Description of the PatientGenesys Dialogue System

Llanos, Leonardo Campillos; Bouamor, Dhouha; Bilinski, Eric; Ligozat, Anne-Laure; Zweigenbaum, Pierre; Rosset, Sophie

doi:10.18653/v1/w15-4660

Cited by 7 publications

(6 citation statements)

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“…The medical domain stands to benefit considerably from automated simplification: The medical literature is vast and technical, and there is a need to make this accessible to non-specialists (Kickbusch et al, 2013). Some research uses those medical documents and deploys various simplification methods based on lexical and syntactic simplification (Damay et al, 2006;Kandula et al, 2010;Llanos et al, 2016). The recent release of the Cochrane dataset provided a new parallel corpus of technical and lay overview of published medical evidence (Devaraj et al, 2021a).…”

Section: Text Simplificationmentioning

confidence: 99%

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

Lu,

Li,

Wallace

et al. 2023

Findings of the Association for Computational Linguistics: EACL 2023

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Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%∼4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS.

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Section: Text Simplificationmentioning

confidence: 99%

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

Lu,

Li,

Wallace

et al. 2023

Findings of the Association for Computational Linguistics: EACL 2023

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“…We processed each question with our VP dialogue system (Campillos et al, 2015). Then, we manu- ally labelled the output of question analysis, based on our knowledge of the dialogue system, into questions that should be processed by rule-based processing (RBPS) and questions requiring some other processing strategy (OPS).…”

Section: Data Preparationmentioning

confidence: 99%

“…Questions to a virtual patient have been addressed by mapping the user input to a set of predefined questions (Jaffe et al, 2015), as is done in a large subset of recent general-domain QA work which queries lists of frequently asked questions (FAQs) and returns their associated predetermined answers (Leuski et al, 2006;Nakov et al, 2016). Our setting is different in two ways: first, we do not rely on a FAQ but instead generate answers based on the question and on the contents of the virtual patient's record; second, we already perform fine-grained question classification with a rule-based system (Campillos et al, 2015), and aim to determine whether a given question should be referred to this rule-based strategy or deserves to be handled by a fallback strategy.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of doctor-patient questions for a virtual patient record query task

Llanos¹,

Rosset²,

Zweigenbaum³

2017

BioNLP 2017

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We present the work-in-progress of automating the classification of doctorpatient questions in the context of a simulated consultation with a virtual patient. We classify questions according to the computational strategy (rule-based or other) needed for looking up data in the clinical record. We compare 'traditional' machine learning methods (Gaussian and Multinomial Naive Bayes, and Support Vector Machines) and a neural network classifier (FastText). We obtained the best results with the SVM using semantic annotations, but the neural classifier achieved promising results without it.

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“…Our setting is different in two ways: first, we do not rely on a FAQ but instead generate answers based on the question and on the contents of the virtual patient's record; second, we already perform fine-grained question classification with a rule-based system (Campillos et al, 2015), and aim to determine whether a given question should be referred to this rule-based strategy or deserves to be handled by a fallback strategy.…”

Section: Effect Of Pre-trained Word Vectorsmentioning

confidence: 99%

BioNLP 2017

Cohen¹,

Demner‐Fushman²,

Ananiadou³

et al. 2017

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According to the Association for Computational Linguistics guidelines on special interest groups (SIGs), The function of a SIG is to encourage interest and activity in specific areas within the ACL's field [1]. Is the SIGBioMed special interest group "within the ACL's field"? The titles of this year's papers suggest that it is, in that the current interest in deep learning in its many and varied manifestations is mirrored in those titles. Do those papers cover a specific area? They do, and in doing so, they demonstrate one of the great satisfactions of working in biomedical natural language processing.One of the joys of involvement in the biomedical natural language processing community is seeing the development of research with clinical applications. As examples of such work being presented at BioNLP 2017, we would like to point out the two papers that discuss the application of natural language processing to the diagnosis of neurological disorders. Bhatia et al. [2] describe an approach to using speech processing in the assessment of patients with amyotrophic lateral sclerosis (also known as Lou Gehrig's disease), one of the more horrific motor neuron diseases. Good assessment of amyotrophic lateral sclerosis patients is important for a number of reasons, including the fact that accurate tracking of the inevitable deterioration that is a hallmark of this disease gives patients and their families the possibility of purposeful planning for the attendant disability and death. However, current methodologies for evaluating the status of amyotrophic lateral sclerosis patients necessarily involve expensive equipment and highly trained personnel; when further developed, this methodology could make such evaluation much more, and more frequently, available to ALS patients. The fact that the work reported here involves a speech modality is especially exciting, as speech-related indicators of future ALS can be present long before diagnosis. The paper uses measurements of phonological features of speech and their divergence from a baseline, and demonstrates correlation with physiological measures.Adams et al. [3] describe work on detecting and categorizing word production errors associated with anomia, a particular kind of inability to find words. Screening for anomia is important because anomia is a symptom of stroke, but it is difficult and time-consuming to do, and therefore is not done as often as it should be. Automatic detection of anomia could be a nice enabler of improved care for stroke victims, but it is made difficult due to the subtlety of the phonological and semantic judgments that have to be made when assessing the phenomenon. The paper uses a combination of language modeling and phonologically-based edit distance calculation to approach the task, applying these techniques to data from the AphasiaBank collection of transcribed aphasic and healthy speech.Although we have summarized only these two examples that address neurological disorders, there are several other papers on the use of natural language proc...

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Description of the PatientGenesys Dialogue System

Abstract: This paper describes the work-in-progress prototype of a dialog system that simulates a virtual patient (VP) consultation. We report some challenges and difficulties that are found during its development, especially in managing the interaction and the vocabulary from the medical domain.

Cited by 7 publications

References 10 publications

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

Automatic classification of doctor-patient questions for a virtual patient record query task

BioNLP 2017

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