Relevant bibliographies by topics / Machine learning in healthcare

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine learning in healthcare'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Machine learning in healthcare"

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Vasundhara, S. "Challenges of Machine Learning in Healthcare Industry." International Journal of Science and Research (IJSR) 12, no. 7 (2023): 495–97. http://dx.doi.org/10.21275/sr23706105157.

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Narayan Koranchirath, Nithin. "Impact of Machine Learning on Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 942–47. http://dx.doi.org/10.21275/sr24210203022.

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Winter, George. "Machine learning in healthcare." British Journal of Healthcare Management 25, no. 2 (2019): 100–101. http://dx.doi.org/10.12968/bjhc.2019.25.2.100.

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Haritima, Haritima, S. Sakena Benazer, Tatiraju V. Rajani Kanth, and K. Dhineshkumar. "An Adaptive Learning-Driven Software Ecosystem for Optimized Healthcare Solutions with Artificial Intelligence." International Journal of BIM and Engineering Science 09, no. 2 (2024): 45–54. http://dx.doi.org/10.54216/ijbes.090206.

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The use of machine learning methods in healthcare has shown encouraging outcomes in terms of better patient care, more efficient use of resources, and streamlined operations. Traditional machine learning methods encounter difficulties when dealing with healthcare data due to its complexity and heterogeneity. Healthcare applications are a good fit for Gradient Boosting Machines (GBMs), which have become a formidable tool for structured data and predictive modelling jobs. Better healthcare system capabilities, including more precise forecasts and well-informed decisions, may be achieved by the i
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Kambala, Mahesh. "AI-Powered Healthcare: Transforming Patient Outcomes with Machine Learning." Journal of Medical Science and clinical Research 12, no. 08 (2024): 34–47. http://dx.doi.org/10.18535/jmscr/v12i08.07.

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AI and ML have flooded the healthcare industry with new technological approaches to affect patient experiences through smart approaches towards predictability, treatment, and diagnosis. The following paper focuses on exploring the effects caused by the implementation of artificial intelligence technologies in the sphere of healthcare. This research explores different case studies to prove that early diagnosis, treatment customization, and organizational effectiveness are all driven by AI. The paper is concerned with the approaches used in the implementation of artificial intelligence in the he
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Chen, Irene Y., Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi. "Ethical Machine Learning in Healthcare." Annual Review of Biomedical Data Science 4, no. 1 (2021): 123–44. http://dx.doi.org/10.1146/annurev-biodatasci-092820-114757.

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The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
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Siddique, Sarkar, and James C. L. Chow. "Machine Learning in Healthcare Communication." Encyclopedia 1, no. 1 (2021): 220–39. http://dx.doi.org/10.3390/encyclopedia1010021.

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare c
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Chen, Irene Y., Shalmali Joshi, Marzyeh Ghassemi, and Rajesh Ranganath. "Probabilistic Machine Learning for Healthcare." Annual Review of Biomedical Data Science 4, no. 1 (2021): 393–415. http://dx.doi.org/10.1146/annurev-biodatasci-092820-033938.

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Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.
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Arora, Aaryan, and Nirmalya Basu. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology 3, no. 4 (2023): 12–18. http://dx.doi.org/10.54105/ijamst.d3037.063423.

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Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article, we delve into the profoun
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Aaryan, Arora, and Basu Nirmalya. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology (IJAMST) 3, no. 4 (2023): 12–18. https://doi.org/10.54105/ijamst.D3037.063423.

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<strong>Abstract: </strong>Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care.Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article,
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Dissertations / Theses on the topic "Machine learning in healthcare"

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Laczik, Tamás. "Encoding Temporal Healthcare Data for Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299433.

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This thesis contains a review of previous work in the fields of encoding sequential healthcare data and predicting graft- versus- host disease, a medical condition, based on patient history using machine learning. A new encoding of such data is proposed for machine learning purposes. The proposed encoding, called bag of binned weighted events, is a combination of two strategies proposed in previous work, called bag of binned events and bag of weighted events. An empirical experiment is designed to evaluate the predictive performance of the proposed encoding over various binning windows to that
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Gligorijevic, Djordje. "Predictive Uncertainty Quantification and Explainable Machine Learning in Healthcare." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/520057.

