Overview of BioASQ 2021: The Ninth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Nentidis, Anastasios; Katsimpras, Georgios; Vandorou, Eirini; Krithara, Anastasia; Gascó, Luis; Krallinger, Martin; Παλιούρας, Γεώργιος

doi:10.1007/978-3-030-85251-1_18

Cited by 23 publications

(21 citation statements)

References 13 publications

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“…The tenth edition of the BioASQ challenge consisted of four tasks: (1) a largescale biomedical semantic indexing task (task 10a), (2) a biomedical question answering task (task 10b), (3) task on biomedical question answering on the developing problem of COVID-19 (task Synergy), all considering documents in English, and (4) a new medical semantic annotation and concept normalization task in Spanish (DisTEMIST). In this section, we first describe the two established tasks 10a and 10b with focus on differences from previous versions of the challenge [25]. For a more detailed description of these tasks the readers can refer to [32].…”

Section: Overview Of the Tasksmentioning

confidence: 99%

“…So that the experts receive and assess the systems' responses and their assessment is fed back to the systems in order to help improving them, in a continuous iterative process. In this direction, last year we introduced the BioASQ Synergy task [25] envisioning a continuous dialog between the experts and the systems. In this model, the experts pose open questions and the systems provide relevant material and answers for these questions.…”

Section: Task Synergymentioning

confidence: 99%

“…and are pre-trained on the 2022 baseline set of all PubMed abstracts provided by the National Library of Medicine and fine-tuned using pairs of relevant and non-relevant question-abstract pairs generated using the BioASQ9 dataset [25].…”

Section: Task 10bmentioning

confidence: 99%

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Overview of BioASQ 2022: The Tenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Nentidis

Katsimpras²,

Vandorou³

et al. 2022

Lecture Notes in Computer Science

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This paper presents an overview of the tenth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2022. BioASQ is an ongoing series of challenges that promotes advances in the domain of large-scale biomedical semantic indexing and question answering. In this edition, the challenge was composed of the three established tasks a, b and Synergy, and a new task named DisTEMIST for automatic semantic annotation and grounding of diseases from clinical content in Spanish, a key concept for semantic indexing and search engines of literature and clinical records. This year, BioASQ received more than 170 distinct systems from 38 teams in total for the four different tasks of the challenge. As in previous years, the majority of the competing systems outperformed the strong baselines, indicating the continuous advancement of the state-of-the-art in this domain.

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Section: Overview Of the Tasksmentioning

confidence: 99%

Section: Task Synergymentioning

confidence: 99%

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Overview of BioASQ 2022: The Tenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Nentidis

Katsimpras²,

Vandorou³

et al. 2022

Lecture Notes in Computer Science

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“…BIOASQ The BIOASQ (Task A: Large-Scale Online Biomedical Semantic Indexing) dataset (Tsatsaronis et al, 2015;Nentidis et al, 2021) comprises biomedical articles from PubMed, 7 annotated with concepts from the Medical Subject Headings (MeSH) taxonomy. 8 MeSH is a controlled and hierarchically-organized vocabulary produced by the National Library of Medicine.…”

Section: Datasetsmentioning

confidence: 99%

Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting

Chalkidis¹,

Søgaard²

2022

Preprint

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In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e.g., policy changes, conflicts, or pandemics. Class imbalance and drift can sometimes be mitigated by resampling the training data to simulate (or compensate for) a known target distribution, but what if the target distribution is determined by unknown future events? Instead of simply resampling uniformly to hedge our bets, we focus on the underlying optimization algorithms used to train such document classifiers and evaluate several group-robust optimization algorithms, initially proposed to mitigate grouplevel disparities. Reframing group-robust algorithms as adaptation algorithms under concept drift, we find that Invariant Risk Minimization and Spectral Decoupling outperform samplingbased approaches to class imbalance and concept drift, and lead to much better performance on minority classes. The effect is more pronounced the larger the label set.

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“…Summarization in the Medical Domain Summarization has been used to address various problems in the field of medicine. These include electronic health record summarization (Liang et al, 2019), medical report generation (Zhang et al, 2019;Liu et al, 2021), medical facts generation (Wallace et al, 2021;Wadden et al, 2020) and medical question answering (Demner-Fushman and Lin, 2006;Nentidis et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization

Katsimpras¹,

Παλιούρας²

2022

Preprint

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Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial. Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study. Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention. Then, an evidence sentence, which conveys information about the effectiveness of the intervention, is extracted automatically from each abstract. Based on the set of evidence sentences extracted from the abstracts, a short summary about the intervention is constructed. Finally, the produced summaries are used to train a BERT-based classifier, in order to infer the effectiveness of an intervention. To evaluate our proposed method, we introduce a new dataset which is a collection of clinical trials together with their associated PubMed articles. Our experiments demonstrate the effectiveness of producing short informative summaries and using them to predict the effectiveness of an intervention.

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Overview of BioASQ 2021: The Ninth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Cited by 23 publications

References 13 publications

Overview of BioASQ 2022: The Tenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Overview of BioASQ 2022: The Tenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting

Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization

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