Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management DOI Creative Commons
Maryam Bagheri, Mohsen Bagheritabar,

Sohila Alizadeh

и другие.

Applied Sciences, Год журнала: 2024, Номер 15(1), С. 296 - 296

Опубликована: Дек. 31, 2024

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding how these models are divided analyzed, leaving gaps in normalization benchmarking. present research usually overlooks holistic for comparing ML-enabled ISs, significantly considering pivotal function criteria like precision, sensitivity, specificity. To address gaps, we conducted broad exploration 306 state-of-the-art papers to novel taxonomy IS management. We categorized studies six key areas, namely systems, treatment-planning monitoring resource allocation preventive hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is most effective parameter throughout all models. In addition, majority were published 2022 2023, with MDPI as leading publisher Python prevalent programming language. This extensive synthesis not only bridges but also proposes actionable insights ML-powered

Язык: Английский

Estimating Axial Bearing Capacity of Driven Piles Using Tuned Random Forest Frameworks DOI

Belal Mohammadi Yaychi,

Mahzad Esmaeili‐Falak

Geotechnical and Geological Engineering, Год журнала: 2024, Номер unknown

Опубликована: Сен. 28, 2024

Язык: Английский

Процитировано

18

Synergistic Effects of Platinum-Based Drugs and Curcumin on Liposomal Delivery in HSC-3 Oral Cancer Cells DOI

Faezeh Amiri,

Parizad Ghanbarikondori, Hora Amoozegar

и другие.

Indian Journal of Clinical Biochemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 6, 2025

Язык: Английский

Процитировано

1

AI augmented edge and fog computing for Internet of Health Things (IoHT) DOI Creative Commons
D. Rajagopal,

P. Subramanian

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2431 - e2431

Опубликована: Янв. 30, 2025

Patients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over Internet enables deployment an infinite number applications to provide services many sectors. The primary limitation these cloud frameworks right now is their limited scalability, which results in inability meet needs. An edge/fog environment, paired current techniques, answer fulfill energy efficiency latency requirements for real-time collection analysis health data. Additionally, Things (IoT) revolution has been essential changing contemporary healthcare systems by integrating social, economic, technological perspectives. This requires transitioning from unadventurous adapted allow patients be identified, managed, evaluated easily. These techniques data sources integrated effectively assess patient status predict potential preventive actions. A subset Things, Health (IoHT) remote exchange physical processes like monitoring, treatment progress, observation, consultation. Previous surveys related mainly focused on architecture networking, left untouched important aspects smart optimal such as artificial intelligence, deep learning, technologies, includes 5G unified communication service (UCaaS). study aims examine future existing fog edge architectures methods have augmented intelligence (AI) use applications, well defining demands challenges incorporating technology IoHT, thereby helping professionals technicians identify relevant technologies required based need developing IoHT healthcare. Among crucial elements take into account framework are efficient resource management, low latency, strong security. review addresses several machine learning management IoT, where (ML) AI crucial. It noted how narrow band-IoT (NB-IoT) wider coverage Blockchain security, transforming IoHT. last part focuses posed services. provides prospective research suggestions enhancing order give improved quality life.

Язык: Английский

Процитировано

0

The Effect of Rabdosia rubescens on Radiotherapy-Induced Oral Mucositis in Nasopharyngeal Carcinoma Patients: A Phase II Clinical Study DOI Creative Commons
Lu Li, Yecai Huang, Jun Yin

и другие.

Integrative Cancer Therapies, Год журнала: 2025, Номер 24

Опубликована: Янв. 1, 2025

Purpose: Radiotherapy-induced oral mucositis is the most common side effect in nasopharyngeal carcinoma (NPC) patients. We aimed to evaluate efficacy and safety of Rabdosia rubescens drop pills NPC patients with radiation-induced (RTOM). Methods: The study involved 40 who were given thrice daily from start radiation therapy. monitored incidence severity pain. main outcomes measured occurrence rate mucositis, grade 3 pain assessment, changes immunological function, body weight, BMI, NRS2002, albumin levels. Results: In study, 38 completed treatment. rates Grade 0 5.26%, 21.05%, 47.37%, 26.32% respectively. Pain levels mild (42.11%), moderate (13.16%), severe (13.16%). onset 1, 2, occurred at 18, 24, 30 days was associated NRS2002 score, Post-treatment, there a decrease CD4 + /CD8 , CD3 immune cells, but an increase CD8 cells. Mild gastrointestinal adverse events observed 13.2% Conclusion: administration can reduce radiotherapy induced mucositis. Our finding suggested positive impact drops upon

Язык: Английский

Процитировано

0

Optimized decision tree algorithms to estimate ultimate strain of concrete wrapped by aramid fiber-reinforced polymer DOI
Yangyang Guo

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(4)

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Application value of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in predicting stress ulcer after acute cerebral hemorrhage surgery DOI
Tingting Wang, Yanfei Chen, Zenghui Liu

и другие.

Clinical Neurology and Neurosurgery, Год журнала: 2024, Номер 246, С. 108557 - 108557

Опубликована: Сен. 16, 2024

Язык: Английский

Процитировано

0

Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management DOI Creative Commons
Maryam Bagheri, Mohsen Bagheritabar,

Sohila Alizadeh

и другие.

Applied Sciences, Год журнала: 2024, Номер 15(1), С. 296 - 296

Опубликована: Дек. 31, 2024

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding how these models are divided analyzed, leaving gaps in normalization benchmarking. present research usually overlooks holistic for comparing ML-enabled ISs, significantly considering pivotal function criteria like precision, sensitivity, specificity. To address gaps, we conducted broad exploration 306 state-of-the-art papers to novel taxonomy IS management. We categorized studies six key areas, namely systems, treatment-planning monitoring resource allocation preventive hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is most effective parameter throughout all models. In addition, majority were published 2022 2023, with MDPI as leading publisher Python prevalent programming language. This extensive synthesis not only bridges but also proposes actionable insights ML-powered

Язык: Английский

Процитировано

0