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Relevant bibliographies by topics / Personalized Treatment Optimization

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Academic literature on the topic 'Personalized Treatment Optimization'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Personalized Treatment Optimization"

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Vazquez, Alexei. "Optimization of personalized therapies for anticancer treatment." BMC Systems Biology 7, no. 1 (2013): 31. http://dx.doi.org/10.1186/1752-0509-7-31.

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Deepak, Kumar, Pramod Pawar Priyanka, Gonaygunta Hari, Sandeep Nadella Geeta, Meduri Karthik, and Singh Shoumya. "Machine learning's role in personalized medicine & treatment optimization." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1675–86. https://doi.org/10.5281/zenodo.14038678.

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The advent of machine learning in personalized medicine has revolutionized the healthcare industry by providing an enhanced diagnosis and treatment regimen to patients based on their unique characteristics such as genetic predispositions, lifestyle variables, and medical history. Machine learning algorithms can analyze vast amounts of patient data to generate accurate diagnoses, establish tailored treatment plans, and improve patient outcomes. By combining multiple data sources, machine learning algorithms can identify patterns, predict the likelihood of specific illnesses, and recommend perso

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Azizi, Tahmineh. "Mathematical Modelling of Cancer Treatments, Resistance, Optimization." AppliedMath 5, no. 2 (2025): 40. https://doi.org/10.3390/appliedmath5020040.

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Mathematical modeling plays a crucial role in the advancement of cancer treatments, offering a sophisticated framework for analyzing and optimizing therapeutic strategies. This approach employs mathematical and computational techniques to simulate diverse aspects of cancer therapy, including the effectiveness of various treatment modalities such as chemotherapy, radiation therapy, targeted therapy, and immunotherapy. By incorporating factors such as drug pharmacokinetics, tumor biology, and patient-specific characteristics, these models facilitate predictions of treatment responses and outcome

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Deepak Kumar, Priyanka Pramod Pawar, Hari Gonaygunta, Geeta Sandeep Nadella, Karthik Meduri, and Shoumya Singh. "Machine learning's role in personalized medicine & treatment optimization." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1675–86. http://dx.doi.org/10.30574/wjarr.2024.21.2.0641.

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The advent of machine learning in personalized medicine has revolutionized the healthcare industry by providing an enhanced diagnosis and treatment regimen to patients based on their unique characteristics such as genetic predispositions, lifestyle variables, and medical history. Machine learning algorithms can analyze vast amounts of patient data to generate accurate diagnoses, establish tailored treatment plans, and improve patient outcomes. By combining multiple data sources, machine learning algorithms can identify patterns, predict the likelihood of specific illnesses, and recommend perso

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Pasupuleti, Murali Krishna. "Adaptive Multi-Scale Optimization Frameworks for Real-Time Personalized Treatment Planning in Precision Medicine." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 315–24. https://doi.org/10.62311/nesx/rp2625.

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Abstract: Personalized medicine demands tailored treatment plans accounting for patient-specific variability and dynamic biological changes. Recent advancements in mathematical optimization, artificial intelligence (AI), and cyber-medical systems have significantly enhanced the feasibility of real-time adaptive treatment planning. This research critically interprets existing literature to propose an integrated multi-scale optimization framework combining reinforcement learning, mathematical programming, and evolutionary algorithms. Data analysis, statistical interpretation, and graphical insig

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R, Aarthi, and Helenprabha K. "ANALYSIS OF BRAIN SUB REGIONS USING OPTIMIZATION TECHNIQUES IN ALZHEIMERS DISEASE." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3175–80. http://dx.doi.org/10.21917/ijivp.2024.0452.

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AD is a progressive neurodegenerative disorder impacting specific brain sub-regions. Accurate identification and analysis of these regions are crucial for early diagnosis and effective intervention. This study employs optimization techniques to enhance the understanding of AD-related alterations in brain sub-regions. Utilizing medical imaging data, a multi-step approach is implemented. Image segmentation algorithms optimize brain sub-region delineation, while feature selection techniques enhance discriminative information extraction. Machine learning models, fine-tuned through optimization, cl

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Lei, Weijun, Chander Sadasivan, Shikui Chen, and Xianfeng David Gu. "536 Topology optimization for personalized intracranial aneurysm implant design." Journal of Clinical and Translational Science 9, s1 (2025): 157. https://doi.org/10.1017/cts.2024.1112.

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Objectives/Goals: To develop a personalized computational framework integrating computational fluid dynamics (CFD) and topology optimization for designing intracranial aneurysm implants. The primary objective is to reduce intra-aneurysmal blood flow velocity and enhance thrombus formation for improved treatment outcomes. Methods/Study Population: Patient-specific aneurysm geometries were extracted from pre-treatment rotational angiograms. A CFD-driven topology optimization framework was employed to design implants that reduce intra-aneurysmal flow velocity. The fluid dynamics were modeled usin

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Researcher. "INTEGRATING AI SYSTEMS IN PERSONALIZED HEALTHCARE: FROM DATA ANALYTICS TO TREATMENT OPTIMIZATION." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1663–74. https://doi.org/10.5281/zenodo.14244182.

