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检索条件"任意字段=Conference on PACS Design and Evaluation - Engineering and Clinical Issues"

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Healthcare is being revolutionized by AI-powered solutions and technological integration for easily accessible and efficient medical care 15

Healthcare is being revolutionized by AI-powered solutions a...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ingale, Vinod Wankar, Bhagyashri Jadhav, Komal Adedoja, Tolu Borate, Vishal Kisan Mali, Yogesh Kisan KJ College of Engineering & Management Research Pune India G.H Raisoni College of Engineering and Management Wagholi Pune India University of Utah Salt Lake City United States Dr D y Patil College of Engineering & Innovation Talegaon Pune India

ISBN: (纸本)9798350370249

The accompanying qualities of the medical care stage that this study recommends for rustic regions are: Client ID or picture based acknowledgment are utilized for a safe login process. Choices for Counsel: Select from clinical visit bots or video conferences with doctors. Infection Expectation: To make proposals for conceivable sickness determination, constant picture investigation, (for example, assessing transferred photographs) and AI are utilized. Risk appraisal: AI calculations assess gambles for various wellbeing issues in view of client gave information (age, clinical history, and so on.).Integration with Aasha Workers: The platform establishes a connection with Aasha workers, who can provide medication as required following consultations. Technical considerations: The platform can be built using web development frameworks such as Flask or Django. Strong privacy and data security protocols are necessary. Project Objectives: Expand access to healthcare in remote locations. Give patients knowledge and tools for self- evaluation to empower them. Improve interaction and cooperation between medical professionals, patients, and Asha employees. © 2024 IEEE.

关键词： Web design

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Artificial Intelligence-Based Approach for Classification and Prediction of Mental Health 14

Artificial Intelligence-Based Approach for Classification an...

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14th International conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Verma, Siddharth Sharma, Chetna Aggarwal, Garima Upadhya, Parth Amity University Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350344837

Mental health evaluation is a crucial component in early identification and treatment of mental illnesses. Mental health disorders should be classified and processed under predictive analysis for their timely and effective treatments. This study attempts to design an Artificial-Intelligence based approach to bridge the gap between clinical expertise and technological advancements. This can be accomplished by visualizing large datasets, and applying different types of machine learning techniques. This not only enhances diagnostic precision but also helps predict mental health problems. The goal is to perform comparative analysis on different machine learning algorithms and classify mental health issues. The study underlines the multiple factors that contribute to the mental health disorders guiding clinicians and professionals in decision making. By utilising various supervised machine learning models, XGBoost model achieves an astounding accuracy of 99.85% which shows to be the most effective one for detecting mental health followed by Decision Tree with 99.76%. XGBoost is robust and achieves high accuracy due to its ensemble learning technique on decision trees. © 2024 IEEE.

关键词： Decision trees

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Machine Learning Approaches for Predicting Heart Failure from Echocardiography Data 10

Machine Learning Approaches for Predicting Heart Failure fro...

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10th IEEE Uttar Pradesh Section International conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Yadav, Kanchan Aswal, Upendra Singh Michaelson, Jacob Laxminarayanamma, K. Arora, Deepika Singh, Pankaj Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

This study examines how to use ultrasound data to predict cardiac failure using a deductive methodology, interpretive theoretical framework, and a descriptive design of research. The foundation of the investigation is secondary information collecting from several healthcare organizations. clinical factors including age, medical history, and test results are collected from echocardiography and combined with properties like the fraction of ejection, thickness of the walls, and valvular function. The main predictive tool utilizes the linear regression method, chosen for its clinical applicability and comprehension. Accuracy, sensitivity, particularity, and AUC-ROC measures are used to assess the algorithm's performance, giving a thorough evaluation of its discriminatory capacity. Results show a strong relationship between certain echocardiogram features and the risk of heart failure. Integrating clinical data improves predictive accuracy and provides a more complete picture of the condition of the patient. The main factors influencing heart failure prediction are clarified through the examination of coefficients with feature significance scores. The study's conclusions are clinically applicable and give healthcare professionals useful information for prompt action and individualized patient treatment. Throughout the investigation process, ethical issues including data protection and informed consent are carefully addressed. By offering a structured framework for using ultrasonography data in coronary artery disease prediction, this research advances the developing field of coronary medicine. Given the context of managing heart failure, it highlights the possibility for improved managerial choices and patient satisfaction. © 2023 IEEE.

关键词： blood pumping crippling cardiovascular deep learning heart failure Numerous methods

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Supporting the Episodic Application of clinical Guidelines over Significant Time Periods

Supporting the Episodic Application of Clinical Guidelines o...

