Relevant bibliographies by topics / Healthcare Analytics Platform

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Academic literature on the topic 'Healthcare Analytics Platform'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Healthcare Analytics Platform"

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Suman Etikala, Suman Etikala. "Healthcare Analytics Platform: Engineering the Future of Data-Driven Healthcare." International Journal of Advances in Engineering and Management 7, no. 4 (2025): 167–73. https://doi.org/10.35629/5252-0704167173.

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This article examines the implementation of an advanced healthcare analytics platform that transformed patient care delivery and operational efficiency across a major healthcare network. The solution addresses critical challenges in data integration, regulatory compliance, and operational demands while demonstrating the effectiveness of a structured three-phase implementation approach. The platform successfully integrates diverse healthcare data sources including Electronic Health Records, clinical systems, IoT devices, and external databases while maintaining strict HIPAA compliance. The impl
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Arjun, R., and Sunil C. D'Souza. "Software Analytics Platform for Converged Healthcare Technologies." Procedia Technology 24 (2016): 1431–35. http://dx.doi.org/10.1016/j.protcy.2016.05.169.

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Štufi, Martin, Boris Bačić, and Leonid Stoimenov. "Big Data Analytics and Processing Platform in Czech Republic Healthcare." Applied Sciences 10, no. 5 (2020): 1705. http://dx.doi.org/10.3390/app10051705.

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Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms,
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Researcher. "ADVANCING CLINICAL DECISION SUPPORT: A TECHNICAL ANALYSIS OF IBM WATSON HEALTH'S AI-DRIVEN HEALTHCARE ANALYTICS PLATFORM." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1265–75. https://doi.org/10.5281/zenodo.14170059.

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This technical article comprehensively examines IBM Watson Health's AI-driven healthcare analytics platform, focusing on its implementation framework and clinical applications. The article explores the platform's advanced analytics capabilities, diagnostic support systems, and specialized oncology applications while providing detailed insights into its technical architecture and deployment methodologies. The article demonstrates how machine learning algorithms, real-time analytics processing, and sophisticated data integration mechanisms work together to create an effective healthcare decision
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López-Martínez, Fernando, Edward Rolando Núñez-Valdez, Vicente García-Díaz, and Zoran Bursac. "A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management." Algorithms 13, no. 4 (2020): 102. http://dx.doi.org/10.3390/a13040102.

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Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population he
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More, Priyanka, Sachin Sakhare, Manya Gupta, Kirti Singh, Om Gavhane, and Rushikesh Mantri. "Predictive Analytics in Healthcare: Empowering Consultation with Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 12, no. 1 (2023): 69–76. http://dx.doi.org/10.17762/ijritcc.v12i1.7912.

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The Smart Healthcare and Online Consultation initiative intends to offer patients a quick and convenient online platform for seeking medical advice and services. Real-time video consultations, appointment scheduling, prescription administration, and health records management are just a few of the capabilities available on the platform. To deliver individualized and superior healthcare services, the initiative to use cutting-edge such as AI, ML, and data analytics. By giving patients an easy and affordable way to receive healthcare services remotely, the Smart Healthcare and Online Consultation
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Asri, Hiba, Hajar Mousannif, and Hassan Al Moatassime. "Big Data Analytics in Healthcare." International Journal of Distributed Systems and Technologies 10, no. 4 (2019): 45–58. http://dx.doi.org/10.4018/ijdst.2019100104.

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Sensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transm
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Babu, Gokulnath Chandra, and S. P. Shantharajah. "Survey on data analytics techniques in healthcare using IOT platform." International Journal of Reasoning-based Intelligent Systems 10, no. 3/4 (2018): 183. http://dx.doi.org/10.1504/ijris.2018.096197.

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Babu, Gokulnath Chandra, and S. P. Shantharajah. "Survey on data analytics techniques in healthcare using IOT platform." International Journal of Reasoning-based Intelligent Systems 10, no. 3-4 (2018): 183. http://dx.doi.org/10.1504/ijris.2018.10017495.

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Suzuki, K. "New platform of data analytics for mental health." European Psychiatry 33, S1 (2016): S33. http://dx.doi.org/10.1016/j.eurpsy.2016.01.863.

