{"title":"Enhancing heart disease prediction with stacked ensemble and MCDM-based ranking: an optimized RST-ML approach.","authors":"T Ashika, G Hannah Grace","doi":"10.3389/fdgth.2025.1609308","DOIUrl":"10.3389/fdgth.2025.1609308","url":null,"abstract":"<p><strong>Introduction: </strong>Cardiovascular disease (CVD) is a leading global cause of death, necessitating the development of accurate diagnostic models. This study presents an Optimized Rough Set Theory-Machine Learning (RST-ML) framework that integrates Multi-Criteria Decision-Making (MCDM) for effective heart disease (HD) prediction. By utilizing RST for feature selection, the framework minimizes dimensionality while retaining essential information.</p><p><strong>Methods: </strong>The framework employs RST to select relevant features, followed by the integration of nine ML classifiers into five stacked ensemble models through correlation analysis to enhance predictive accuracy and reduce overfitting. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranks the models, with weights assigned using the Mean Rank Error Correction (MEREC) method. Hyperparameter tuning for the top model, Stack-4, was conducted using GridSearchCV, identifying XGBoost (XG) as the most effective classifier. To assess scalability and generalization, the framework was evaluated using additional datasets, including chronic kidney disease (CKD), obesity levels, and breast cancer. Explainable AI (XAI) techniques were also applied to clarify feature importance and decision-making processes.</p><p><strong>Results: </strong>Stack-4 emerged as the highest-performing model, with XGBoost achieving the best predictive accuracy. The application of XAI techniques provided insights into the model's decision-making, highlighting key features influencing predictions.</p><p><strong>Discussion: </strong>The findings demonstrate the effectiveness of the RST-ML framework in improving HD prediction accuracy. The successful application to diverse datasets indicates strong scalability and generalization potential, making the framework a robust and scalable solution for timely diagnosis across various health conditions.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1609308"},"PeriodicalIF":3.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12222165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Beyond the interface: benchmarking pediatric mobile health applications for monitoring child growth using the Mobile App Rating Scale.","authors":"Anggi Septia Irawan, Arie Dwi Alristina, Rizky Dzariyani Laili, Nuke Amalia, Arief Purnama Muharram, Adriana Viola Miranda, Bence Döbrössy, Edmond Girasek","doi":"10.3389/fdgth.2025.1621293","DOIUrl":"10.3389/fdgth.2025.1621293","url":null,"abstract":"<p><strong>Introduction: </strong>As mHealth applications become increasingly adopted in Indonesia, it is crucial to assess their quality and usability for parents and healthcare professionals.</p><p><strong>Aim: </strong>This study evaluated the quality of pediatric-related mobile health (mHealth) applications available in Indonesia, focusing on their ability to support child growth monitoring and provide educational resources for parents and caregivers.</p><p><strong>Methodology: </strong>This is a cross-sectional study. From December 1, 2024, and January 31, 2025 we conducted systematic search for pediatric mHealth applications in Indonesian Google Play Store and Apple App Store using predetermined keywords. Inclusion criteria required the applications to be available in Bahasa Indonesia, focus on child health, and include growth tracking or stunting prevention features. We excluded applications that were not functioning during the testing period. Quality assessment was conducted by five healthcare professionals using the Mobile App Rating Scale (MARS). MARS assessed applications from multiple domains, including engagement, functionality, aesthetics, and information quality. Inter-rater reliability was ensured using the Intraclass Correlation Coefficient (ICC). The results were analyzed using descriptive statistics, Pearson's correlation, and T-tests. A <i>p</i>-value of <0.05 is considered to be statistically significant.</p><p><strong>Findings: </strong>Nine applications were included in this study. Seven of the applications (77.78%) focused on tracking child growth and development and providing educational content. Less than half of the apps had built-in community features that enabled social support (<i>n</i> = 4, 44.44%) and features for feedback mechanisms & personalized guidance (<i>n</i> = 3, 33.33%) respectively. The majority were developed by commercial companies (<i>n</i> = 7, 77.78%). Quality assessment found significant variability across the apps, with high functionality and aesthetics scores but more variability in the domains of app engagement, quality of information, and subjective quality or perceived value.</p><p><strong>Conclusion: </strong>This research underscored the need for the development of higher-quality, evidence-based mHealth apps for pediatric care in Indonesia, particularly in improving user engagement, feedback mechanisms and accessibility.