Contempré, Edeline

Szlávik, Zoltán

ten Teije, Annette

Tiddi, Ilaria

Velazquez Godinez, Erick

English

We are presenting preliminary results of a work-in-progress project that aims to increase healthcare professionals’ trust in treatment search engines by introducing a) explainability based re-ordering of re- trieved documents, and b) providing user-friendly explanations for each of these documents. Through the use of crowdsourcing, we assess the importance of various features for explainability, and also investigate as- pects of explanation formulation as presented to end-users. Our results allow us to determine feature weights that will be inputs to the document re-ordering model, the per-document explanatory sentence formulation module, and the sentence ordering model

VU Research Portal

VU Research PortalTowards explained treatment search results: feature analysis and explanationformulationContempré, Edeline; Szlávik, Zoltán; Velazquez Godinez, Erick; ten Teije, Annette; Tiddi,Ilaria2021document versionPeer reviewed versionLink to publication in VU Research Portalcitation for published version (APA)Contempré, E., Szlávik, Z., Velazquez Godinez, E., ten Teije, A., & Tiddi, I. (2021). Towards explained treatmentsearch results: feature analysis and explanation formulation. 36. Paper presented at First InternationalWorkshop on eXplainable AI in Healthcare , Porto, Portugal.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 12. Dec. 2021Towards explained treatment search results:feature analysis and explanation formulationEdeline Contempré1,2[0000−0002−1767−121X], ZoltánSzlávik1[0000−0002−2781−3795], Erick Velazquez Godinez1[0000−0001−9449−8265],Annette ten Teije2[0000−0002−9771−8822], and Ilaria Tiddi2[0000−0001−7116−9338]1 myTomorrows, Anthony Fokkerweg 61, 1059 CP Amsterdam, The Netherlands{edeline.contempre,zoltan.szlavik,erick.velazquez}@mytomorrows.com2 Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, TheNetherlands {annette.ten.teije,i.tiddi}@vu.nlAbstract. We are presenting preliminary results of a work-in-progressproject that aims to increase healthcare professionals’ trust in treatmentsearch engines by introducing a) explainability based re-ordering of re-trieved documents, and b) providing user-friendly explanations for eachof these documents. Through the use of crowdsourcing, we assess theimportance of various features for explainability, and also investigate as-pects of explanation formulation as presented to end-users. Our resultsallow us to determine feature weights that will be inputs to the documentre-ordering model, the per-document explanatory sentence formulationmodule, and the sentence ordering model.Keywords: Explainability · Feature analysis · Healthcare · Treatmentsearch.1 IntroductionHealthcare professionals (HCPs) increasingly rely on Artificial Intelligence(AI) models to help their patients and save their lives. This could especiallybe the case for HCPs referring patients (with often short prognosis) to clinicaltrials as, for these patients, no treatments might be available on the market.While various relevant AI models’ accuracy keeps increasing, their underlyingprocesses, and consequently, their outcomes, are becoming increasingly difficultto understand. In the medical domain, where the pressure to make no mistakes ishigh, not being sure why and how a decision is made could have dire consequencesfor everyone involved. Consequently, without understanding an AI model and itsoutput, HCPs’ trust in the model will decrease, potentially leading to abandoningits use [1].In this paper, we present the first results from a work-in-progress projectin which we aim to develop a local explainability [2] based method, that a) re-sults in a model that, based on explainability, re-orders documents retrievedby a treatment search engine, and b) provides an ordered list of explanatorysentences to end-users (HCPs) for each retrieved document. We believe that,2 E. Contempré et al.through enabling HCPs to efficiently find relevant clinical trials while under-standing search results, we can increase their trust in the search engine, andultimately facilitate the process of getting more patients treated.The paper focuses on the first stage of the research, which – through the useof crowdsourcing – identifies and assesses i) the importance of features used forexplainability, and ii) the formulation of explanations as presented to end-users.2 Explainability features, in contextOur method will generate local explainability scores for each result retrievedby a search engine, and use these scores to re-order the search engine’s results.For each clinical trial in the result list, users will also be shown user-friendlyformulated sentences as explanations providing descriptions of features playinga role in the retrieval process. We rely on a set of user-interpretable features forboth re-ordering and explanatory sentences (see Figure 1 for the pipeline).Fig. 1. Explainability pipeline overview.We determined the individual features based on related work on features usedfor learning-to-rank (e.g., [3]), the LIRME method [4], earlier work within