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34th Medical Informatics Europe conference, MIE 2024

作者： Ben Shahar, Bruria Shahar, Yuval Jaffe, Shai Cohen, Odeya Shalom, Erez Selivanova, Maya Rimon, Ephraim Goldstein, Ayelet Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Nursing Faculty of Health Sciences Ben-Gurion University Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University Israel Herzfeld Geriatric Rehabilitation Medical Center Clalit Health Services Israel Department of Computer Science Hadassah Academic College Jerusalem Israel

ISBN: (纸本)9781643685335

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations. © 2024 The Authors.

关键词： Random errors

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An Application for Melanoma Detection using Deep Learning Techniques 14

An Application for Melanoma Detection using Deep Learning Te...

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14th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Rahul, G.R. Manasa, R. Subha, T. SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India Faculty of Engineering & Technology SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence Chennai India

Utilizing PC vision, machine learning, and deep learning, the objective is to track down new data and concentrate data from advanced pictures. Images can now be used for both early illness detection and treatment. Dermatology uses deep neural networks to tell the difference between images with and without melanoma. Two important melanoma location research topics have been emphasized in this essay. Classifier accuracy is impacted by even minor alterations to the dataset's bounds, the primary variable under investigation. We examined the Exchange Learning issues in this example. We propose using continuous preparation test cycles to create trustworthy prediction models on the basis of this initial evaluation's findings. Second, a more flexible design philosophy that can oblige changes in the preparation datasets is fundamental. We recommended the creation and utilization of a half breed plan in view of cloud, dimness, and edge figuring to give Melanoma Area the board in light of clinical and dermoscopic pictures. By lessening the span of the consistent retrain, this design must continually adjust to the quantity of data that should be investigated. This aspect has been highlighted in experiments conducted on a single PC using various conveyance methods, demonstrating how a distributed system guarantees yield fulfillment in an unquestionably more acceptable amount of time. © Grenze Scientific Society, 2023.

关键词： Feature extraction

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Realizing the Application Potential of Graphene-Modified Bionanocomposites for Prosthesis and Implant Applications 8th

Realizing the Application Potential of Graphene-Modified Bio...

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8th International and 29th All India Manufacturing Technology, design and Research conference, AIMTDR 2021

作者： Singh, Devendra Kumar Verma, Rajesh Kumar Materials and Morphology Laboratory Department of Mechanical Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India

ISBN: (纸本)9789811945700

The bionanocomposite is successively replacing conventional biomaterials such as metallic and nonmetallic alloys. The medical sector widely uses it due to its strength, inertness, tailored shape, ease of fabrication, and biocompatibility. Bionanocomposites are used in clinical practice to alleviate a load of damaged or infected bone to enhance the patient’s quality of life. The ultra-high molecular weight polyethylene (UHMWPE) is a widely used polymeric biomaterial for joint replacements and the development of critical implant shapes. Adding nanofillers like graphene and its derivatives to polymers have significantly improved the mechanical properties, thermal stability, wear resistance, and conductivity of the resultant polymeric biomaterial. A significant number of scholars are working on it because there is rising interest among researchers about improving its tribological characteristics and its numerous benefits. The current hectic daily routine, obesity, long working hours, poor trend of consuming packaged food items, accidents, and other health issues have made it necessary to investigate the fabrication of improved, cost-effective biomaterials with longer functional life. The purpose of this work is to provide a comprehensive evaluation of the different fabrication methods, the effect of graphene-based nanofillers, and application fields of other graphene-based composite materials to provide a clear understanding of the future development of polymeric biomaterials with preferred assets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphene

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design of a novel recurrent neural network design with augmented activation for classification of ECG signals 10

Design of a novel recurrent neural network design with augme...

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10th International conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Gahane, Aroh Kotadi, Chinnaiah G H Raisoni College of Engineering Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9781665467414

Electrocardiogram (ECG) waves are capable of identification of heart related issues, that include cardio myopathy, heart failure, arrythmias, etc. designing of these models includes segmentation of periodic signal types, extraction of features, selection of features, classification of features, and performing post processing tasks. A large number of systems are proposed to perform these tasks, and each of them vary in terms of application-of-use, accuracy, computational delay, and other performance parameters. From the analysis of these models, it was observed that they have moderate accuracy when applied to multiple types of heart diseases. In order to improve this accuracy & reliability for different signal types, this text proposes an augmented recurrent neural network (RNN) model, that combines gated recurrent unit (GRU) with long-short-term memory (LSTM) unit for design of an augmented classifier for heart diseases. The proposed model uses a combination of these feature activation modes, and integrates them in order to improve their feature selection capabilities. The selected features are classified using a standard RNN model, which assists in achieving an accuracy of 99.65% across different ECG datasets. The model was evaluated on MITBIH, PhysioNet, & PTB XL datasets, and a consistent performance was observed across these datasets. Heart conditions like coronary artery disease, heart valve disease, cardiomyopathy, and heart arrhythmias were classified effectively by the proposed model. Due to which, it can be scaled to multiple dataset types, and can be used for clinical evaluation of different heart diseases. Delay performance of the proposed model was also evaluated on these datasets, and an improvement of 9.6% in terms of overall execution speed was observed. This is due to use of augmented feature reduction of signals, which assists in selection of most relevant features based on input dataset, thereby improving real-time applicability for multiple types