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IntroductionMental disorder is a key public health challenge and a leading cause of disability-adjusted life years (DALYs) due to its high level of disability and mortality. Therefore, a slight improvement on mental care provision and management could generate solid benefits on relieving the social burden of mental diseases.ObjectivesThis paper presents a long-term vision of strategic collaboration between Fujitsu Laboratories, Fujitsu Spain, and Hospital Clinico San Carlos to generate value through predictive and preventive medicine improving healthcare outcomes for every clinical area, benef
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Books on the topic "Healthcare Analytics Platform"

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van, José. Healthcare and Health Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190889760.003.0006.

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This chapter focuses on the platformization of the health sector. A burgeoning field of online health platforms has emerged, ranging from personal fitness apps to medical platforms that are used by patients, doctors, and researchers. A global industry of health-related platforms is being stacked onto, and interwoven with, the infrastructural core of the ecosystem; developing sectoral health platforms is a potentially lucrative and data-rich area that major operators are keen to invest in. Examining health platforms, the chapter unravels a peculiar double-edged logic in how their benefit is arg
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Book chapters on the topic "Healthcare Analytics Platform"

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Gancheva, Veska, and Ivaylo Georgiev. "Computer aided platform for healthcare data analytics." In System Innovation for an Artificial Intelligence Era. CRC Press, 2024. http://dx.doi.org/10.1201/9781003514831-74.

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Mouchakis, Giannis, Babis Kostopoulos, Stasinos Konstantopoulos, et al. "A Cloud-Native NGS Data Processing and Annotation Platform." In Heterogeneous Data Management, Polystores, and Analytics for Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93663-1_10.

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Kathiravelu, Pradeeban, and Ashish Sharma. "A Dynamic Data Warehousing Platform for Creating and Accessing Biomedical Data Lakes." In Data Management and Analytics for Medicine and Healthcare. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57741-8_7.

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Kaur, Jagreet, and Kulwinder Singh Mann. "AI Based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0755-3_11.

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Sujatha, R., S. Nathiya, and Jyotir Moy Chatterjee. "Clinical Data Analysis Using IoT Data Analytics Platforms." In Internet of Things Use Cases for the Healthcare Industry. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37526-3_12.

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Casanova, Ana, Alicia Gomis-Berenguer, Dale Brownson, and Jesús Iniesta. "Mechanisms for Analytes Biosensing: Platforms from Single Use to Intermittent or Continuous Monitoring." In Biosensors for Personalized Healthcare. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5473-1_9.

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YongQi, Z., S. Chan-Bormei, and H. Miri. "Transforming Healthcare with Immersive Visualization: An Analysis of Virtual and Holographic Health Information Platforms." In Learning and Analytics in Intelligent Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56521-2_8.

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Privietha, P., Dhruva Teja Kandalam Sunil, K. Thangaraj, Vikrant Sharma, Srikanth Cherukuvada, and Thomas Koilraj. "Artificial Intelligence Augmented Public Health Platforms for Community Oriented Predictive Healthcare Analytics and Telemedicine Solutions." In Advances in Computer Science Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-718-2_102.

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Sykes, Edward R., Jinhe Zhang, and Uri Sevilla. "Assisting Personal Support Worker’s e-Training with AI Prediction." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90341-0_13.

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Abstract The increasing need for effective caregiver training, particularly for Personal Support Workers, has led to the development of innovative e-training platforms. This study explores the application of advanced ML models to predict training outcomes and identify at-risk learners early in the process. The primary goal is to improve training completion rates while ensuring compliance with industry standards. We employed a range of ML models, including Decision Trees, Random Forest, Support Vector Machines, Neural Networks, to predict the likelihood of successful course completion using a dataset comprising over 27 million user interaction records. Feature engineering was used to extract key metrics such as module and lesson completion ratios. The results indicate that the Multilayer Perceptron model performed best, achieving an AUC score of 0.99, while K-NN also demonstrated strong performance with an AUC of 0.98. Key features such as module completion ratio and temporal progress were found to be significant predictors of training success. These findings suggest that integrating predictive analytics into e-training platforms can significantly enhance the effectiveness of PSW certification processes, ultimately supporting the growing demand for skilled caregivers in healthcare.
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R., Sapna, and Bentham Science Publisher Preethi. "Healthcare Applications Centered on AIoT." In IoT and Big Data Analytics. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815196054123050020.