thecompany [5], and conversations with company UX designers and stakeholders.Features can be classified into categories based on whether their value dependson the search query, and their output type (i.e. binary – receives a score of 1 ifpresent or 0 if not, or numeric – a count that will be normalised). An overview ofall selected features is shown in Table 1. For example, the feature Query in titleis a query dependent feature as its value depends on a match between the query(e.g. breast cancer) and the title. In contrast, Number of publications associatedwith a clinical trial remains the same regardless of the user’s query, and can takeup a value larger than one.Query dependent Query independentBinary Query in title, Preferred term in ti-tleClinical stage present, Stage is re-cruiting, Overall status givenNumeric Query in summary, Preferred termin summary, Preferred term in sum-mary, Query in detailed descriptionNumber of publicationsTable 1. Classification of features created for the local explainability based searchengine.Towards explained treatment search results 3Our explainability method does not depend on the exact method used forthe search engine retrieving relevant trials, though naturally there is overlap be-tween what both methods make use of. The search engine used for this research3extracts properties of clinical trials such as the trial’s title, trial stage, summary,etc., and uses these to match trials to queried medical conditions. The extractionprocess relies on a condition-focused knowledge graph based on UMLS4 and Dis-ease Ontology5. We also use PubMed6 abstracts for some features, and processabout 400.000 trials from clinical trial registries such as clinicaltrials.gov7.3 Crowdsourced feature importanceIn order to combine features to eventually re-order search results, we decidedto determine features’ weights based on their perceived importance, throughtheir corresponding explanatory sentences, by users. Weights for features mayalso be used to rank explanatory sentences for each retrieved trial.As no data was available on users’ interaction with the search engine, andno real-time user-interaction data could be collected for privacy reasons, wewere not able to infer weights through search engine usage. Our solution was touse an implicit preference elicitation strategy via crowdsourcing, as – althoughHCPs were the target audience for this research – earlier studies employingcrowdsourcing tasks have shown that lay participants’ annotation reach similarlyhigh levels of quality output compared to that of niche-sourcing with medicalexperts [6, 7].Our hypotheses were twofold: a) features are not equally preferred by users,and b) formulation of explanations makes a difference. More specifically, weare interested in three dimensions of sentence formulation: numeric vs. non-numeric (‘5 times’ vs. ‘multiple times’ mentioned in explanatory sentences),action-oriented vs. fact-driven formulations (‘retrieved’ vs. ‘clearly mentioned’ ),and disease specific vs. non-disease specific outputs (‘HIV’ vs. ‘condition’ ).To address these, we created a labeling job that simulated the context of thesearch engine of myTomorrows, and ran these via Amazon Sagemaker GroundTruth (which uses Amazon Mechanical Turk). Crowd workers were asked toimagine themselves in a situation where they had a friend or family memberwith one of the four randomly allocated conditions: invasive breast cancer, Lymedisease, HIV, or chronic migraine. Furthermore, they were asked to imaginethemselves querying the condition in a search engine containing clinical trials,and to rate sentences based on how convincing a sentence was to make themwant to read clinical trial in further detail and more closely assess its relevance.Note that due to the difficulty of understanding clinical trial documents by laypeople [8], participants were not asked to look at such documents.3 https://search.mytomorrows.com/public4 https://www.nlm.nih.gov/research/umls5 https://disease-ontology.org/6 https://pubmed.ncbi.nlm.nih.gov/7 https://www.clinicaltrials.gov/4 E. Contempré et al.We created different sentences for the four conditions, sentences for the 10features (see table 1), using the three dimensions of formulation, and presentedthese to workers in a random order. In addition, we employed ‘gold questions’used in crowdsourcing to control for quality.4 Results and discussionThrough crowdsourcing, we obtained 1116 responses to our labeling task.Each feature was attributed 16 sentences where each single sentence’s task wasanswered by 9 workers at a time (the maximum number per task on the plat-form). This way we collected n = 144 data points per feature. Numeric, non-numeric, retrieve and clearly mentioned entities were each assessed by n = 144workers. Disease specific and disease unspecific entities were each assessed by n= 198 workers. 