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An adaptive QoS computation for medical data processing in intelligent healthcare applications

NEURAL COMPUTING & APPLICATIONS 2020年第3期32卷 723-734页

作者： Sodhro, Ali Hassan Malokani, Abdul Sattar Sodhro, Gul Hassan Muzammal, Muhammad Zongwei, Luo Sukkur IBA Univ Dept Elect Engn Sukkur 65200 Pakistan Linkoping Univ IDA Comp & Informat Sci Dept S-58183 Linkoping Sweden Kyunghee Univ South Korea Dept Biomed Engn Suwon 16705 South Korea Shah Abdul Latif Univ Dept Phys Khairpur Mirs 66111 Sindh Pakistan Bahria Univ Dept Comp Sci Islamabad Pakistan Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China

Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This paper contributes in three distinct ways. First, it proposes the novel adaptive QoS computation algorithm (AQCA) for fair and efficient monitoring of the performance indicators, i.e., transmission power, duty cycle and route selection during medical data processing in healthcare applications. Second, framework of QoS computation in medical applications is proposed at physical, medium access control (MAC) and network layers. Third, QoS computation mechanism with proposed AQCA and quality of experience (QoE) is developed. Besides, proper examination of QoS computation for medical healthcare application is evaluated with 4-10 inches large-screen user terminal (UT) devices (for example, LCD panel size, resolution, etc.). These devices are based on high visualization, battery lifetime and power optimization for ECG service in emergency condition. These UT devices are used to achieve highest level of satisfaction in terms, i.e., less power drain, extended battery lifetime and optimal route selection. QoS parameters with estimation of QoE perception identify the degree of influence of each QoS parameters on the medical data processing is analyzed. The experimental results indicate that QoS is computed at physical, MAC and network layers with transmission power (- 15 dBm), delay (100 ms), jitter (40 ms), throughput (200 Bytes), duty cycle (10%) and route selection (optimal). Thus it can be said that proposed AQCA is the potential candidate for QoS computation than Baseline for medical healthcare applications.

关键词： Adaptive QoS computation medical data processing QoS-QoE correlation Intelligent healthcare applications

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ERLNEIL-MDP: Evolutionary reinforcement learning with novelty-driven exploration for medical data processing

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Lv, Jianhui Kim, Byung-Gyu Slowik, Adam Parameshachari, B. D. Kumari, Saru Chen, Chien-Ming Li, Keqin Jinzhou Med Univ Affiliated Hosp 1 Jinzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sookmyung Womens Univ Seoul South Korea Koszalin Univ Technol Koszalin Poland Nitte Meenakshi Inst Technol Bengaluru 560064 Karnataka India Chaudhary Charan Singh Univ Meerut India Nanjing Univ Informat Sci & Technol Nanjing Peoples R China SUNY Coll New Paltz New Paltz NY 12561 USA

The rapid growth of medical data presents opportunities and challenges for healthcare professionals and researchers. To effectively process and analyze this complex and heterogeneous data, we propose evolutionary reinforcement learning with novelty-driven exploration and imitation learning for medical data processing (ERLNEIL-MDP) algorithm, including a novelty computation mechanism, an adaptive novelty-fitness selection strategy, an imitation-guided experience fusion mechanism, and an adaptive stability preservation module. The novelty computation mechanism quantifies the novelty of each policy based on its dissimilarity to the population and historical data, promoting exploration and diversity. The adaptive novelty-fitness selection strategy balances exploration and exploitation by considering policies' novelty and fitness during selection. The imitation-guided experience fusion mechanism incorporates expert knowledge and demonstrations into the learning process, accelerating the discovery of effective solutions. The adaptive stability preservation module ensures the stability and reliability of the learning process by dynamically adjusting the algorithm's hyperparameters and preserving elite policies across generations. These components work together to enhance the exploration, diversity, and stability of the learning process. The significance of this work lies in its potential to revolutionize medical data analysis, leading to more accurate diagnoses and personalized treatments. Experiments on MIMIC-III and n2c2 datasets demonstrate ERLNEIL-MDP's superior performance, achieving F1 scores of 0.933 and 0.928, respectively, representing 6.0 % and 6.7 % improvements over state-of-the-art methods. The algorithm exhibits strong convergence, high population diversity, and robustness to noise and missing data.

