLEF annotation schema could be reused were not suitable for the NLP task. Examples include overlapping for our work, since the CLEF annotation schema did not consider inter-related entities such as acute lithium toxicity where lithium adverse drug reactions, instead we created two relations: DRUG- is related to acute toxicity. After removal of nested annotations, CAUSE-CONDITION, DRUG-HAS-DOSAGE. In this work we focused the ADE-EXT corpus was decomposed into a training set (ADE- only on automating the detection of DRUG-CAUSE-CONDITION thus EXT-TRAIN) and a test set (ADE-EXT-TEST). Counts of entities and DOSAGE will not be mentioned further. The ADE corpus has been relations in subsets of ADE-EXT corpora is shown in Table 1.
created using the Knowtator plugin for Prot´eg´e (Ogren, 2006), anontology-driven corpus annotation tool also used for the creation of the CLEF corpus. Although we adopted the same tool used in CLEF For the identification and extraction of drug-condition entity pairs and also adopted the standard established by the CLEF framework that fit into adverse effect relation, the Java Simple Relation Extra- for the annotation workflow, we could not adopt the same annota- ction (JSRE) system (Giuliano et al., 2007) was employed. JSRE tion ontology since the latter was not able to capture the adverse provides a re-trainable and scalable supervised classification plat- drug relation and the drug dosing relation. The annotation onto- form that uses Support Vector Machines (SVMs) (Burges, 1998) logy described above was therefore used to create the ADE corpus.
with different kernels specially designed for the NLP and relation Subsequent to the corpus creation, the realism-based biomedical extraction. All sentences in ADE-EXT-TRAIN and ADE-EXT-TEST ontology for representation of adverse events (AEO) has been publi- containing drug-condition pairs labeled as either True or False were shed (Yongqun et al., 2011). AEO has been developed following the transformed into the SRE format before subjecting them to rela- principles of Ontological Realism, thus is aligned with the Basic tion extraction. The SRE format is a unique way of representing Formal Ontology and the Relation Ontology, and with the Open data within the JSRE platform where tokens appearing in sente- Biological and Biomedical Ontologies (OBO) Foundry principles nces are enriched with their parts-of-speech tags, lemmas, and flags of openness, collaboration and use of a common shared syntax.
indicating if a token is a part of named entity or not. Amongst diffe- AEO has 484 representational units, annotated by means of 369 rent kernels available, the shallow linguistic kernel was thoroughly terms with specific identifiers and 115 terms imported from existing used since it has been widely applied and has shown success during ontologies. The use of ontologies has proven of great value in bio- similar relation extraction tasks (Tikk et al., 2010). The ADE-EXT- medicine, also since it enable machine reasoning, abstraction and TRAIN was used as data for training and cross-evaluation of JSRE automatic hypothesis generation. We therefore had interest in inve- whereas the ADE-EXT-TEST was used an independent test set.
stigating if the knowledge encoded in the annotations of the ADE Extraction of Adverse Drug Effects
Table 2. Assessment of results of relation corpus could be semantically connected to the AEO. For doing this, we manually compared the definitions of the entities of AEO and of Table 3. Impact of the size of the training set.
ADE annotation ontology. Figure 2 shows the basic design patternsof AEO, ADE and CLEF as from the original papers, emphasizingshared entities using green and red colours.
Mapping the ADE Annotation Ontology to the As clearly shown in Figure 2, both the ADE annotation ontology The performance of relation extraction was evaluated by 10-fold and the Ontology of Adverse Events represent adverse drug rea- cross-validation of the training data. During cross-validation of the ctions using formal ontological methods. Inspite of this common training data and final evaluation over the test set, classification goal, the two ontologies use different naming for the two core enti- performances were assessed using the F-score over True-labeled ties: a CONDITION in the ADE annotation ontology coincide with a relations since they denote adverse effect relations between drugs DRUG ADVERSE EVENT in AEO; a DRUG in the ADE annotation and conditions that denote a focussed relation class being studied.
