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Artificial Intelligence - Applications in Medicine and Biology最新文献

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Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding 利用人工智能和基于大数据的文档优化医疗编码
Artificial Intelligence - Applications in Medicine and Biology Pub Date : 2019-06-13 DOI: 10.5772/INTECHOPEN.85749
Joseph Noussa-Yao, D. Heudes, P. Degoulet
{"title":"Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding","authors":"Joseph Noussa-Yao, D. Heudes, P. Degoulet","doi":"10.5772/INTECHOPEN.85749","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85749","url":null,"abstract":"Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model.","PeriodicalId":162168,"journal":{"name":"Artificial Intelligence - Applications in Medicine and Biology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114349393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research 设计数据驱动的学习算法:确保有效的后基因组医学和生物医学研究的必要性
Artificial Intelligence - Applications in Medicine and Biology Pub Date : 2019-05-15 DOI: 10.5772/INTECHOPEN.84148
G. Mazandu, I. Kyomugisha, Ephifania Geza, Milaine Seuneu, B. Bah, E. Chimusa
{"title":"Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research","authors":"G. Mazandu, I. Kyomugisha, Ephifania Geza, Milaine Seuneu, B. Bah, E. Chimusa","doi":"10.5772/INTECHOPEN.84148","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84148","url":null,"abstract":"Advances in sequencing technology have significantly contributed to shaping the area of genetics and enabled the identification of genetic variants associated with complex traits through genome-wide association studies. This has provided insights into genetic medicine, in which case, genetic factors influence variability in disease and treatment outcomes. On the other side, the missing or hidden heritability has suggested that the host quality of life and other environmental factors may also influence differences in disease risk and drug/treatment responses in genomic medicine, and orient biomedical research, even though this may be highly con-strained by genetic capabilities. It is expected that combining these different factors can yield a paradigm-shift of personalized medicine and lead to a more effective medical treatment. With existing “big data” initiatives and high-performance computing infrastructures, there is a need for data-driven learning algorithms and models that enable the selection and prioritization of relevant genetic variants (post-genomic medicine) and trigger effective translation into clinical practice. In this chapter, we survey and discuss existing machine learning algorithms and post-genomic analysis models supporting the process of identifying valuable markers.","PeriodicalId":162168,"journal":{"name":"Artificial Intelligence - Applications in Medicine and Biology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131525987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Survey on 3D Ultrasound Reconstruction Techniques 三维超声重建技术综述
Artificial Intelligence - Applications in Medicine and Biology Pub Date : 2019-04-27 DOI: 10.5772/INTECHOPEN.81628
F. Mohamed, Chan Vei Siang
{"title":"A Survey on 3D Ultrasound Reconstruction Techniques","authors":"F. Mohamed, Chan Vei Siang","doi":"10.5772/INTECHOPEN.81628","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81628","url":null,"abstract":"This book chapter aims to discuss the 3D ultrasound reconstruction and visualization. First, the various types of 3D ultrasound system are reviewed, such as mechanical, 2D array, position tracking-based freehand, and untrackedbased freehand. Second, the 3D ultrasound reconstruction technique or pipeline used by the current existing system, which includes the data acquisition, data preprocessing, reconstruction method and 3D visualization, is discussed. The reconstruction method and 3D visualization will be emphasized. The reconstruction method includes the pixel-based method, volume-based method, and function-based method, accompanied with their benefits and drawbacks. In the 3D visualization, methods such as multiplanar reformatting, volume rendering, and surface rendering are presented. Lastly, its application in the medical field is reviewed as well.","PeriodicalId":162168,"journal":{"name":"Artificial Intelligence - Applications in Medicine and Biology","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 28
Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine 大数据时代的放射肿瘤学和精准医疗的机器学习
Artificial Intelligence - Applications in Medicine and Biology Pub Date : 2019-03-20 DOI: 10.5772/INTECHOPEN.84629
A. Osman
{"title":"Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine","authors":"A. Osman","doi":"10.5772/INTECHOPEN.84629","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84629","url":null,"abstract":"Machine learning (ML) applications in medicine represent an emerging field of research with the potential to revolutionize the field of radiation oncology, in particular. With the era of big data, the utilization of machine learning algorithms in radiation oncology research is growing fast with applications including patient diagnosis and staging of cancer, treatment simulation, treatment planning, treatment delivery, quality assurance, and treatment response and outcome predictions. In this chapter, we provide the interested reader with an overview of the ongoing advances and cutting-edge applications of state-of-the-art ML techniques in radiation oncology process from the radiotherapy workflow perspective, starting from patient’s diagnosis to follow-up. We present with discussion the areas where ML has presently been used and also areas where ML could be applied to improve the efficiency (i.e., optimizing and automating the clinical processes) and quality (i.e., potentials for decision-making support toward a practical application of precision medicine in radiation therapy) of patient care.","PeriodicalId":162168,"journal":{"name":"Artificial Intelligence - Applications in Medicine and Biology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124837970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
A Review of EMG Techniques for Detection of Gait Disorders 肌电图技术检测步态障碍的综述
Artificial Intelligence - Applications in Medicine and Biology Pub Date : 2019-03-01 DOI: 10.5772/INTECHOPEN.84403
Rajat Emanuel Singh, K. Iqbal, G. White, J. Holtz
{"title":"A Review of EMG Techniques for Detection of Gait Disorders","authors":"Rajat Emanuel Singh, K. Iqbal, G. White, J. Holtz","doi":"10.5772/INTECHOPEN.84403","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84403","url":null,"abstract":"Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices.","PeriodicalId":162168,"journal":{"name":"Artificial Intelligence - Applications in Medicine and Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130932064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Quantum Neural Machine Learning: Theory and Experiments 量子神经机器学习:理论与实验
Artificial Intelligence - Applications in Medicine and Biology Pub Date : 2019-02-04 DOI: 10.5772/INTECHOPEN.84149
C. Gonçalves
{"title":"Quantum Neural Machine Learning: Theory and Experiments","authors":"C. Gonçalves","doi":"10.5772/INTECHOPEN.84149","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84149","url":null,"abstract":"Cloud-based access to quantum computers opens up the way for the empirical implementation of quantum artificial neural networks and for the future integration of quantum computation in different devices, using the cloud to access a quantum computer. The current work experimentally implements quantum artificial neural networks on IBM ’ s quantum computers, accessed via cloud. Examples are provided for the XOR Boolean function representation problem and decision under risk; in the last case, quantum object-oriented programming using IBM ’ s Qiskit Python library is employed to implement a form of quantum neural reinforcement learning applied to a classical decision under risk problem, showing how decision can be integrated into a quantum artificial intelligence system, where an artificial agent learns how to select an optimal action when facing a classical gamble. A final reflection is provided on quantum robotics and a future where robotic systems are connected to quantum computers via cloud, using quantum neural computation to learn to optimize tasks and act accordingly.","PeriodicalId":162168,"journal":{"name":"Artificial Intelligence - Applications in Medicine and Biology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127947240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
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