Clinical decision support system for chronic obstructive pulmonary disease using machine learning techniques

Anakal, Sudhir; Sandhya, P.

doi:10.1109/iceeccot.2017.8284601

Cited by 26 publications

(13 citation statements)

References 11 publications

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“…Machine learning algorithms are now being used to predict the development of septic shock and aid diagnosis 26 and treatment of chronic obstructive pulmonary disease patients and many other specialist decisions. 31 They also have potential to help personalise treatment decisions for patients drawing upon large-scale data about previous cases that historically would have been difficult to make use of in clinical decision making. For example, a study identified how an AI framework employing sequential decision making could recommend alternate treatment paths, infer patient's health status even when measurements were not available and refine treatment/management plans as new information was received.…”

Section: Clinical Decision Supportmentioning

confidence: 99%

Artificial intelligence-enabled healthcare delivery

2018

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In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.

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Section: Clinical Decision Supportmentioning

confidence: 99%

Artificial intelligence-enabled healthcare delivery

2018

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“…The appropriate techniques are required and the proper use of data is needed to solve this issue which corresponds to the pattern recognition problems. ANN has been used in the medical fields as a diagnosis of disease tool that uses principles in pattern classification [ 25 ]. Based on this fact, ANN has enough potential to make predictions in medical outcome such as arteriovenous fistula stenosis.…”

Section: Introductionmentioning

confidence: 99%

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Stephanus

2018

Sensors

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This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

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“…Numerous studies have already documented the suc-cessful implementation of AI in different areas of healthcare, whether it is health administration (Amato et al, 2019;Anderson & Agarwal, 2011;Diamond, Mostashari & Shirky, 2009;Dimitrov, 2016;Impedovo & Pirlo, 2019;Winter & Davidson, 2019;etc. ), diagnosis, prediction and decision support for physicians (Anakal & Sandhya, 2017;Arsene, Dumitrache & Mihu, 2015;Esteva et al, 2017;Lynn, 2019;Meena et al, 2019;Samuel, Omisore & Ojokoh, 2013;Seo, 2019;etc. ), or medical robotics and related efforts to reduce the cost of healthcare services (Ballantyne, 2002;Barbash & Glied, 2010;Slakey & Davidson, 2019;etc.…”

Section: Healthcare Services and Artificial Intelligencementioning

confidence: 99%

Implementation and Application of Artificial Intelligence in Selected Public Services

et al. 2021

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Data-intensive technologies, such as artificial intelligence, imply huge opportunities for transforming the delivery of healthcare and social services, improving people’s quality of life and working in the health and welfare system. The aim of this paper is to present examples of the implementation of artificial intelligence techniques in healthcare and social services and to sketch the trends and challenges in the adoption of artificial intelligence techniques, with an emphasis on the public sector and selected public services. Analysis is based on a realistic assessment of current artificial intelligence technologies and their anticipated development. Besides the benefits and potential opportunities for healthcare and social services, there are also challenges for governments. Understanding the huge potential of artificial intelligence as well as its limitations will be a key step forward, but it is essential to avoid the trap of an overestimation of artificial intelligence potential.

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Clinical decision support system for chronic obstructive pulmonary disease using machine learning techniques

Cited by 26 publications

References 11 publications

Artificial intelligence-enabled healthcare delivery

Artificial intelligence-enabled healthcare delivery

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Implementation and Application of Artificial Intelligence in Selected Public Services

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