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Computer and Information Science<br>Ph.D.<br>Predictive modeling is an ever-increasingly important part of decision making. The advances in Machine Learning predictive modeling have spread across many domains bringing significant improvements in performance and providing unique opportunities for novel discoveries. A notably important domains of the human world are medical and healthcare domains, which take care of peoples' wellbeing. And while being one of the most developed areas of science with active research, there are many ways they can be improved. In particular, novel tools developed ba
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Ferdousi, Rahatara. "Digital Twin Disease Diagnosis Using Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42773.

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COVID-19 has led to a surge in the adoption of digital transformation in almost every sector. Digital health and well-being are no exception. For instance, now people get checkupsvia apps or websites instead of visiting a physician. The pandemic has pushed the health-care sector worldwide to advance the adoption of artificial intelligence (AI) capabilities.Considering the demand for AI in supporting the well-being of an individual, we presentthe real-life diagnosis as a digital twin(DT) diagnosis using machine learning. The MachineLearning (ML) technology enables DT to offer a prediction.
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Pérez, Benito Francisco Javier. "Healthcare data heterogeneity and its contribution to machine learning performance." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/154414.

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[EN] The data quality assessment has many dimensions, from those so obvious as the data completeness and consistency to other less evident such as the correctness or the ability to represent the target population. In general, it is possible to classify them as those produced by an external effect, and those that are inherent in the data itself. This work will be focused on those inherent to data, such as the temporal and the multisource variability applied to healthcare data repositories. Every process is usually improved over time, and that has a direct impact on the data distribution. Simila
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Hjalmarsson, Victoria. "Machine learning and Multi-criteria decision analysis in healthcare : A comparison of machine learning algorithms for medical diagnosis." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33940.

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Medical records consist of a lot of data. Nevertheless, in today’s digitized society it is difficult for humans to convert data into information and recognize hidden patterns. Effective decision support tools can assist medical staff to reveal important information hidden in the vast amount of data and support their medical decisions. The objective of this thesis is to compare five machine learning algorithms for clinical diagnosis. The selected machine learning algorithms are C4.5, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Naïve Bayes classifier. First, the mac
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Moniz, Francisco Fernandes Correia do Canto. "Healthcare provider effciency in workers' compensation : an approach with Machine Learning." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19400.

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Mestrado em Actuarial Science<br>O ramo de Acidentes de Trabalho é uma linha de negócio obrigatória e com bastante competitividade. Nos últimos anos, temos observado um crescimento na popularidade de "Data Science" e esta transformação passa também por atualizar os modelos e processos internos aplicados em seguros. Após um Acidente de Trabalho, é recomendado ao beneficiário um prestador clínico para acompanhar o seu tratamento. Usando várias variáveis sociais e patológicas modelamos custos médicos e de transportes, dependendo estes do prestador clínico principal do lesado. Esta metodologia
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Frandsen, Abraham Jacob. "Machine Learning for Disease Prediction." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/5975.

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Millions of people in the United States alone suffer from undiagnosed or late-diagnosed chronic diseases such as Chronic Kidney Disease and Type II Diabetes. Catching these diseases earlier facilitates preventive healthcare interventions, which in turn can lead to tremendous cost savings and improved health outcomes. We develop algorithms for predicting disease occurrence by drawing from ideas and techniques in the field of machine learning. We explore standard classification methods such as logistic regression and random forest, as well as more sophisticated sequence models, including recurre
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Pauphilet, Jean(Jean A. ). "Algorithmic advancements in discrete optimization : applications to machine learning and healthcare operations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127298.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 235-253).<br>In the next ten years, hospitals will operate like air-traffic control centers whose role is to coordinate care across multiple facilities. Consequently, the future of hospital operations will have three salient characteristics. First, data. The ability to process, analyze and exploit data effectively will become a vital skill for practitioners. Second, a holistic approac
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Maxhuni, Alban. "Managing the Scarcity of Monitoring Data through Machine Learning in Healthcare Domain." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/369267.