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1621293"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research trends in the application of artificial intelligence in nursing of chronic disease: a bibliometric and network visualization study.","authors":"Chao Du, Jing Zhou, Yuexin Yu","doi":"10.3389/fdgth.2025.1608266","DOIUrl":"10.3389/fdgth.2025.1608266","url":null,"abstract":"<p><strong>Purpose: </strong>The incidence of chronic diseases is increasing annually and exhibits a trend of multimorbidity, posing significant challenges to global healthcare and nursing. The rapid rise of artificial intelligence has provided broad application prospects in the field of chronic disease care. However, with the increasing number of related studies, there is a lack of systematic review and prediction of future trends in this area. Bibliometric methods provide possibility for addressing this gap. This study aimed to investigate the current status, hot topics, and future prospects of artificial intelligence in the field of chronic disease care.</p><p><strong>Methods: </strong>Literature related to artificial intelligence and chronic disease care was retrieved from the Web of Science Core Collection database, published between 2001 and 31 December 2023. Bibliometric analysis and visualization was conducted using CiteSpace 5.7.R5 and VOSviewer 1.6.19 to analyze countries/regions, institutions, journals, references, and keywords.</p><p><strong>Results: </strong>A total of 2438 articles were retrieved, indicating an explosive growth in publications over the past five years. The United States emerged as the earliest adopter of research in this domain (since 2002) and contributed the most publications (490 articles), with IEEE ACCESS being the most cited journal. Hot application areas of artificial intelligence in chronic disease care included \"diabetic retinopathy\", \"heart disease prediction\", \"breast cancer\", and \"skin cancer\". Major research methodologies encompassed \"machine learning\", \"deep learning\", \"neural network\", and \"text mining\". Potential future research hotspots include \"internet of medical things\".</p><p><strong>Conclusion: </strong>This study unveils the current status and development trends of artificial intelligence in chronic disease care, offering novel insights for future artificial intelligence application research.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1608266"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bridging clinic to home: domestic devices in dermatological diagnostics and treatments.","authors":"Diala Haykal, Frederic Flament","doi":"10.3389/fdgth.2025.1595484","DOIUrl":"10.3389/fdgth.2025.1595484","url":null,"abstract":"<p><p>The integration of diagnostic and therapeutic tools into home-used devices has significantly transformed dermatology, making advanced skincare technologies more accessible to the public. Home-based diagnostic devices empower individuals to monitor, assess, and track skin conditions in real time, promoting earlier interventions and personalized skincare. Therapeutic devices, on the other hand, enable users to actively treat cosmetic and dermatological concerns, offering greater autonomy in managing skin health outside the clinical setting. These technologies, often inspired by clinical-grade equipment, promise enhanced patient engagement but also raise critical questions regarding safety, efficacy, and regulatory oversight. Importantly, the regulatory status of these devices, particularly for diagnostic tools, varies significantly across regions, affecting standards for quality, permitted energy outputs, and intended uses. This commentary separately explores the opportunities and challenges posed by home-used diagnostic and therapeutic devices, evaluates their roles in cosmetic dermatology, and highlights key insights from the literature to contextualize their growing influence on personalized skincare.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1595484"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Generative AI in healthcare: challenges to patient agency and ethical implications.","authors":"Scott A Holmes, Vanda Faria, Eric A Moulton","doi":"10.3389/fdgth.2025.1524553","DOIUrl":"10.3389/fdgth.2025.1524553","url":null,"abstract":"<p><p>Clinical research is no longer a monopolistic environment wherein patients and participants are the sole voice of information. The introduction and acceleration of AI-based methods in healthcare is creating a complex environment where human-derived data is no longer the sole mechanism through which researchers and clinicians explore and test their hypotheses. The concept of self-agency is intimately tied into this, as generative data does not encompass the same person-lived experiences as human-derived data. The lack of accountability and transparency in recognizing data sources supporting medical and research decisions has the potential to immediately and negatively impact patient care. This commentary considers how self-agency is being confronted by the introduction and proliferation of generative AI, and discusses future directions to improve, rather than undermine AI-fueled healthcare progress.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1524553"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martha Neary, Emily Fulton, Victoria Rogers, Julia Wilson, Zoe Griffiths, Ram Chuttani, Paul M Sacher
