A System for Medical Information Extraction and Verification from Unstructured Text

Jurić, Damir; Stoilos, Giorgos; Melo, André; Moore, Jonathan; Khodadadi, Mohammad

doi:10.1609/aaai.v34i08.7042

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…TransE [23] is a well-known translational approach that uses simple assumptions to achieve considerably accurate and scalable results, proved to be effective and efficient embedding model [24]. Several enhanced models (e.g.…”

Section: Knowledge Embedding Representationmentioning

confidence: 99%

Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

Tissot

Pedebôs

2021

J Healthc Inform Res

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Section: Knowledge Embedding Representationmentioning

confidence: 99%

Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

Tissot

Pedebôs

2021

J Healthc Inform Res

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“…Several empirical studies that have been conducted recently have revealed difficulties in processing and acquiring new knowledge from unstructured text [12][13][14][15]. This is especially evident from groupware discussions, considering the nature of conversations which are not written in formal sentence patterns and contain grammatical and typographical errors.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Methodology for Knowledge Acquisition from Groupware

et al. 2022

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Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.

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Improving Risk Assessment of Miscarriage during Pregnancy with Knowledge Graph Embeddings

Tissot

Pedebôs

2020

Preprint

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Miscarriages are the most common type of pregnancy loss, mostly occurring in the first 12 weeks of pregnancy due to known factors of different natures. Pregnancy risk assessment aims to quantify evidence in order to reduce such maternal morbidities during pregnancy, and personalized decision support systems are the cornerstone of high-quality, patient-centered care in order to improve diagnosis, treatment selection, and risk assessment. However, the increasing number of patient-level observations and data sparsity requires more effective forms of representing clinical knowledge in order to encode known information that enables performing inference and reasoning. Whereas knowledge embedding representation has been widely explored in the open domain data, there are few efforts for its application in the clinical domain. In this study, we discuss differences among multiple embedding strategies, and we demonstrate how these methods can assist on clinical risk assessment of miscarriage both before and specially in the earlier pregnancy stages. Our experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both are able to improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score, and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets.

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A System for Medical Information Extraction and Verification from Unstructured Text

Cited by 5 publications

References 14 publications

Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

Ontology-Based Methodology for Knowledge Acquisition from Groupware

Improving Risk Assessment of Miscarriage during Pregnancy with Knowledge Graph Embeddings

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