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In the field of Ubiquitous Computing, a significant problem of building accurate machine learning models is the effort and time consuming process to gather labeled data for the learning algorithm. Moreover, efficient data use demands are constantly growing. These demands for efficient data use are growing constantly. Researchers are therefore exploring the use of machine learning techniques to overcome the problem of data scarcity. In healthcare, classification tasks require a ground truth normally provided by an expert physician, ending up with a small set of labeled data with a larger set of
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Maxhuni, Alban. "Managing the Scarcity of Monitoring Data through Machine Learning in Healthcare Domain." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2079/1/PhD-Thesis.pdf.

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In the field of Ubiquitous Computing, a significant problem of building accurate machine learning models is the effort and time consuming process to gather labeled data for the learning algorithm. Moreover, efficient data use demands are constantly growing. These demands for efficient data use are growing constantly. Researchers are therefore exploring the use of machine learning techniques to overcome the problem of data scarcity. In healthcare, classification tasks require a ground truth normally provided by an expert physician, ending up with a small set of labeled data with a larger set of
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Books on the topic "Machine learning in healthcare"

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Dua, Sumeet, U. Rajendra Acharya, and Prerna Dua, eds. Machine Learning in Healthcare Informatics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40017-9.

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Chaurasia, Mousmi Ajay, Prasanalakshmi Balaji, and Alejandro C. Frery, eds. Smart Healthcare and Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3312-5.

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Chen, Hao, and Luyang Luo, eds. Trustworthy Machine Learning for Healthcare. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39539-0.

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Chandran, C. Karthik, M. Rajalakshmi, Sachi Nandan Mohanty, and Subrata Chowdhury. Machine Learning for Healthcare Systems. River Publishers, 2023. http://dx.doi.org/10.1201/9781003438816.

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Panesar, Arjun. Machine Learning and AI for Healthcare. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3799-1.

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Panesar, Arjun. Machine Learning and AI for Healthcare. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6537-6.

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Pranav, Prashant, Archana Patel, and Sarika Jain. Machine Learning in Healthcare and Security. CRC Press, 2023. http://dx.doi.org/10.1201/9781003388845.

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Maier, Andreas K., Julia A. Schnabel, Pallavi Tiwari, and Oliver Stegle, eds. Machine Learning for Multimodal Healthcare Data. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47679-2.

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Saxena, Ankur, and Shivani Chandra. Artificial Intelligence and Machine Learning in Healthcare. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0811-7.

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Gupta, Punit, Dinesh Kumar Saini, and Rohit Verma. Healthcare Solutions Using Machine Learning and Informatics. Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003322597.

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Book chapters on the topic "Machine learning in healthcare"

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Akshay, B. R., Sini Raj Pulari, T. S. Murugesh, and Shriram K. Vasudevan. "Smart healthcare." In Machine Learning. CRC Press, 2024. http://dx.doi.org/10.1201/9781032676685-19.

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Mathur, Puneet. "Monetizing Healthcare Machine Learning." In Machine Learning Applications Using Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3787-8_6.

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Udeechee and T. V. Vijay Kumar. "Machine Learning for Healthcare." In Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0. CRC Press, 2022. http://dx.doi.org/10.1201/9781003279044-9.

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Nandy, Aadrita, Jyoti Choudhary, Joanne Fredrick, T. S. Zacharia, Tom K. Joseph, and Veerpal Kaur. "Machine Learning for Healthcare." In Federated Deep Learning for Healthcare. CRC Press, 2024. http://dx.doi.org/10.1201/9781032694870-5.

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Ojha, Ananta Charan, and C. Vinitha. "Machine Learning in Healthcare." In Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics. CRC Press, 2022. http://dx.doi.org/10.1201/9780367548445-3.

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Dambhare, Shruti, and Sanjay Kumar. "Machine Learning in Healthcare." In Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9781003189053-1.

