Explainable Word-Embeddings for Medical Digital Libraries - A Context-Aware Approach

Wawrzinek, Janus; Hussaini, Said Ahmad Ratib; Wiehr, Oliver; Pinto, José María González; Balke, Wolf‐Tilo

doi:10.1145/3383583.3398522

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The interpretable word embeddings correspond to categorical embeddings, trained separately using expert-provided definitions and additional knowledge from a biomedical knowledge graph. Wawrzinek et al (2020) introduce an entity embedding-based explanation method for drug-disease association (DDA) prediction. They construct explanations following the drug-centric and the disease-centric notion of similarity:…”

Section: Explainable Nlp In Pharmacologymentioning

confidence: 99%

Review of Natural Language Processing in Pharmacology

et al. 2023

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Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers. Significance StatementThe main objective of this work is to survey the recent use of NLP in the field of pharmacology, in order to provide a comprehensive overview of the current state in the area after the rapid developments which occurred in the last few years. We believe the resulting survey to be useful to practitioners and interested observers in the domain.

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Section: Explainable Nlp In Pharmacologymentioning

confidence: 99%

Review of Natural Language Processing in Pharmacology

et al. 2023

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“…In addition, the type of explanation changes depending on the data source such as images [44,91], symbolic logic representation [4], or text based explanations [6] . Yet other methods exist for domains beyond supervised learning / classification, applied instead to unsupervised learning / clustering [19,32,68], reinforcement learning [53], AI planning [15], computer vision [91], recommendation systems [88], natural language processing [57,81,86], speech recognition [33], or multi-agent simulations [6] . Specialized XAI techniques even exist for adaptive systems such as interactive visualization systems [77], interactive virtual agents [37,83], active learning [27], and human-in-the-loop systems [86] .…”

Section: The Challengesmentioning

confidence: 99%

Seven challenges for harmonizing explainability requirements

Chen,

Storchan

2021

Preprint

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Regulators have signalled an interest in adopting explainable AI (XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the financial services industry. In this short overview, we review the recent technical literature in XAI and argue that based on our current understanding of the field, the use of XAI techniques in practice necessitate a highly contextualized approach considering the specific needs of stakeholders for particular business applications.

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“…In this paper, we focus on the computer assisted interactive curation of map-like visualisations, which are two-dimensional semantic layouts of a digital library. This form of visualisation has been used on text corpora in different domains, for example in medicine [37], for climate change research [6], and patents [16]. Pang et al [27] found that transferring concepts and analogies from geographic maps to these artificial maps helps users to get a better overview of their digital library.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of digital libraries, there is a plethora of related work on learning and applying representations for documents. High-dimensional vector representations are utilised in explainable models to interactively gather insights into a digital library [36,37], for visual search interfaces [25,34,38], as well as for interactive clustering or classification [11,12]. Visualisations, such as overview maps of an entire digital library, are powerful tools to explore the data [15].…”

Section: Introductionmentioning

confidence: 99%

Interactive Curation of Semantic Representations in Digital Libraries

Repke¹,

Krestel²

2021

Lecture Notes in Computer Science

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Digital libraries often contain many heterogeneous documents and cover a variety of topics. Computer generated virtual maps of such collections can help to get an overview and explore the data. The position of each document from the corpus on this virtual two-dimensional map is determined by its semantic similarity to the other documents. However, the computed layout of the data may not adhere to the expectation of domain experts. To this end, we propose a novel approach that enables users to interactively curate the layout of the data. By dragging only a few documents on the canvas, the user can adjust the computed layout to better reflect the expected interpretation of the underlying data. We demonstrate the effectiveness and robustness of our approach using a series of real world datasets.

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Explainable Word-Embeddings for Medical Digital Libraries - A Context-Aware Approach

Cited by 4 publications

References 19 publications

Review of Natural Language Processing in Pharmacology

Review of Natural Language Processing in Pharmacology

Seven challenges for harmonizing explainability requirements

Interactive Curation of Semantic Representations in Digital Libraries

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