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Including artificial intelligence (AI) into customized medicine marks a paradigm change in healthcare delivery and gives hitherto unheard-of chances to customize medical treatments depending on particular patient traits. To maximize therapy options, this thorough paper investigates the transforming possibilities of artificial intelligence technologies in assessing complicated patient data comprising genomic profiles, electronic health records, and clinical biomarkers. Particularly in oncology and chronic disease management, this paper highlights how remarkably capable machine learning algorith

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Khan, Sajjad Ahmed, Sadab Khan, Huma Kausar, et al. "Pharmacogenomics and risk stratification in cardiovascular care: Insights from randomized controlled trials." Medicine 104, no. 24 (2025): e42868. https://doi.org/10.1097/md.0000000000042868.

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Genomics and personalized medicine are transforming cardiology by improving the assessment, diagnosis, and treatment of cardiovascular diseases (CVDs). By understanding the genetic factors underlying CVDs, personalized treatment strategies can be developed to enhance patient outcomes and reduce the burden of cardiovascular conditions. The integration of genomics into clinical practice enables more precise risk assessments, early detection, and targeted interventions. This narrative review examines randomized clinical trials (RCTs) on genotype-guided therapies, pharmacogenomics, and personalize

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Konigorski, Stefan, Sarah Wernicke, Tamara Slosarek, et al. "StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials." Journal of Medical Internet Research 24, no. 7 (2022): e35884. http://dx.doi.org/10.2196/35884.

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N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric element

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Book chapters on the topic "Personalized Treatment Optimization"

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Sugie, Tomoharu, and Takashi Inamoto. "Lymphatic Mapping and Optimization of Sentinel Lymph Node Dissection." In Personalized Treatment of Breast Cancer. Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55552-0_9.

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Korunovic, Nikola, and Jovan Arandjelovic. "Structural Analysis and Optimization of Fixation Devices Used in Treatment of Proximal Femoral Fractures." In Personalized Orthopedics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98279-9_17.

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Lee, Eva K., Xin Wei, Michael D. Wright, Francine Baker-Witt, and Alexander Quarshie. "Drug-Dose-Drug-Effect Predictive Treatment Optimization for Personalized Diabetes Management." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81010-7_1.

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Shamanth Showri, N. R., Sathvik V. Koushik, P. Urjitha, C. R. Shreya, S. Samarth, and C. D. Divya. "Intelligence swarm-based personalized cancer treatment optimization using health big data analytics." In Data Science & Exploration in Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003589273-15.

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Swetha, M., R. Shireesh Kiran, D. Satya Sireesha, and Geetha Karra. "Biometric Sensors in Personalized Drug Therapy: A Novel Approach to Treatment Optimization." In Advances in Sports Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003616283-43.

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Moldovan, Flaviu, Adrian Gligor, and Tiberiu Bataga. "Dimensional Optimization in Screw Fixation for Personalized Treatment of the Tibial Plateau Fracture." In The 15th International Conference Interdisciplinarity in Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93817-8_69.

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Rahmaty, Maryam. "Personalized Medicine and Treatment Optimization with Artificial Intelligence of Every Medical Thing (AIoEMT)." In Artificial Intelligence of Everything and Sustainable Development. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7202-8_5.

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Senyukova, Olga, Valeriy Gavrishchaka, Maria Sasonko, Yuri Gurfinkel, Svetlana Gorokhova, and Nikolay Antsygin. "Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization." In Computational Collective Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45243-2_18.

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Zhang, Ziqi, Hongfa Ding, Shuochun Yu, and Zhou He. "An Intelligent Optimization Method for Transcranial Magnetic Stimulation Waveforms to Improve Stimulation Selectivity." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_4.

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Abstract Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique that stimulates the brain using an induced electric field generated by a stimulation coil and pulsed current. The precision of this stimulation largely determines its effectiveness. This paper proposes an intelligent waveform optimization method that utilizes intelligent algorithms combined with real brain structures to optimize stimulation waveforms, innovatively improving stimulation accuracy from the temporal scale while also guiding the construction of TMS circuits. First, a selectivity index is es

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Italia, Matteo, and Fabio Dercole. "In Silico Modelling, Analysis, and Control of Complex Diseases: Addressing Clinical Questions, Personalized Treatments, and Healthcare Management." In SpringerBriefs in Applied Sciences and Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80268-3_8.