ontology coincide with a DRUG-ADMINISTRATION in AEO. The ADE ontology additionally introduce the entity DOSAGE, not spe- cified in AEO at the time of its development since AEO originallyfocused on vaccines for which dosing is not an essential medical Baseline experiments began with training and cross-validation of concept. Both ADE and AEO model a causal relationship between JSRE over the ADE-EXT-TRAIN corpus. Results of system’s per- CONDITION OR ADVERSE EVENT and DRUG OR MEDICAL INTE- formances are shown in Table 2. The system achieved an overall RVENTION , with the latter being the causal source. The only entity F-score of 0.87 after cross-validation. Upon the final test over shared by the CLEF annotation ontology with AEO and ADE is ADE-EXT-TEST, the system attained F-score of 0.87 indicating a the DRUG-OR-DEVICE, that coincide with a DRUG OR MEDICAL consistency in classification. A subset of instances misclassified during the cross-validation and testing were manually investiga-ted to understand the common sources of errors. Limited contextappeared to be one reason for misclassification. For example, the CONCLUSION
title Niacin maculopathy (PMID:3174043) infers maculopathy as This work reports on the adaptation of a machine learning-based an adverse effect of niacin that lacks contextual description to JSRE system for the identification and extraction of adverse effe- support machine classification. Distantly co-occurring inter-related cts of drugs in case reports. A methodology has been discussed to entities constituted couple of errors. For example, in the sentence enrich a sparsely annotated corpus and its subsequent use to build CASE SUMMARY: A 65-year-old patient chronically treated with a classification model. Evaluation of the system’s performance sho- the selective serotonin reuptake inhibitor (SSRI) citalopram develo- wed promising results. Performance of the system can be improved ped confusion, agitation, tachycardia, tremors, myoclonic jerks and in several ways. In the current experiments, only the default features unsteady gait, consistent with serotonin syndrome, following initi- acceptable by JSRE were used. Optimization of feature represen- ation of fentanyl, and all symptoms and signs resolved following tation to include additional features for instance from syntactic discontinuation of fentanyl (PMID:17381671); the relation betw- sentence parse trees may further improve the results. Development een confusion and the last appearing drug name fentanyl was not of additional strategies like post-processing to classify relations with missing contextual descriptions can help to recover more relations.
The reported experimental results denote research status on Impact of Size of the Training Set on the adverse drug effect identification from text. There are several strate- gies that will be immediately followed. The authors plan to bench- In order to study the impact of size of the training data on per- mark the performances of several named entity taggers against the formance of classification, the ADE-EXT-TRAIN was decomposed ADE corpus for the identification of drugs and conditions mentions into random subsets containing 10, 20, 50, 100, 200, 500, 1000, and 2000 documents. The JSRE was trained on these subsets The current experiments have been performed on the ADE cor- independently in different rounds and subsequently applied on the pus, since that was the only one available when this work was done, ADE-EXT-TEST for performance evaluation. Table 3 shows that alre- however while writing this report a new corpus has been published, ady using 500 documents one could achieve performances in the namely the EU-ADR corpus(van Mulligen et al., 2012). It will be 80% range. Whereby, to reach a classifier with a standard deviation interesting to see if the performance of JSRE on the ADE corpus will of 1%, one needs a substantially large training data.
be different compared to the EU-ADR corpus.
Gurulingappa et al
Similarly, benchmarking results of commercial and public rela- extraction of drug-related adverse effects from medical case reports. Journal of tion extraction systems such as SemRep, Luxid MER Skill Hanisch, D., Fundel, K., Mevissen, H.-T., Zimmer, R., and Fluck, J. (2005). Prominer: R , RelEx, MedScan will be performed. The outcome of rule-based protein and gene entity recognition. BMC Bioinformatics, 6 Suppl 1, relation extraction from text to support signal detection and identify potentially novel or under-reported adverse effects will be studied.
Hauben, M. and Bate, A. (2009). Decision support methods for the detection of adverse The use of ontologies for driving information extraction has been events in post-marketing data. Drug Discov Today, 14(7-8), 343–357.
reported (Wimalasuriya and Dou, 2010; Pandit and Honavar, 2010), Henegar, C., Bousquet, C., Lillo-Le Louet, A., Degoulet, P., and Jaulent, M.-C. (2006).
Building an ontology of adverse drug reactions for automated signal generation in we plan to explore the use of various available tools (e.g. ODIE, pharmacovigilance. Computers in Biology and Medicine, 36, 748–767.
semantixs) using the AEO ontology and compare the performance Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, of the ontology driven methods against the method presented here.
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