What Makes a Top-Performing Precision Medicine Search Engine?  Tracing Main System Features in a Systematic Way

Faessler, Erik; Oleynik, Michel; Hahn, Udo

doi:10.1145/3397271.3401048

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…When considering the findings in TREC's precision medicine track we find a potential explanation, as it was shown that using hyponyms during term expansion has a negative effect on search rankings [12]. The search tasks we identified may be too generic, and having more specific tasks may improve the efficacy of our approach.…”

Section: Term Expansionmentioning

confidence: 90%

Exploring task-based query expansion at the TREC-COVID track

Schoegje¹,

Kamphuis²,

Dercksen³

et al. 2020

Preprint

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We explore how to generate effective queries based on search tasks. Our approach has three main steps: 1) identify search tasks based on research goals, 2) manually classify search queries according to those tasks, and 3) compare three methods to improve search rankings based on the task context. The most promising approach is based on expanding the user's query terms using task terms, which slightly improved the NDCG@20 scores over a BM25 baseline. Further improvements might be gained if we can identify more specific search tasks.

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Section: Term Expansionmentioning

confidence: 90%

Exploring task-based query expansion at the TREC-COVID track

Schoegje¹,

Kamphuis²,

Dercksen³

et al. 2020

Preprint

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“…We applied information extraction pipelines to extract a variety of named entity classes. Despite the limitations we discussed, the information extracted so far can be of immediate use to enable semantic search functionalities in the guideline app (Seufferlein et al, 2019), precision medicine search engines (Faessler et al, 2020) or in clinical decision support systems (Schapranow et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines

Borchert¹,

Lohr²,

Modersohn³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.

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“…The use of advanced IR techniques to improve scientific literature search has become an essential part of modern biomedical search engines [10,13,27,32]. Of particular interest to this work, the BERT contextual language model (LM) [8] and many of its extensions and variants such as ALBERT [17], SciBERT [4], or RoBERTa [20] have shown significant improvements in several natural language processing (NLP) tasks, such as question answering, named entity recognition, and passage retrieval [21,23].…”

Section: Related Workmentioning

confidence: 99%

Structured Fine-Tuning of Contextual Embeddings for Effective Biomedical Retrieval

Ueda

Santos

Macdonald

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Biomedical literature retrieval has greatly benefited from recent advances in neural language modeling. In particular, fine-tuning pretrained contextual language models has shown impressive results in recent biomedical retrieval evaluation campaigns. Nevertheless, current approaches neglect the inherent structure available from biomedical abstracts, which are (often explicitly) organised into semantically coherent sections such as background, methods, results, and conclusions. In this paper, we investigate the suitability of leveraging biomedical abstract sections for fine-tuning pretrained contextual language models at a finer granularity. Our results on two TREC biomedical test collections demonstrate the effectiveness of the proposed structured fine-tuning regime in contrast to a standard fine-tuning that does not leverage structure. Through an ablation study, we show that models fine-tuned on individual sections are able to capture potentially useful word contexts that may be otherwise ignored by structure-agnostic models. CCS CONCEPTS• Applied computing → Bioinformatics; • Information systems → Retrieval models and ranking.

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What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way

Cited by 10 publications

References 19 publications

Exploring task-based query expansion at the TREC-COVID track

Exploring task-based query expansion at the TREC-COVID track

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines

Structured Fine-Tuning of Contextual Embeddings for Effective Biomedical Retrieval

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