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The Internet of Things (IoT) is a quickly expanding environment which combines software, hardware, physical components, as well as computing tools for data collection, sharing, or rather interaction. The IoT enables a unified platform for humans to interact with a wide range of physical and virtual objects, like personalised healthcare domains. Due to the explosive growth and advancement of the internet, traditional patient care strategies have enhanced with the replacing e-medical records mechanisms. The use of IoT technology provides medical modern healthcare equipment device setting for both physicians and clients. IoT devices and Artificial Intelligence are beneficial in many implementations, starting with remote weather monitoring to mechanical mechanisation. Furthermore, medical care applications are showing a strong interest in IoT devices due to cost savings, easiness of using it, and an increase in service quality. The most recent services for IoT-based healthcare, which have been investigated and are still facing challenges in the clinical setting, are required for intellectual, creative solutions. An exploration of prospects for artificial intelligence and the internet of things in the medical sector is provided in this chapter.
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Conference papers on the topic "Healthcare Analytics Platform"

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Foschi, Antonio, Domenico Pistilli, Gaia Bondani, and Fabio Rebecchi. "Developing a Prototype Healthcare Data Platform for Advanced Analytics in Rehabilitation Environments." In 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI). IEEE, 2024. http://dx.doi.org/10.1109/rtsi61910.2024.10761573.

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Jogi, Karan, Stephanie Greenwald, Marco Colombo, Sarah Owens, and Rigel Gjomemo. "VISTA: Variant Insights, Surveillance and Tracking Analytics, a Visualization Platform for Public Health." In 2024 IEEE 15th Workshop on Visual Analytics in Healthcare (VAHC). IEEE, 2024. https://doi.org/10.1109/vahc65315.2024.00008.

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Kunderu, Pranav, Kshitij Purani, and Shivm Patel. "Post-Care Continuum: Predictive Analytics Platform for Continuous Patient Monitoring After Hospital Discharge." In 2024 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT). IEEE, 2024. https://doi.org/10.1109/hi-poct64255.2024.10876432.

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Li, Yang, Bian Tang, Yang Zhao, Xinxin Lin, and Qian Deng. "Establishment of Information Platform for Enterprise Health Management Using Digital Intelligence and Analytics." In 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, 2024. https://doi.org/10.1109/ecbios61468.2024.10885438.

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V, Dankan Gowda, Chaithra S M, Shantanu Sudhir Gujar, Salman Firoz Shaikh, Balaji Shesharao Ingole, and N. Sudhakar Reddy. "Scalable AI Solutions for IoT-based Healthcare Systems using Cloud Platforms." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714810.

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Deshmukh, Shantanu, and Natalia Khuri. "PediatricDB: Data Analytics Platform for Pediatric Healthcare." In 2018 Thirteenth International Conference on Digital Information Management (ICDIM). IEEE, 2018. http://dx.doi.org/10.1109/icdim.2018.8847072.

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Iyengar, Arun, Ashish Kundu, Upendra Sharma, and Ping Zhang. "A Trusted Healthcare Data Analytics Cloud Platform." In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2018. http://dx.doi.org/10.1109/icdcs.2018.00123.

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Chrimes, Dillon, Belaid Moa, Hamid Zamani, and Mu-Hsing Kuo. "Interactive Healthcare Big Data Analytics Platform under Simulated Performance." In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)2016. IEEE, 2016. http://dx.doi.org/10.1109/dasc-picom-datacom-cyberscitec.2016.140.

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Moatamed, Babak, Sajad Darabi, Migyeong Gwak, et al. "Sport analytics platform for athletic readiness assessment." In 2017 IEEE Healthcare Innovations and Point-of-Care Technologies (HI-POCT). IEEE, 2017. http://dx.doi.org/10.1109/hic.2017.8227608.

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Höhn, Markus, Hendrik Lücke-Tieke, Jan Burmeister, and Jörn Kohlhammer. "Towards medhub: A Self-Service Platform for Analysts and Physicians." In 2023 Workshop on Visual Analytics in Healthcare (VAHC). IEEE, 2023. http://dx.doi.org/10.1109/vahc60858.2023.00016.

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