Health Causal Probability Knowledge Graph

Yu, Hongqing

doi:10.1145/3440067.3440077

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Generation of causal models may be as a result of medical study such as the WHO-UMC [61], or may even be developed by using machine learning approach [62], [63]. In our research, we refer to the causal graphs generated by experts representing their expertise and domain knowledge in a cause-effect manner [64].…”

Section: Towards Causal Interpretability For Accountabilitymentioning

confidence: 99%

Accountable AI for Healthcare IoT Systems

Bagave

Westberg

Dobbe

et al. 2022

2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)

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Various AI systems have taken a unique space in our daily lives, helping us in decision-making in critical as well as non-critical scenarios. Although these systems are widely adopted across different sectors, they have not been used to their full potential in critical domains such as the healthcare sector enabled by the Internet of Things (IoT). One of the important hindering factors for adoption is the implication for accountability of decisions and outcomes affected by an AI system, where the term accountability is understood as a means to ensure the performance of a system. However, this term is often interpreted differently in various sectors. Since the EU GDPR regulations and the US congress have emphasised the importance of enabling accountability in AI systems, there is a strong demand to understand and conceptualise this term. It is crucial to address various aspects integrated with accountability and understand how it affects the adoption of AI systems. In this paper, we conceptualise these factors affecting accountability and how it contributes to a trustworthy healthcare AI system. By focusing on healthcare IoT systems, our conceptual mapping will help the readers understand what system aspects those factors are contributing to and how they affect the system trustworthiness. Besides illustrating accountability in detail, we also share our vision towards causal interpretability as a means to enhance accountability for healthcare AI systems. The insights of this paper shall contribute to the knowledge of academic research on accountability, and benefit AI developers and practitioners in the healthcare sector.

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Section: Towards Causal Interpretability For Accountabilitymentioning

confidence: 99%

Accountable AI for Healthcare IoT Systems

Bagave

Westberg

Dobbe

et al. 2022

2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)

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“…The information contained in knowledge graphs is increasingly used in the scientific community to solve different problems [1,3,67]. Specifically, community-maintained knowledge graphs such as Wikidata [71] or DBpedia [33] represent rich sources of structured knowledge not only from general domains but also in biomedicine [10,30,79]. Figure 1(b) depicts the contextual knowledge obtained through structured data in a portion of Wikidata.…”

Section: Introductionmentioning

confidence: 99%

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

Benítez-Andrades

García-Ordás

Russo

et al. 2023

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Social networks have become information dissemination channels, where announcements are posted frequently; they also serve as frameworks for debates in various areas (e.g., scientific, political, and social). In particular, in the health area, social networks represent a channel to communicate and disseminate novel treatments’ success; they also allow ordinary people to express their concerns about a disease or disorder. The Artificial Intelligence (AI) community has developed analytical methods to uncover and predict patterns from posts that enable it to explain news about a particular topic, e.g., mental disorders expressed as eating disorders or depression. Albeit potentially rich while expressing an idea or concern, posts are presented as short texts, preventing, thus, AI models from accurately encoding these posts’ contextual knowledge. We propose a hybrid approach where knowledge encoded in community-maintained knowledge graphs (e.g., Wikidata) is combined with deep learning to categorize social media posts using existing classification models. The proposed approach resorts to state-of-the-art named entity recognizers and linkers (e.g., Falcon 2.0) to extract entities in short posts and link them to concepts in knowledge graphs. Then, knowledge graph embeddings (KGEs) are utilized to compute latent representations of the extracted entities, which result in vector representations of the posts that encode these entities’ contextual knowledge extracted from the knowledge graphs. These KGEs are combined with contextualized word embeddings (e.g., BERT) to generate a context-based representation of the posts that empower prediction models. We apply our proposed approach in the health domain to detect whether a publication is related to an eating disorder (e.g., anorexia or bulimia) and uncover concepts within the discourse that could help healthcare providers diagnose this type of mental disorder. We evaluate our approach on a dataset of 2,000 tweets about eating disorders. Our experimental results suggest that combining contextual knowledge encoded in word embeddings with the one built from knowledge graphs increases the reliability of the predictive models. The ambition is that the proposed method can support health domain experts in discovering patterns that may forecast a mental disorder, enhancing early detection and more precise diagnosis towards personalized medicine.

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A survey of the extraction and applications of causal relations

Drury

Oliveira

Lopes³

2022

Nat. Lang. Eng.

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Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.

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Health Causal Probability Knowledge Graph

Cited by 5 publications

References 12 publications

Accountable AI for Healthcare IoT Systems

Accountable AI for Healthcare IoT Systems

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

A survey of the extraction and applications of causal relations

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