关键词： Evolutionary reinforcement learning medical data processing Novelty-driven exploration Imitation learning

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Computerized medical data processing system

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Biomedical Engineering 1979年第2期13卷 88-91页

作者： Vasin, Yu.G. Gromov, V.P. Gervagin, Yu.G. Smirnov, A.F. Kirilyuk, V.D. Appl. Mathemat. Cybernet. Res. Inst. Lobachevskii Univ. Gorki unknown

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Analysis and strategy development for improving the performance of the Paillier cryptosystem algorithm in medical data processing 4

Analysis and strategy development for improving the performa...

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4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Semchyshyn, Volodymyr Mykhalyk, Dmytro Ternopil Ivan Puluj National Technical University 1 Ruska str 56 Ternopil46001 Ukraine

The Paillier cryptosystem, known for its additive homomorphic properties, plays a crucial role in preserving the confidentiality of computations with encrypted medical data. However, its computational complexity poses significant challenges when applied to large-scale medical datasets, affecting both performance and efficiency. This study focuses on analyzing the performance limitations of the Paillier cryptosystem and developing strategies to improve its performance for medical data processing. This study provides a comprehensive framework for improving the performance of the Paillier cryptosystem, contributing to its effectiveness in the secure processing of medical data and paving the way for future advances in privacy-preserving cryptographic techniques. Copyright © 2024 for this paper by its authors.

关键词： Cloud Computing data Confidentiality Encryption Algorithms medical data processing Paillier Cryptosystem

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Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey

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METHODS 2025年 239卷 85-110页

作者： Ghorbian, Mohsen Ghobaei-Arani, Mostafa Ghorbian, Saied Islamic Azad Univ Dept Comp Engn Qo C Qom Iran Islamic Azad Univ Dept Mol Genet Ah C Ahar Iran

Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to its complex biological mechanisms. Early and accurate diagnosis plays a fundamental role in improving survival rates, but the limitations of existing imaging methods and clinical data interpretation often prevent optimal results. Large Language Models (LLMs), which are developed based on advanced architectures such as transformers, have brought about a significant revolution in data processing and medical decision-making. By analyzing a large volume of medical and clinical data, these models enable early diagnosis by identifying patterns in images and medical records and provide personalized treatment strategies by integrating genetic markers and clinical guidelines. Despite the transformative potential of these models, their use in BrCa management faces challenges such as data sensitivity, algorithm transparency, ethical considerations, and model compatibility with the details of medical applications that need to be addressed to achieve reliable results. This review systematically reviews the impact of LLMs on BrCa treatment and diagnosis. This study's objectives include analyzing the role of LLM technology in diagnosing and treating this disease. The findings indicate that the application of LLMs has resulted in significant improvements in various aspects of BrCa management, such as a 35% increase in the Efficiency of Diagnosis and BrCa Treatment (EDBC), a 30% enhancement in the System's Clinical Trust and Reliability (SCTR), and a 20% improvement in the quality of patient education and information (IPEI). Ultimately, this study demonstrates the importance of LLMs in advancing precision medicine for BrCa and paves the way for effective patient-centered care solutions.

关键词： Large Language Models Breast Cancer Artificial Intelligence in Oncology medical data processing LLM Applications in Cancer

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medical emergency department triage data processing using a machine-learning solution

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HELIYON 2023年第8期9卷 e18402页

作者： Vantu, Andreea Vasilescu, Anca Baicoianu, Alexandra Transilvania Univ Brasov Fac Math & Comp Sci Brasov Romania Transilvania Univ Brasov Dept Math & Comp Sci Brasov Romania

