论文标题
智能监测和诊断的深度学习和健康信息学
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis
论文作者
论文摘要
使用信息技术的设计和提供医疗服务的连接被称为健康信息学。它涉及使用多层深度学习技术的神经网络对综合医学分析进行数据使用，验证和转移，以分析复杂数据。例如，Google将“ Deepmind”的“ DeepMind”健康移动工具集成在一起，以整合\＆利用增强向患者提供专业医疗保健服务所需的医疗数据。伦敦Moorfield眼科医院引入了DeepMind研究算法，其中具有数十种视网膜扫描属性，而DeepMind UCL使用CT \＆MRI扫描工具处理了癌组织的鉴定。 Atomise分析了具有深度学习神经网络的药物和化学物质，以识别准确的临床前处方。健康信息学使医学保健使智能，互动，具有成本效益且可访问；特别是使用DL应用工具来检测疾病的实际原因。神经网络工具的广泛使用导致了不同的医学学科的扩展，这些医学学科可以使用目标点标签数据检测器来降低数据的复杂性并增强3-4D重叠图像，从而支持数据增强，不易于征服的学习，多模式性和转移学习架构。多年来，健康科学专注于人工智能工具，用于提供护理，慢性护理管理，预防/健康，临床支持和诊断。他们的研究结果通过心信号计算机辅助诊断工具（CADX）和其他提供护理，诊断\＆治疗的多功能深度学习技术导致心脏骤停诊断。健康信息学通过对间质肺疾病进行分类的医学图像提供了对人体器官的监测结果，检测了重建\＆肿瘤分割的图像结节。新兴的医学研究应用产生了临床病理学的人级表现工具，用于处理放射学，眼科和牙齿诊断。这项研究将评估方法，深度学习架构，方法，生物信息学，指定功能需求，监视工具，ANN（人工神经网络），数据标记\＆注释算法，以控制数据验证，建模和使用智能监测健康信息的不同疾病来控制数据验证，建模和诊断。
The connection between the design and delivery of health care services using information technology is known as health informatics. It involves data usage, validation, and transfer of an integrated medical analysis using neural networks of multi-layer deep learning techniques to analyze complex data. For instance, Google incorporated ''DeepMind'' health mobile tool that integrates \& leverage medical data needed to enhance professional healthcare delivery to patients. Moorfield Eye Hospital London introduced DeepMind Research Algorithms with dozens of retinal scans attributes while DeepMind UCL handled the identification of cancerous tissues using CT \& MRI Scan tools. Atomise analyzed drugs and chemicals with Deep Learning Neural Networks to identify accurate pre-clinical prescriptions. Health informatics makes medical care intelligent, interactive, cost-effective, and accessible; especially with DL application tools for detecting the actual cause of diseases. The extensive use of neural network tools leads to the expansion of different medical disciplines which mitigates data complexity and enhances 3-4D overlap images using target point label data detectors that support data augmentation, un-semi-supervised learning, multi-modality and transfer learning architecture. Health science over the years focused on artificial intelligence tools for care delivery, chronic care management, prevention/wellness, clinical supports, and diagnosis. The outcome of their research leads to cardiac arrest diagnosis through Heart Signal Computer-Aided Diagnostic tool (CADX) and other multifunctional deep learning techniques that offer care, diagnosis \& treatment. Health informatics provides monitored outcomes of human body organs through medical images that classify interstitial lung disease, detects image nodules for reconstruction \& tumor segmentation. The emergent medical research applications gave rise to clinical-pathological human-level performing tools for handling Radiological, Ophthalmological, and Dental diagnosis. This research will evaluate methodologies, Deep learning architectures, approaches, bio-informatics, specified function requirements, monitoring tools, ANN (artificial neural network), data labeling \& annotation algorithms that control data validation, modeling, and diagnosis of different diseases using smart monitoring health informatics applications.