10% of workers answering the gold question were discarded assuspected low quality workers.Data obtained with respect to feature importance shows that there isat least a partial ordering that can be obtained via the crowdsourcing (basedon statistical tests). The feature with the highest mean score (3.69, on a 5-point Likert scale) was Query in detailed description, whereas the two leastconvincing features were Query in title, and Trial is recruiting (3.15, and 3.13,respectively). Our interpretation of these results is that features that peopleaccept as more intuitive bring less convincing power, i.e. explainability: throughweb search, people are used to query words appearing in the title, and peopletend to be looking for trials that are recruiting at the moment. We determinedthe features’ weights, using chi-square tests, based on statistical values. If twofeatures were, for example, not statistically equally preferred, these two featureswould be attributed different weights.When it comes to results for the three formulation dimensions (Table 2),when performing chi-square tests, we found that: 1. users prefer explanationswithout specific numbers mentioned (e.g. query term occurring ‘multiple times’vs. ‘5 times’ ). This may indicate that users are used to words and are, therefore,more convincing than those that require more mental processing, i.e. when spe-cific numbers are displayed. 2. Users seem to prefer facts (‘clearly mentioned’ )to actions related to the search procedure (‘retrieved’ ). 3. There is no preferencedifference between being specific about the condition in the query (e.g. ‘HIV’ )vs. (‘the condition’ ). This is in line with the finding in the previous paragraphthat what is obvious, i.e. that we are looking for documents containing the queryterm, need not be mentioned in detail in an explanation.The results provided us with guidance for a) which features are consideredmore important in re-ordering search results based on explainability, b) the for-mulation of explanatory sentences, and c) ordering explanatory sentences forindividual retrieved documents. The formulation point is particularly interest-ing for future work, as it suggests that a considerable amount of research is stillneeded on how search experience should be designed. To the best of our knowl-edge, no related work has been done on the type of explanations users wouldlike to see associated with retrieved documents in the medical domain.Towards explained treatment search results 5Entity x(1) x(2) P-value(1) Non-numerical(2) Numerical3.7 3.34 0.01*(1) Clearly mentioned(2) Retrieved3.65 3.33 0.036*(1) Specify disease(2) Not specify disease3.4 3.48 0.44Table 2. Preferences per formulation dimension (5-point Likert scales).5 Conclusions and future workIn this paper we have briefly presented a preliminary study towards re-ordering documents retrieved by a treatment search engine, and providing end-user-friendly explanations for each document retrieved. We used crowdsourcingto assess both feature importance, and user preferences in terms of the formula-tion of explanatory sentences.The next step in our work is to implement an ‘explanation engine’ attachedto a search system, and measure the impact of both the re-ordering, and theexplanations themselves. If privacy policy allows, we would, in addition, like touse results from this paper to address the cold start problem for an explanationengine that learns through user interaction.References1. R. H. Kay, “The relation between locus of control and computer literacy,” Journalof Research on Computing in Education, vol. 22, no. 4, pp. 464–474, 1990.2. A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on ExplainableArtificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52 138–52 160, 2018.3. T. Joachims, “Optimizing search engines using clickthrough data,” in Proceedingsof the eighth ACM SIGKDD international conference on Knowledge discovery anddata mining, 2002, pp. 133–142.4. M. Verma and D. Ganguly, “LIRME: locally interpretable ranking model explana-tion,” in Proceedings of the 42nd International ACM SIGIR Conference on Researchand Development in Information Retrieval, 2019, pp. 1281–1284.5. S. Gu, “Improving search relevance feedback through human centered design,” Mas-ter’s thesis, TU Delft, The Netherlands, 2020.6. A. Dumitrache, L. Aroyo, C. Welty, R.-J. Sips, and A. Levas, “Dr. Detective: com-bining gamification techniques and crowdsourcing to create a gold standard in med-ical text,” in Proceedings of the 1st International Conference on Crowdsourcing theSemantic Web, vol. 1030, 2013.7. A. Dumitrache, L. Aroyo, and C. Welty, “Crowdsourcing ground truth for medicalrelation extraction,” ACM Trans. Interact. Intell. Syst., vol. 8, no. 2, Jul. 2018.[Online]. Available: https://doi.org/10.1145/31528898. D. T. Wu, D. A. Hanauer, Q. Mei, P. M. Clark, L. C. An, J. Proulx, Q. T. Zeng,V. V. Vydiswaran, K. Collins-Thompson, and K. Zheng, “Assessing the readabilityof clinicaltrials. gov,” Journal of the American Medical Informatics Association,vol. 23, no. 2, pp. 269–275, 2016.

Towards explained treatment search results: feature analysis and explanation formulation

https://research.vu.nl/files/137579653/XAI_Healthcare_2021_paper_7_2.pdf

Towards explained treatment search results: feature analysis and explanation formulation

Abstract

Similar works

Full text

Available Versions

VU Research Portal