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Banerjee, Srijita, Adrish Bhattacharya, and Shampa Sen. "Healthcare IoT (H-IoT)." In Machine Learning and IoT. CRC Press, 2018. http://dx.doi.org/10.1201/9781351029940-15.

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Kumar Singh, Bikesh, and G. R. Sinha. "Introduction to Machine Learning." In Machine Learning in Healthcare. CRC Press, 2022. http://dx.doi.org/10.1201/9781003097808-7.

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Panesar, Arjun. "Machine Learning Algorithms." In Machine Learning and AI for Healthcare. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3799-1_4.

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Subramanian, Devika, and Trevor A. Cohen. "Machine Learning Systems." In Cognitive Informatics in Biomedicine and Healthcare. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09108-7_6.

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Conference papers on the topic "Machine learning in healthcare"

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Islam Prova, Nuzhat Noor. "Healthcare Fraud Detection Using Machine Learning." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696476.

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P, Kumar, and Yashini P. "Machine Learning-Based Healthcare Guidance System." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690330.

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G, Kothai, George princess T, Nandhagopal Subramani, Balakrishnan D, Ravi P, and S. Amutha. "Improving Healthcare with Machine Learning and Deep Learning." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763173.

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Gancheva, Veska. "Healthcare Data Analytics Based on Machine Learning." In 2024 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE, 2024. http://dx.doi.org/10.1109/ccci61916.2024.10736473.

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Saxena, Isha, Ajay Pratap, and Sourabh Kumar. "Transforming Oral Healthcare Using Machine Learning Technique." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932409.

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Chitteti, Chengamma, Mopuri Yamuna, Mattam Srinath, Chakali Govardhan, and Alavalapati Vignatha. "Healthcare Insurance Fraud Detection Using Machine Learning." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013497.

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M.A.Y, Peer Mohamed Appa, K. Sudha, E. Pooja, D. Lekha, M. Ezhilvendan, and Gayathri S. "Machine Learning in Healthcare: Opportunities and Challenges." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026363.

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Raushan, Rahul, Shiv Nath Chaudhri, Saurabh Suman, Dhiraj Kumar, Gulshan Kumar, and Abhishek Kumar. "Advancing Healthcare Through Internet of Things: A Comprehensive Review of Smart Healthcare Systems and Their Applications." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967632.

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Charan, Bakka, Dasu Jaswanth, E. Hemanth, and Mathireddy Sumanth Naidu. "Machine Learning and Deep Learning Approaches for Healthcare Predictive Analytics." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689833.

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M, Ramnath, Revathi B, Selva Birunda S, Usharani C, Ramana R, and Tyson Masillamani J. "Machine Learning in Healthcare: Prognosis Heart Disease Prediction." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10910928.

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Reports on the topic "Machine learning in healthcare"

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Lucas, Christine, Emily Hadley, Jason Nance, et al. Machine Learning for Medical Coding in Health Care Surveys. National Center for Health Statistics (U.S.), 2021. http://dx.doi.org/10.15620/cdc:109828.

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Sneha, Suruchi. Healthcare Provider Fraud Detection Analysis by applying supervised machine learning models. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-815.

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Balla, Yashaswini, Santosh Tirunagari, and David Windridge. A protocol for conducting review on the Challenges, Opportunities of Machine Learning in Pediatric Healthcare. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.5.0045.

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013015.

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This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.
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Sharma, Bhavna, Bryan Swanton, Joseph Kuo, et al. Use of Life Cycle Assessment in the Healthcare Industry: Environmental Impacts and Emissions Associated With Products, Processes, and Waste. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepctb48.

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Objectives. The objective of this Technical Brief is to assess the current use of life cycle assessment (LCA) frameworks in healthcare research and practice, understand the components of those frameworks, review LCA studies that have been conducted, and assess gaps in research and practice to guide future directions. Review methods. A scoping review combined with Key Informant interviews provided the input for the report. We searched a combination of biomedical (PubMed®); environmental (Agricultural &amp; Environmental Science Collection, Environmental Science Database, Environment Index); and
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental
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