{"title":"Think FAST: a novel framework to evaluate fidelity, accuracy, safety, and tone in conversational AI health coach dialogues.","authors":"Martha Neary, Emily Fulton, Victoria Rogers, Julia Wilson, Zoe Griffiths, Ram Chuttani, Paul M Sacher","doi":"10.3389/fdgth.2025.1460236","DOIUrl":"10.3389/fdgth.2025.1460236","url":null,"abstract":"<p><p>Developments in Machine Learning based Conversational and Generative Artificial Intelligence (GenAI) have created opportunities for sophisticated Conversational Agents to augment elements of healthcare. While not a replacement for professional care, AI offers opportunities for scalability, cost effectiveness, and automation of many aspects of patient care. However, to realize these opportunities and deliver AI-enabled support safely, interactions between patients and AI must be continuously monitored and evaluated against an agreed upon set of performance criteria. This paper presents one such set of criteria which was developed to evaluate interactions with an AI Health Coach designed to support patients receiving obesity treatment and deployed with an active patient user base. The evaluation framework evolved through an iterative process of development, testing, refining, training, reviewing and supervision. The framework evaluates at both individual message and overall conversation level, rating interactions as Acceptable or Unacceptable in four domains: Fidelity, Accuracy, Safety, and Tone (FAST), with a series of questions to be considered with respect to each domain. Processes to ensure consistent evaluation quality were established and additional patient safety procedures were defined for escalations to healthcare providers based on clinical risk. The framework can be implemented by trained evaluators and offers a method by which healthcare settings deploying AI to support patients can review quality and safety, thus ensuring safe adoption.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1460236"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12216977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Privacy, ethics, transparency, and accountability in AI systems for wearable devices.","authors":"Petar Radanliev","doi":"10.3389/fdgth.2025.1431246","DOIUrl":"10.3389/fdgth.2025.1431246","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) and machine learning (ML) into wearable sensor technologies has substantially advanced health data science, enabling continuous monitoring, personalised interventions, and predictive analytics. However, the fast advancement of these technologies has raised critical ethical and regulatory concerns, particularly around data privacy, algorithmic bias, informed consent, and the opacity of automated decision-making. This study undertakes a systematic examination of these challenges, highlighting the risks posed by unregulated data aggregation, biased model training, and inadequate transparency in AI-powered health applications. Through an analysis of current privacy frameworks and empirical assessment of publicly available datasets, the study identifies significant disparities in model performance across demographic groups and exposes vulnerabilities in both technical design and ethical governance. To address these issues, this article introduces a data-driven methodological framework that embeds transparency, accountability, and regulatory alignment across all stages of AI development. The framework operationalises ethical principles through concrete mechanisms, including explainable AI, bias mitigation techniques, and consent-aware data processing pipelines, while aligning with legal standards such as the GDPR, the UK Data Protection Act, and the EU AI Act. By incorporating transparency as a structural and procedural requirement, the framework presented in this article offers a replicable model for the responsible development of AI systems in wearable healthcare. In doing so, the study advocates for a regulatory paradigm that balances technological innovation with the protection of individual rights, fostering fair, secure, and trustworthy AI-driven health monitoring.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1431246"},"PeriodicalIF":3.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristine Tarp, Regina Christiansen, Randi Bilberg, Caroline Dalsgaard, Simone Borkner, Marie Folker, Anette S Nielsen
{"title":"Therapist experiences with implementation of blended (iCBT and face-to-face) treatment of alcohol use disorder (Blend-A): mixed methods study.","authors":"Kristine Tarp, Regina Christiansen, Randi Bilberg, Caroline Dalsgaard, Simone Borkner, Marie Folker, Anette S Nielsen","doi":"10.3389/fdgth.2025.1429582","DOIUrl":"10.3389/fdgth.2025.1429582","url":null,"abstract":"<p><strong>Introduction: </strong>Though therapists' experiences of offering internet-based treatment for alcohol use disorder have been examined in previous studies, the process of implementing blended internet-based and face-to-face treatment has so far not been studied. This study aims to investigate therapist experiences during implementation of blended face-to-face and internet-based treatment for alcohol use disorder.</p><p><strong>Methods: </strong>The study employed a mixed methods design, more specifically a triangulation design with a convergence model. Quantitative data using NoMAD were collected in two waves, involving 48 therapists at the 1st wave and 18 at the 2nd wave. Qualitative interviews were conducted six months after the 2nd wave. Eleven therapists participated in focus group interviews for qualitative data collection, and an additional three semi-structured interviews were recorded, transcribed, and subsequently analyzed using the Normalization Process Theory.