关键词： Heart Recurrent neural networks Computational modeling Electrocardiography Feature extraction Reliability engineering Delays

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Artificial Intelligence-Based Approach for Classification and Prediction of Mental Health

Artificial Intelligence-Based Approach for Classification an...

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International conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Siddharth Verma Chetna Sharma Garima Aggarwal Parth Upadhya Computer Science and Engineering Amity University Noida Uttar Pradesh India

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IOT Based clinical Data Set Formation Model for Smart Health Care Application 3

IOT Based Clinical Data Set Formation Model for Smart Health...

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3rd International conference on Intelligent engineering and Management, ICIEM 2022

作者： Shrivastava, Anurag Chauhan, Divyansh Negi, Prashansha Jain, Neha Chauhan, Hemant Singh, Sohan Sushila Devi Bansal College Indore India Chitkara University Department of Computer Science and Application Punjab Rajpura Chandigarh India Graphic Era Hill University Uttarakhand Dehradun India Galgotias University Department of Management Uttar Pradesh Noida India GRD College Uttarakhand Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The rapid development of the Internet and computer technology makes it possible to develop IoT based health care services in today's ageing population. However, the IoT data problem in elderly care seriously restricts the process of intelligentization in this field. issues such as missing real IoT data, interference of dirty IoT data and too few standard samples emerge in an endless stream. Aiming at the problem of missing IoT data sets, based on collecting a small sample IoT data of community health care in a city, a three-stage IoT data generation model based on machine learning is proposed. The generation strategy based on tree structure is used in the first stage. The distribution of the IoT data generates the essential attributes of the IoT data set;in the second stage, the naive Bayesian algorithm is used to generate the primary behavioural ability evaluation index of the sample;in the third stage, the multiple linear regression method is used to generate the high Behavioral competency indicators and evaluation stages. In addition, to verify the effectiveness of the IoT data set generated by the model for downstream tasks, based on the generated IoT data, this paper uses neural networks to design multiple rehabilitation training plan recommendation models to achieve five multi-classification tasks and two multi-label classification tasks. Finally, this paper verifies the authenticity and validity of the generated IoT data by analyzing the experimental results and injecting expert knowledge. © 2022 IEEE.

关键词： Linear regression

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design and Implementation of A Real-Time Health Monitoring System Using IoT Sensors and Machine Learning Algorithms

Design and Implementation of A Real-Time Health Monitoring S...

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International conference on Contemporary Computing and Informatics (IC3I)

作者： Anurag Aeron Pawan Kumar Goel Sakil Ahmad Ansari Vyom Sharma Komal Computer Science & Engineering MIET Meerut Meerut Uttar Pradesh India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad Uttar Pradesh India Computer Science & Engineering Shri Ram Group of Colleges Muzaffarnagar Ghaziabad UP India Computer Science & Engineering Meerut Institute of Technology Meerut Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Real-Time wi-fi Monitoring System design & Implementation with IoT Sensors and System Learning Algorithms for clinical Improvement - The motive is to decorate the healthcare enterprise with an advanced era. It collects patients’ we-fitness parameters like heartbeat, blood pressure, and body temp in real time with sensors of Net of things (IoT). The accumulated records are then despatched to a cloud server for addition processing and evaluation of the usage of the system gaining knowledge of algorithms. The system provides a user-friendly interface for healthcare professionals to receive the data and monitor patients remotely. It also sends a real-time alert notification if an abnormal reading is detected so that corrective actions can be taken quickly and possibly prevent major wireless issues. The machine learning algorithms employed in the system will be able to learn from all the data gathered, resulting in personalized and precise we-fitness tracking. The device design is scalable and can easily fit into current healthcare systems. It also has stable security features to ensure the pirates and confidentiality of patient records. This machine has so much potential and if applied correctly, it could change the way healthcare operates affecting a lot more people around the globe wireless powerful healthcare services to them, Wi-Fi -ally in remote & served parts of our planet. universal, Wifi-enabled tracking system in real-time has the potential to significantly improve patient outcomes and reduce healthcare costs.

关键词： Wireless communication Wireless sensor networks Machine learning algorithms Medical services Real-time systems Sensor systems Sensors Communication system security Biomedical monitoring Monitoring

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