Over the years, artificial intelligence has demonstrated its ability to overcome many challenges in our day-to-day life. The evolution of it inquired more studies about Machine Learning possible solutions for different domains, including health care. The increasing demand for artificial intelligence solutions has brought accessibility to loads of data, including clinical data. The availability of medical records facilitates new opportunities to explore Machine Learning models and their abilities to process a significant amount of data and to identify patterns with the purpose of solving a medical problem. Understanding the applicability of artificial intelligence on this type of data has to be a compelling aim for emergency medicine clinicians. This paper focuses on the general clinical problem of the complex correlation between medical records and later diagnosis and, especially, on the process of emergency department triage which uses the Emergency Severity Index (ESI) as triage protocol. This study presents a comparison between three different Machine Learning models, such as Logistic Regression, Random Forest Tree and NN-Sequentail, with the purpose of classifying patients with an emergency code. We conducted four experiments because of imbalanced data. A web-based application was developed to improve the triage process after our theoretical and exploratory results. Overall, in all experiments, the NN-Sequential model had better results, having, in the first experiment, a ROC-AUC score for each ESI emergency code of: 0.59%, 0.76%, 0.71%, 0.78% 0.64%. After applying methods to balance the data, the model yielded a ROC-AUC score for each emergency code of 0.72%, 0.75%, 0.69%, 0.74%, 0.78%. In the last experiment consisting of a three-class classification problem, the NN Sequential and Random Forest Tree models had similar metric outcomes, and the NN-Sequential algorithm had a ROC-AUC score for each emergency code of: 0.76%, 0.72%, 0.84%. Without any doubt, our res

关键词： Emergency medicine Triage Clinical decision support Patient medical record medical data processing Machine learning Supervised learning algorithms

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Splitting Large medical data Sets Based on Normal Distribution in Cloud Environment

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2020年第2期8卷 518-531页

作者： Zhang, Hao Lan Zhao, Yali Pang, Chaoyi He, Jinyuan Zhejiang Univ Ctr SCDM NIT Hangzhou Zhejiang Peoples R China Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia

The surge of medical and e-commerce applications has generated tremendous amount of data, which brings people to a so-called "Big data" era. Different from traditional large data sets, the term "Big data" not only means the large size of data volume but also indicates the high velocity of data generation. However, current data mining and analytical techniques are facing the challenge of dealing with large volume data in a short period of time. This paper explores the efficiency of utilizing the Normal Distribution (ND) method for splitting and processing large volume medical data in cloud environment, which can provide representative information in the split data sets. The ND-based new model consists of two stages. The first stage adopts the ND method for large data sets splitting and processing, which can reduce the volume of data sets. The second stage implements the ND-based model in a cloud computing infrastructure for allocating the split data sets. The experimental results show substantial efficiency gains of the proposed method over the conventional methods without splitting data into small partitions. The ND-based method can generate representative data sets, which can offer efficient solution for large data processing. The split data sets can be processed in parallel in Cloud computing environment.

关键词： data splitting cloud computing Big data medical data processing normal distribution

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A cognitive technology based healthcare monitoring system and medical data transmission

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MEASUREMENT 2019年 146卷 322-332页

作者： Kumar, M. Arun Vimala, R. Britto, K. R. Aravind PSNA Coll Engn & Technol Dept IT Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept EEE Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept ECE Dindigul Tamil Nadu India

In healthcare management, the volume of patient data generated from various medical devices used for patient monitoring. Increasing healthcare data is persisted in distributed database available in cloud, for sharing among medical experts. Cloud provides most excellent methods to explore the organized and unorganized data prepared from healthcare management systems. By analyzing the healthcare data, the health conditions and the treatment methods are predicted for future treatment. Yet, the existing design could not sustain both analysis and procedure for huge quantity of multi-organized healthcare data. In this paper, it is aimed to provide a cognitive technology for efficient monitoring and transmission of medical data in a healthcare industry. To do that, a Cognitive data Transmission Method (CDTM) is proposed for monitor, record and transmit the data patient's health related data. A highest priority is assigned in data transmission, when the health condition is critical, which can be identified from the data-packet information. The cognitive nature is obtained using Simulated Annealing method. Finally, a stochastic prophesy representation is intended to predict the future health condition of the most related patients based on their present health conditions. Performance assessment of the planned procedure is recognized through general model in the cloud surroundings, that ensures 98% accurateness of prediction and sustain 99% of CPU and bandwidth consumption to decrease the analysis time (C) 2019 Published by Elsevier Ltd.

