An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription

Shen, Ying; Yuan, Kun; Chen, Daoyuan; Colloc, Joël; Yang, Min; Li, Yaliang; Lei, Kai

doi:10.1016/j.artmed.2018.01.003

Cited by 64 publications

(43 citation statements)

References 19 publications

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“…Previous works presented ontologies agents and the experience layer, the case based reasoning approach in medicine [20,21,71,72].…”

Section: Discussionmentioning

confidence: 99%

“…This allows analog reasoning at different levels of the object composition hierarchy. In previous works, we have shown that object-oriented approaches are well suited to building very complex ontologies in infectious disease diagnosis and antibiotic prescription, which needs to assess a lot of diseases, drugs, clinical objects and attributes [20,21]. These previous works were not using the TFVS model.…”

Section: State Of the Art Of Knowledge And Time Modeling With Fuzzy Lmentioning

confidence: 99%

“…Then, for each object type, it is necessary to define private internal functions and public accessors. The object types described in the ontology can be mapped in one or several UML class diagrams provided that the semantic relationships of the domain ontology are well respected as in the ontology of the diagnosis of infectious diseases [21]. The third step concerns the knowledge design with a fuzzy vector space and relies on the composition relationship and the qualifier attribute descriptors that defines the characteristics of each attribute of the object type and thus of the object instances Table 1.…”

Section: Dictionnary Of Attribute Descriptorsmentioning

confidence: 99%

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Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

Colloc

2020

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The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model.

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“…Previous works presented ontologies agents and the experience layer, the case based reasoning approach in medicine [20,21,71,72].…”

Section: Discussionmentioning

confidence: 99%

Section: State Of the Art Of Knowledge And Time Modeling With Fuzzy Lmentioning

confidence: 99%

Section: Dictionnary Of Attribute Descriptorsmentioning

confidence: 99%

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Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

Colloc

2020

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“…Overall, this and previous studies (Zhanga and Amin, 2016) suggest that machine learning provides good accuracy confirming other studies validating in silico methods to be used for screening of large datasets to identify potential antiinfectious candidates. In line with this, Shen et al have clearly shown how treatments can be assisted using mathematical models (Shen et al, 2018). Shen et al proposed a decision support system can propose an antibiotic therapy adapted to the patient based on factors such as the body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, and even contraindications and drug-drug interactions.…”

Section: Treatments and Antimicrobial Drug Resistancementioning

confidence: 97%

Use of artificial intelligence in infectious diseases

Agrebi

Larbi

2020

Artificial Intelligence in Precision Health

107

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“…Infectious Disease Ontology 1 (IDO), Infectious Disease Ontology for Dengue 2 (IDODEN), and Disease Ontology 3 (DO) are adopted as baselines. Our material includes an in-house ontology that is used to develop an ontology-driven clinical decision support system for infectious disease diagnosis and antibiotic prescription (IDDAP) [ 8 ]. To demonstrate the applicability and generality of our quality metric for ontologies, we conduct evaluations on real-world ontologies with different structures and different textual information.…”

Section: Introductionmentioning

confidence: 99%

EAPB: entropy-aware path-based metric for ontology quality

et al. 2018

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BackgroundEntropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.ResultsWe propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.ConclusionsWe leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate).

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An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription

Cited by 64 publications

References 19 publications

Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

Use of artificial intelligence in infectious diseases

EAPB: entropy-aware path-based metric for ontology quality

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