Integrating deep learning to improve text understanding in conversation-based ITS

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

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Using deep learning for short text understanding

Journal Of Big Data ◽

10.1186/s40537-017-0095-2 ◽

2017 ◽

Vol 4 (1) ◽

Cited By ~ 14

Author(s):

Justin Zhan ◽

Binay Dahal

Keyword(s):

Deep Learning ◽

Short Text ◽

Text Understanding

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Integrating deep learning to improve text understanding in conversation-based ITS

International Journal of Smart Technology and Learning ◽

10.1504/ijsmarttl.2021.10042579 ◽

2021 ◽

Vol 2 (4) ◽

pp. 304

Author(s):

Zhiqiang Cai ◽

Xiangen Hu ◽

Frank Andrasik ◽

Sheng Xu

Keyword(s):

Deep Learning ◽

Text Understanding

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Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

10.21203/rs.3.rs-500521/v1 ◽

2021 ◽

Author(s):

Hossein Hematialam ◽

Wlodek W. Zadrozny

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Clinical Guidelines ◽

Domain Adaptation ◽

Learning Methods ◽

Text Understanding ◽

Prior Art ◽

Medical Guidelines ◽

Conceptual Link ◽

Automated Methods

Abstract Background: Medical guidelines provide the conceptual link between a diagnosis and a recommendation. They often disagree on their recommendations. There are over thirty five thousand guidelines indexed by PubMed, which creates a need for automated methods for analysis of recommendations, i.e., recommended actions, for similar conditions. Results: This article advances the state of the art in text understanding of medical guidelines by showing the applicability of transformer-based models and transfer learning (domain adaptation) to the problem of finding condition-action and other conditional sentences. We report results of three studies using syntactic, semantic and deep learning methods, with and without transformer-based models such as BioBERT and BERT. We perform in depth evaluation on a set of three annotated medical guidelines. Our experiments show that a combination of machine learning domain adaptation and transfer can improve the ability to automatically find conditional sentences in clinical guidelines. We show substantial improvements over prior art (up to 25%), and discuss several directions of extending this work, including addressing the problem of paucity of annotated data.Conclusion: Modern deep learning methods, when applied to the text of clinical guidelines, yield substantial improvements in our ability to find sentences expressing the relations of condition-consequence, condition-action and action.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Local and Global Coherence in Text Understanding

PsycEXTRA Dataset ◽

10.1037/e537272012-217 ◽

1994 ◽

Author(s):

Joseph P. Magliano ◽

Tom Trabasso

Keyword(s):

Text Understanding ◽

Global Coherence

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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