</p><p><strong>Results: </strong>We found that the therapists generally had a positive experience with implementing blended face-to-face and internet-based treatment for alcohol use disorder and that their motivation to implement increased. The therapists found it challenging to find coherence between digital and face-to-face treatment in the beginning of the implementation process; however, later in the process, they experienced sense-making. Furthermore, the therapists reflected on their own practice regarding the intervention, both in terms of the amount of time spent on the platform and how it was received by the patients. Moreover, the therapists perceived that if they had all been engaged in the intervention to begin with, it would have led to a shared understanding of the platform and collective ownership. Finally, through each of their individual experiences, the therapists had gained adequate knowledge of the digital intervention; thus, had come to each of their individual perceptions of the best way to incorporate the digital technology in their workday.</p><p><strong>Discussion: </strong>Familiarity and perceived normalcy of using Blend-A did not change significantly over time, but the cognitive attitude to Blend-A did. The therapists were optimistic about the possible use of a blended treatment format, and that this had a positive effect on the implementation process. Over time, the therapists developed confidence in benefits and disadvantages of a blended format.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1429582"},"PeriodicalIF":3.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elvis Asangbeng Tanue, Denis L Nkweteyim, Moise Ondua, Ginyu Innocentia Kwalar, Odette Dzemo Kibu, Madeleine L Nyamsi, Peter L Achankeng, Christian Tchapga, Justine Ayuk, Patrick Jolly Ngono Ema, Maurice Marcel Sandeu, Gregory Eddie Halle-Ekane, Jude Dzevela Kong, Dickson Shey Nsagha
{"title":"Leveraging AI in digital one health: an inter-university collaboration for emerging and re-emerging infectious disease control in Cameroon.","authors":"Elvis Asangbeng Tanue, Denis L Nkweteyim, Moise Ondua, Ginyu Innocentia Kwalar, Odette Dzemo Kibu, Madeleine L Nyamsi, Peter L Achankeng, Christian Tchapga, Justine Ayuk, Patrick Jolly Ngono Ema, Maurice Marcel Sandeu, Gregory Eddie Halle-Ekane, Jude Dzevela Kong, Dickson Shey Nsagha","doi":"10.3389/fdgth.2025.1507391","DOIUrl":"10.3389/fdgth.2025.1507391","url":null,"abstract":"<p><p>Emerging and re-emerging infectious diseases (ERID) pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak mitigation. This article explores collaboration between research teams based in the faculties of Health Sciences and Science of the University of Buea and the School of Veterinary Medicine and Science of the University of Ngaoundere (DigiCare Cameroon) for integrating artificial intelligence (AI) for early detection and management of ERID through a Digital One Health (DOH) approach. DigiCare is part of an interdisciplinary network called Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP) aimed at addressing pandemic and epidemic preparedness and response by strengthening more equitable and effective public health preparedness and response to infectious disease outbreaks in low- and middle-income countries. DigiCare is aimed at improving the health and well-being of the population through sustainable and effective solutions that protect lives and ensure a resilient future leveraging on the power of AI and DOH. DigiCare Cameroon was launched on November 23rd, 2023, at the University of Buea campus during an event graced by numerous high-ranking university and government officials from the public health, environmental, scientific research, and veterinary sectors, alongside representatives from civil society, researchers, students, and community leaders. Baseline data have been collected in communities to provide an evidence-based platform to develop applications that tailor AI towards health care delivery using integrated DOH approaches. This inter-university collaboration will ultimately contribute in strengthening the capacities of health systems to prepare, prevent and mitigate epidemics and pandemics.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1507391"},"PeriodicalIF":3.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrea P Garzón-Partida, Citlali B Padilla-Gómez, Diana Emilia Martínez-Fernández, Joaquín García-Estrada, Sonia Luquin, David Fernández-Quezada
{"title":"The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.","authors":"Andrea P Garzón-Partida, Citlali B Padilla-Gómez, Diana Emilia Martínez-Fernández, Joaquín García-Estrada, Sonia Luquin, David Fernández-Quezada","doi":"10.3389/fdgth.2025.1595243","DOIUrl":"10.3389/fdgth.2025.1595243","url":null,"abstract":"<p><p>Mood Disorders are a group of mental health conditions characterized by a disruption of the emotional state that affects the quality of life of the people living with them. Mental Disorders are difficult to diagnose and treat due to the complex processes involved and limitations of the healthcare system. Digital biomarkers have created accessible, long-term, non-invasive, and user-friendly alternatives for the diagnosis, treatment, and monitoring of these conditions. The use of everyday devices like smartphones and smartwatches and specialized tools like actigraphy, in conjunction with powerful statistical tools, artificial intelligence, and machine learning, represents a promising avenue for the implementation of personalized strategies to monitor and treat Mood Disorders, and potentially higher adherence to treatment. We conducted several studies that implement a variety of methodologies and tools to better understand Mood Disorders, using a patient-focused approach with the ultimate goal of identifying better strategies to improve their quality of life.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1595243"},"PeriodicalIF":3.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}