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Abstract Human diseases are complex and dynamic. Understanding and controlling diseases require interdisciplinary approaches, aided by advances in digital technology, data analysis, and computational power. Specifically, in his Ph.D. Thesis, Matteo Italia has developed in silico models to study cancers, Restless Legs Syndrome (RLS), and Covid-19. The goals are to answer clinical questions, optimize treatments, and manage healthcare. For cancers, the developed models suggest that dynamic and personalized protocols can overcome drug resistance more effectively than static protocols. For neurobla

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Conference papers on the topic "Personalized Treatment Optimization"

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Li, Lei, Hongfei Di, Zan Zhang, and Zhenchao Tao. "Chemotherapy Optimization for Personalized Adaptive Cancer Treatment." In 2025 10th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2025. https://doi.org/10.1109/icccbda64898.2025.11030543.

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Indhumathi, V., Veeramalai Sankaradass, and V. K. Manindra Manish. "Integrating Machine Learning and Big Data Analytics for Personalized Medicine and Treatment Optimization." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011716.

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Gu, James, and Jake Y. Chen. "MLPA: A Multi-scale Digital Twin Framework for Personalized Cancer Simulation and Treatment Optimization." In BCB '24: 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2024. https://doi.org/10.1145/3698587.3701425.

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Goubergrits, L., R. Mevert, P. Yevtushenko, et al. "Treatment of the Aortic Coarctation: Prediction of the Hemodynamic Impact." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14397.

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Aortic coarctation (CoA) accounts for approximately 10% of congenital heart diseases 1. CoA causing high pressure gradient can be successfully treated surgical or catheter-based. Long-term results, however, revealed decreased life expectancy associated with abnormal hemodynamics 1. To develop a next-generation personalized diagnostic-prognostic tools allowing treatment optimization and thus to improve life expectance, the innovative combination of imaging science, biofluid mechanics, and computer modeling is necessary. Patient-specific computational fluid dynamics (CFD) models of the CoA based

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Sala, F. "Preliminary evaluation of an additive manufacturing procedure for producing patient-specific upper-limb orthotic devices." In Italian Manufacturing Association Conference. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902714-13.

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Abstract. In the orthopedic field, the need for patient-specific devices is crucial to ensure a rapid and successful care treatment. The traditional techniques for manufacturing customized orthopedic systems, specifically orthoses, are laborious and present multiple and time-consuming steps. The present research analyzed the possibility of optimizing the conventional process for manufacturing personalized orthoses by leveraging the principles of Reverse Engineering (RE) and Additive Manufacturing (AM). Digital orthotic models of different anatomical regions were obtained using 3D laser scannin

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Ghosh, Susobhan, Yongyi Guo, Pei-Yao Hung, et al. "ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/805.

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The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs. re

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Chan, Kimberly J., Joel A. Paulson, and Ali Mesbah. "Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine." In 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023. http://dx.doi.org/10.1109/cdc49753.2023.10384190.

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Wickramasinghe, Nilmini, and Nalika Ulapane. "Explainable Digital Twins of Patients: Towards Precision and Personalisation through Cohort Matching." In 38th Bled eConference. University of Maribor Press, 2025. https://doi.org/10.18690/um.fov.4.2025.10.

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Modern healthcare services have advanced greatly due to rapid improvements in technology. The next generation of advancements requires precise and personalised treatments, especially for chronic diseases. Computational means are an effective way to achieve this through intelligent decision support assisted by superior data collection and analytics. An emerging concept to facilitate this is digital twins (DTs)—digital replicas of physical entities. DTs have evolved over the years across various industries including aerospace, control engineering, manufacturing, design optimization, and more. DT

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Valentin, Ana Beatriz Miranda, Glaucia Maria Bressan, Leonardo Canuto Junior, and Elisângela Ap da Silva Lizzi. "Optimized Neural Networks for Breast Cancer Classification Using Gene Expression Data." In Simpósio Brasileiro de Bioinformática. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/bsb.2024.245194.

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This study aims to develop and evaluate optimized neural networks, including Multilayer Perceptrons (MLP) and Convolutional Neural Networks (CNN), by employing deep learning techniques to classify breast cancer subtypes, based on gene expression data. By implementing different neural network architectures and optimization strategies, this research seeks to determine the accuracy and efficiency of these classification methods. Data is sourced from The Cancer Genome Atlas (TCGA) repository and undergoes preprocessing, including dimensionality reduction, to prepare it for analysis. The contributi

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Reports on the topic "Personalized Treatment Optimization"

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Latorre, Lucía, Eduardo Rego, Lorenzo De Leo, and Mariana Gutierrez. Tech Report: Digital Twins. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013166.

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Digital twins are finding innovative applications in a wide range of industries. In manufacturing, they enable product design optimization, predictive machinery maintenance, and customized production. Healthcare will benefit from precise diagnostics, personalized treatments, and advanced surgical planning. In city planning, they support efficient urban design and complex situation management. Regarding energy, they promote the efficiency and sustainability of systems and infrastructure. Finally, in the agricultural sector, they improve crop management, resource use and animal welfare.

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