Understanding mental health in breast cancer from screening to Survivorship: an integrative phasic Model and tool DOI
Justine Fortin, Émilie Rudd, Claudia Trudel‐Fitzgerald

et al.

Psychology Health & Medicine, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: Nov. 23, 2024

Integrative models of mental illness and health in psycho-oncology are aimed at all types cancer, although the patients' experiences issues may vary. This review summarizes different theories pertaining to breast cancer experience proposes an integrative phasic model applicable trajectory. Five databases were searched for studies related models. The PRISMA checklist form was used extract essential information from included studies. Eleven on found. based these illustrates that is conceptualized as a trajectory with seven landmark '

Language: Английский

Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension DOI

Xiao-Ming Yu,

Wenxiang Qin,

Xiao Lin

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107408 - 107408

Published: Aug. 29, 2023

Language: Английский

Citations

47
Towards integration of artificial intelligence into medical devices as a real-time recommender system for personalised healthcare: State-of-the-art and future prospects DOI Creative Commons
Talha Iqbal, Mehedi Masud, Bilal Amin

et al.

Health Sciences Review, Journal Year: 2024, Volume and Issue: 10, P. 100150 - 100150

Published: Jan. 25, 2024

In the era of big data, artificial intelligence (AI) algorithms have potential to revolutionize healthcare by improving patient outcomes and reducing costs. AI frequently been used in health care for predictive modelling, image analysis drug discovery. Moreover, as a recommender system, these shown promising impacts on personalized provision. A system learns behaviour user predicts their current preferences (recommends) based previous preferences. Implementing improves this prediction accuracy solves cold start data sparsity problems. However, most methods are tested simulated setting which cannot recapitulate influencing factors real world. This review article systematically reviews prevailing methodologies systems discusses specifically field healthcare. It also provides discussion around cutting-edge academic practical contributions present literature, identifies performance evaluation matrices, challenges implementation acceptance AI-based clinicians. The findings direct researchers professionals comprehend currently developed future medical devices integrated with real-time

Language: Английский

Citations

7
Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review DOI Creative Commons
Nahid Zeinali, Nayung Youn, Alaa Albashayreh

et al.

JMIR Cancer, Journal Year: 2024, Volume and Issue: 10, P. e52322 - e52322

Published: Jan. 19, 2024

People with cancer frequently experience severe and distressing symptoms associated its treatments. Predicting in patients continues to be a significant challenge for both clinicians researchers. The rapid evolution of machine learning (ML) highlights the need current systematic review improve symptom prediction.

Language: Английский

Citations

5
Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models DOI
Han Yan, Weibin Chen, Ali Asghar Heidari

et al.

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(2), P. 1022 - 1054

Published: Feb. 27, 2024

Language: Английский

Citations

5
Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection DOI
Jun Li, Hao Ren, Huiling Chen

et al.

Soft Computing, Journal Year: 2023, Volume and Issue: 27(23), P. 17887 - 17908

Published: Aug. 18, 2023

Language: Английский

Citations

11
Applying analytics to sociodemographic disparities in mental health DOI
Aaron Baird, Yusen Xia

Nature Mental Health, Journal Year: 2025, Volume and Issue: 3(1), P. 124 - 138

Published: Jan. 8, 2025

Language: Английский

Citations

0
Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design DOI Creative Commons

Xinru Li,

Zihan Lin,

Haoxuan Lv

et al.

iScience, Journal Year: 2023, Volume and Issue: 26(10), P. 107736 - 107736

Published: Aug. 28, 2023

Highlights•A new SMA-based method integrating DE and Powell mechanisms, named PSMADE, is proposed•PSMADE effectively improves SMA performance on unimodal multimodal functions•PSMADE outperforms other high-performance optimizers the CEC 2014 benchmark•PSMADE successfully solves four real-world engineering problemsSummaryThe slime mould algorithm (SMA) a population-based swarm intelligence optimization that simulates oscillatory foraging behavior of moulds. To overcome its drawbacks slow convergence speed premature convergence, this paper proposes an improved which integrates differential evolution (DE) mechanism. PSMADE utilizes crossover mutation operations to enhance individual diversity improve global search capability. Additionally, it incorporates mechanism with taboo table strengthen local facilitate toward better solutions. The evaluated by comparing 14 metaheuristic algorithms (MA) 15 MAs benchmarks, as well solving constrained problems. Experimental results demonstrate compensates for limitations exhibits outstanding in various complex problems, showing potential effective problem-solving tool.Graphical abstract

Language: Английский

Citations

7
Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross‐Sectional Study DOI Creative Commons
Li Shu, Jing Shi, Chunyu Shao

et al.

Depression and Anxiety, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Depression and anxiety are highly prevalent among patients with breast cancer. We tested the capacity of personal resources (psychological resilience, social support, process recovery) for predicting depression, anxiety, comorbid depression (CDA) such using machine learning (ML). conducted a cross-sectional survey in Liaoning Province, China, including questions about demographics, COVID-19's impact, (707 valid responses). In training set, we used Lasso logistic regression to establish resource models. Subsequently, six ML methods tenfold cross-validation strategy models combining resources, COVID-19 impacts. Findings indicate that total, 21.9%, 35.1%, 14.7% participants showed CDA, respectively. Loneliness, vitality, mental health, bodily pain, self-control predicted CDA. Furthermore, general health physical function anxiety. Demographic were far less predictive than (0.505-0.629 vs. 0.826-0.869). Among combined models, support vector model achieved best prediction (AUC: 0.832-0.873), which was slightly better Personal features can help predict CDA Accordingly, interventions should target loneliness, self-control.

Language: Английский

Citations

2
Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort DOI Creative Commons
Dirk H. M. Pelt, Philippe C. Habets, Christiaan H. Vinkers

et al.

Nature Mental Health, Journal Year: 2024, Volume and Issue: 2(10), P. 1217 - 1230

Published: Aug. 14, 2024

Effective personalized well-being interventions require the ability to predict who will thrive or not, and understanding of underlying mechanisms. Here, using longitudinal data a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim build machine learning prediction models for adult from exposome genome, identify most predictive factors (

Language: Английский

Citations

2
Sine cosine algorithm with communication and quality enhancement: Performance design for engineering problems DOI Creative Commons
Helong Yu,

Zisong Zhao,

Jing Zhou

et al.

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(4), P. 1868 - 1891

Published: July 4, 2023

Abstract In recent years, the sine cosine algorithm (SCA) has become one of popular swarm intelligence algorithms due to its simple and convenient structure. However, standard SCA tends fall into local optimum when solving complex multimodal tasks, leading unsatisfactory results. Therefore, this study presents with communication quality enhancement, called CCEQSCA. The proposed includes two enhancement strategies: collaboration strategy (CC) (EQ). algorithm, CC strengthens connection populations by guiding search agents closer range optimal solutions. EQ improves candidate solutions enhance exploitation algorithm. Furthermore, can explore potential in other scopes, thus strengthening ability prevent trapping optimum. To verify capability CCEQSCA, 30 functions from IEEE CEC2017 are analyzed. is compared 5 advanced original 10 variants. outcomes indicate that it dominant over comparison global optimization tasks. work paper also utilized tackle three typical engineering design problems excellent capabilities. It been experimentally demonstrated CCEQSCA works as an effective tool real issues constraints space.

Language: Английский

Citations

6