Neural expert decision support system for stroke diagnosis

Куперштейн, Леонід; Мартынюк, Т. Б.; Krencin, Myhail D.; Kozhemiako, A. V.; Bezsmertnyi, Yu. O.; Bezsmertna, Halyna V.; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, Róża; Uvaysova, Svetlana

doi:10.1117/12.2280956

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…A study [ 24 ] conducted a review on AI health-related applications in low- and mid-income countries and concluded that data from these countries were used for AI research. An expert system integrated with a neural network model, and production rules [ 25 ] were proposed for stroke diagnosis and treatment plan provision.…”

Section: Introductionmentioning

confidence: 99%

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Juang

Hsu²,

Cai³

et al. 2022

PLoS ONE

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Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician’s decision in response of patient’s physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient’s comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.

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Section: Introductionmentioning

confidence: 99%

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Juang

Hsu²,

Cai³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…A wide variety of research work proposes machine learning algorithms such as Decision Tree, Bayesian Learning, and Artificial Neural Network to learn the symptoms and predict the presence of stroke disease [3]- [5]. The models like Neural Network-Based Expert Decision Support System for Stroke diagnosis are used to set the preliminary and final diagnoses to get the examination and treatment plans to analyze statistical data [6]. The literature includes predicting the probability of developing stroke based on various medical factors of an individual, such as heart disease, hypertension, and immunity disease [7].…”

Section: Introductionmentioning

confidence: 99%

A Clinical Decision Support System for Identification of Factors Causing Stroke in Adults

Sharma*,

Sharma,

Singhal

et al. 2020

IJRTE

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Among neurological patients, stroke is a significant concern that can lead to permanent disability or may cause death. Determining the contributing factors to stroke would better enable proactive forms of healthcare measures to be taken for reducing mortality and other effects of this disease. Numerous works have been carried out for determining the various factors which contribute to stroke. The goal of this work is to identify the factors which may cause stroke by proposing a Decision Support System. In this research work, various medical and psychological parameters are analyzed with the aim of determining association between these parameters and stroke. The methodology includes appropriate feature selection, attribute type conversion, univariate and multivariate analysis. Data mining techniques such as Chi square test and Frequency Distribution analysis are used for hypothesis testing and drawing inferences. The analysis shows that psychological factors such as Marital Status, EmploymentStatus, and Age Group are the major contributors, which may lead to stroke in comparison to Medical factors. The findings can be used as a proactive measure for individuals who have high possibility of developing stroke. This work can also be used as a foundation to build a recommendation system for prevention of stroke.

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Evaluation of Recurrent Neural Network Model Training for Health Care Suggestions

Hsu

Juang

Cai

et al. 2022

2021 International Conference on Security and Information Technologies With AI, Internet Computing and Big-Data Applications

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Neural expert decision support system for stroke diagnosis

Cited by 3 publications

References 7 publications

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

A Clinical Decision Support System for Identification of Factors Causing Stroke in Adults

Evaluation of Recurrent Neural Network Model Training for Health Care Suggestions

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