What are the current trends and developments in data representation methods for smart computing in healthcare?
Best insight from top research papers
Current trends and developments in data representation methods for smart computing in healthcare encompass various technologies and approaches. These include leveraging complex event-processing (CEP) technology for real-time extraction of critical insights from massive data streams , utilizing natural language processing (NLP) for analysing human language in smart healthcare applications , employing soft computing methods like neural networks and machine learning for predictive and classification tasks in healthcare systems , integrating IoT and cloud computing to enhance healthcare systems' performance and information sharing capabilities , and exploring natural computation and unsupervised learning methods for innovative problem-solving and data-centric operations in healthcare industries . These advancements aim to improve decision-making, efficiency, and resource utilization in healthcare settings.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
01 Jan 2022 1 Citations | Natural computing and unsupervised learning methods are pivotal in enhancing data-centric operations in smart healthcare, offering innovative data representation techniques for improved healthcare systems. |
23 Dec 2022 | Cloud-IoT based smart healthcare frameworks integrate IoT devices and cloud computing to enhance healthcare systems, enabling seamless data sharing among patients, medical professionals, and institutions, improving performance and efficiency. |
| Soft computing methods like neural networks and machine learning are trending in healthcare for dynamic diagnostic processes. The paper introduces a weighted averaging ensemble technique for dermatological disease classification. | |
Open access•Posted Content | Data representation methods in smart healthcare utilize natural language processing for tasks like feature extraction and modeling, enhancing applications in clinical practice, hospital management, personal care, public health, and drug development. |
| The paper introduces a novel data analysis framework in smart healthcare using complex event-processing technology, Viterbi algorithm, and SVM for real-time patient condition estimation and emergency notifications to physicians. |
Related Questions
What are the latest trends in healthcare systems?5 answersHealthcare systems are currently experiencing several trends. There is a shift towards decentralization of responsibilities, with governments moving towards reimbursement schemes that reward good outcomes and performance. Transparency, accountability, and competition are being emphasized, along with a focus on preventive initiatives and empowering patients to make decisions about their own health. Additionally, the impact of the COVID-19 pandemic and geopolitical events has highlighted the need for increased cyber security, reliance on digital technologies, and coordinated attacks in healthcare systems. Demographic aging, reduced economic resources, violence against emergency medical staff, and the increased need for psychosocial support are also important trends. Clinical systems have emerged as a top IT priority, with a focus on improving safety, efficiency, and effectiveness of healthcare. Furthermore, the use of artificial intelligence (AI) and big data analytics is seen as crucial in developing smart healthcare systems, with applications in administration, adaptability, productivity, and efficiency.
How ai can describe health related data?5 answersAI can describe health-related data by utilizing various techniques and algorithms to analyze and interpret the data patterns. These AI techniques can be applied to different types of health data, such as multi-omics, clinical, behavioral, environmental, and pharmaceutical research and development (R&D) data. The use of AI in healthcare allows for personalized care and improved performance in tasks related to disease prevention, health monitoring, and healthcare research. AI models can be trained on electronic health record (EHR) data, which is collected during treatment and can provide valuable insights into patient health. However, there are challenges in integrating and interpreting multi-modal data, ensuring algorithm performance and interpretability, protecting model security, and addressing model bias. Overall, AI has the potential to revolutionize healthcare by effectively analyzing and understanding health-related data for better decision-making and patient care.
What are the challenges and opportunities for data strategy in healthcare?5 answersThe challenges and opportunities for data strategy in healthcare are significant. The use of big data analytics in healthcare presents opportunities to improve patient outcomes, reduce costs, and enhance clinical decision-making. However, there are challenges associated with implementing big data analytics in healthcare, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. In addition, the healthcare industry faces barriers in terms of inconsistent terminology, non-standardized coding practices, lack of longitudinal data, and limited access and sharing of real-world data (RWD). Overcoming these challenges requires the development of collaborative research networks, standardized data processing and validation practices, and the use of cutting-edge technological innovations and data solutions. By addressing these challenges, healthcare stakeholders can harness the power of data to make smarter decisions and improve healthcare delivery.
How federated learning is used in data privacy in healthcare domain?3 answersFederated learning is used in the healthcare domain to address privacy concerns in data sharing. It allows multiple entities to collaboratively train machine learning models without sharing their data. This is particularly important in healthcare where data privacy and security are major concerns. By using federated learning, healthcare organizations can protect sensitive medical data while still benefiting from the insights gained through machine learning. Several papers propose different methods to enhance privacy in federated learning. Gobinath and Shanmugam propose a method called Polymorphic Cipher Functional Encryption that integrates differential privacy with functional encryption to minimize noise infusion and maintain privacy. Kumar and Suguna introduce FL+DQRE-SCnet, a privacy-preserving technique that combines deep Q reinforcement learning with spectral clustering and homomorphic encryption to enhance the accuracy of disease diagnosis while protecting privacy. PETs (Privacy Enhancing Technologies) are also discussed as tools that can be used to enhance privacy and security in federated learning in the healthcare domain.
Which data mining techniques for healthcare?5 answersClassification techniques are commonly used in data mining for healthcare applications. These techniques involve the use of supervised learning algorithms such as decision trees, logistic regression, and support vector machines to predict disease diagnosis, patient outcomes, and potential risk factors. Additionally, classification techniques are also applied in image recognition and analysis in fields like radiology and pathology. Other data mining techniques used in healthcare include predictive modeling, clustering, and association rule mining. These techniques help in transforming healthcare delivery and research by providing insights from healthcare data. Furthermore, the application of machine learning algorithms like linear regression and multi-layer perceptron has been proposed for detecting diseases such as diabetes in healthcare datasets. Overall, data mining techniques play a crucial role in healthcare by enabling accurate disease diagnosis, treatment planning, and patient care, as well as supporting evidence-based decision-making.
What is the current state of artificial intelligence (AI) in healthcare?3 answersThe current state of artificial intelligence (AI) in healthcare is rapidly evolving and has the potential to revolutionize the industry. AI offers personalized, accurate, and innovative solutions that can enhance patient outcomes, diagnosis, and treatment while lowering costs and increasing efficiency. AI is aimed to complement, rather than replace, doctors and healthcare providers, and the human-AI collaboration is crucial for maintaining safety and quality in healthcare services. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications in disease diagnosis, risk assessment, treatment planning, and drug discovery. The utilization of AI in the healthcare sector has seen significant progress, especially in deep learning algorithms and artificial neural networks, and has the potential to improve precision medicine and clinical diagnostic tasks. AI in healthcare aims to make machines more useful in solving healthcare challenges, such as early detection of chronic diseases, reducing financial burden, and severity of diseases.