关键词： medical data processing Big-data Healthcare monitoring system Clustering and classification

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The role of medical data in efficient patient care delivery: a review

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RISK MANAGEMENT AND HEALTHCARE POLICY 2019年 12卷 67-73页

作者： Adane, Kasaw Gizachew, Mucheye Kendie, Semalegne Univ Gondar Unit Qual Assurance & Lab Management Sch Biomed & Lab Sci POB 196 Gondar Ethiopia Univ Gondar Sch Biomed & Lab Sci Dept Med Microbiol Gondar Ethiopia Univ Gondar Sch Sociol & Social Work Dept Social Work Gondar Ethiopia

Background: Implementing accurate data management systems ensure safe and efficient transfer of confidential health care data. However, health care professionals overlooked their important tasks of medical data processing. Hence, using high-quality electronic health record (EHR) applications in health care is important to minimize medical errors. Therefore, this review tries to indicate the roles of EHR in advancing quality health care service provisions. Methods: The keywords identified were EHR, EMR, medical data processing, medical data retention, medical data destruction, health care, and patient care, and a few related terms with different combinations. PubMed (National Library of Medicine), Google Scholar, and Google search engine were used to search for articles from those databases. Searching was done using boolean words "AND", "OR", and "NOT" using all [All fields] and [MeSH Terms] searching strategies. Results: Articles were screened using the title, checked by their abstract, and the remaining related full-text materials were included or excluded by two individuals deciding its eligibility. Finally, 73 materials issued from 2013-2018 were used for qualitatively synthesizing and reconciling the idea to produce this review article. Conclusion: Poor medical data processing systems are the key reasons for medical errors. Employing standardized data management systems reduce errors and associated sufferings. Therefore, using electronic tools in the health care institution ensures safe and efficient data management. Therefore, it is important to establish appropriate medical data management systems for efficient health care delivery.

关键词： electronic medical data health care data medical data processing

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On the building of self-adaptable systems to efficiently manage medical data 22

On the building of self-adaptable systems to efficiently man...

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22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Sanchez-Gallegos, Genaro Sanchez-Gallegos, Dante D. Gonzalez-Compean, J. L. Carretero, Jesus Cinvestav Tamaulipas Victoria Mexico Univ Carlos III Madrid Madrid Spain

ISBN: (纸本)9781665499569

The systems that meet non-functional requirements (NFRs) are key for e-health services to face up events such as service outages and violations of confidentiality. However, traditional NFR systems produce overhead in execution time, which could affect critical decision-making processes. This paper presents a dynamic parallel pattern construction model to design and create efficient NFR self-adaptable systems. The construction of patterns is performed in two design phases: in the first one, the designers build NFR systems by creating pipelines including as many applications as required to meet the NFRs established by healthcare organizations. In the second phase, a pipeline is converted into a worker that automatically is added to a dynamic pattern. In a dynamic pattern, the workers can be cloned to be executed by different parallel patterns (e.g., manager/worker, divide&conquer, etc.) to face changes in the incoming workload during execution time, which converts a worker into a self-adaptable NFR system. A prototype was implemented to create self-adaptable NFR systems, which were used in a case study to manage spirometry studies, tomography images, and electrocardiograms. The evaluation showed the effectiveness of this dynamic pattern model to create self-adaptable systems when processing multiple types of medical data/contents. The evaluation also revealed that the self-adaptable NFR systems built by dynamic patterns yielded significant performance gain in a direct comparison with the implementation of NFR application pipelines built by a traditional framework called Jenkins.

关键词： Self-adaptable systems Dynamic patterns medical data processing Cloud computing Non-functional requirements

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