PLOS digital health最新文献

{"title":"Formative evaluation of the acceptance of HIV prevention Artificial Intelligence chatbots by Black gay, bisexual, and other men who have sex with men in the Southern United States: Focus group study.","authors":"Jackson Jr Nforbewing Ndenkeh, Gloria A Aidoo-Frimpong, LaRon E Nelson, Mary L Peng, Vimala Balakrishnan, Victoria Barnhart, Bernard Davis, James Donté Prayer, Alvan Quamina, Zhao Ni","doi":"10.1371/journal.pdig.0000891","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000891","url":null,"abstract":"<p><p>Gay, bisexual, and other men who have sex with men (MSM) account for 60% of new HIV infections among Black Americans in the Southern United States (U.S.). Despite recommendations for frequent HIV testing and daily pre-exposure prophylaxis (PrEP) uptake, there remains a gap in PrEP uptake among these Black MSM in the Southern U.S. Artificial Intelligence (AI) chatbots have the potential to boost users' health awareness and medication adherence. This study aims to evaluate Black MSM' perspectives on the challenges to the uptake of PrEP and identify Black MSM-preferred chatbot functionalities and platforms for embedding AI chatbots. Five focus group discussions were conducted (February - March 2024) among 21 Black MSM in the Southern U.S. Interview transcripts were thematically analyzed according to challenges to PrEP uptake and the four domains of the Unified Theory of Acceptance and Use of Technology (UTAUT): performance expectancy, effort expectancy, facilitating conditions, and social influence. Black MSM identified lack of awareness or insufficient information, stigmatizations of sexuality, HIV, and PrEP, as well as concerns with side effects, and low self-perceived HIV vulnerability as the major challenges they faced in PrEP uptake. Moreover, chatbots were perceived as an acceptable option for delivering PrEP education (performance expectancy), especially with accessible, user-friendly interfaces (effort expectancy). Other desired features included simplifying access to PrEP information, incorporating culturally sensitive algorithms, upholding anonymity (social influence), and linking users to healthcare providers and resources (facilitating condition). The study highlights the multifaceted considerations for the adoption of AI chatbots as an HIV-prevention intervention among Black MSM in the Southern U.S.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000891"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing fairness in disease prediction by optimizing multiple domain adversarial networks.","authors":"Bin Li, Xiaoqian Jiang, Kai Zhang, Arif O Harmanci, Bradley Malin, Hongchang Gao, Xinghua Shi","doi":"10.1371/journal.pdig.0000830","DOIUrl":"10.1371/journal.pdig.0000830","url":null,"abstract":"<p><p>Predictive models in biomedicine need to ensure equitable and reliable outcomes for the populations they are applied to. However, biases in AI models for medical predictions can lead to unfair treatment and widening disparities, underscoring the need for effective techniques to address these issues. However, current approaches struggle to simultaneously mitigate biases induced by multiple sensitive features in biomedical data. To enhance fairness, we introduce a framework based on a Multiple Domain Adversarial Neural Network (MDANN), which incorporates multiple adversarial components. In an MDANN, an adversarial module is applied to learn a fair pattern by negative gradients back-propagating across multiple sensitive features (i.e., the characteristics of patients that should not lead to a prediction outcome that may intentionally or unintentionally lead to disparities in clinical decisions). The MDANN applies loss functions based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address the class imbalance, promoting equitable classification performance for minority groups (e.g., a subset of the population that is underrepresented or disadvantaged.) Moreover, we utilize pre-trained convolutional autoencoders (CAEs) to extract deep representations of data, aiming to enhance prediction accuracy and fairness. Combining these mechanisms, we mitigate multiple biases and disparities to provide reliable and equitable disease prediction. We empirically demonstrate that the MDANN approach leads to better accuracy and fairness in predicting disease progression using brain imaging data and mitigating multiple demographic biases for Alzheimer's Disease and Autism populations than other adversarial networks.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000830"},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188650","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":"Impact of digital wound care solution on healing time: A descriptive study in home health settings.","authors":"Heba Tallah Mohammed, Robert D J Fraser, Amy Cassata","doi":"10.1371/journal.pdig.0000855","DOIUrl":"10.1371/journal.pdig.0000855","url":null,"abstract":"<p><strong>Background: </strong>Chronic wounds pose significant challenges in home healthcare (HH) due to prolonged healing times and high costs. Digital wound care solutions (DWCS) have shown potential for improving healing efficiency. This study evaluated the impact of continuous DWCS use on healing times at HH organizations and explored area reduction in non-healed yet improved pressure injuries (PIs) and diabetic ulcers (DUs).</p><p><strong>Methods: </strong>This descriptive study analyzed 195,915 wound assessments from 59 HH organizations using DWCS in 2022 and 2023. Average healing time was calculated by wound type and compared across the two years, with subgroup analyses for wounds healing within three months versus longer. Improvements in non-healed DUs and PIs were further categorized by initial wound size (≤2 cm², >2 cm² for DUs; ≤4 cm², >4 cm² for PIs).</p><p><strong>Results: </strong>Average healing time for all wounds decreased significantly from 62.5 days in 2022 to 38.6 days in 2023, a 38.2% improvement (p < 0.001). DU and PIs showed reductions of 30.8 and 29.3 days, respectively. The proportion of wounds healing within three months rose by 8.9%, with decreased average healing times within this period. For wounds requiring over three months, the average time saved was 57.6 days (8.2 weeks; P = 0.014), representing a 27% improvement. Non-healed but improving PIs showed increase in area reduction from 5.2 cm² to 17.7 cm², with a 25.4% faster time to reduction. Larger PIs (>4 cm²) showed greater reductions, with time to improvement decreasing by 35.5 days (34.7%, p < 0.001). DUs also improved, with area reduction increasing from 4.8 cm² to 15.3 cm² and a 23.8% faster reduction time, while larger DUs (>2 cm²) saw a 32.6-day decrease in time to improvement.</p><p><strong>Conclusion: </strong>Continuous DWCS use significantly reduces healing times and improves wound area reduction, underscoring its effectiveness in enhancing wound care outcomes in HH settings.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000855"},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188651","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":"Patient engagement strategies in digital health interventions for cancer survivors: A scoping review.","authors":"Maria Ren, Camila E Orsso, Homa Ghomashchi, Bruna R da Silva, Christa Aubrey, Ingrid Nielssen, Sophia Pin, Margaret L McNeely, Puneeta Tandon, Carla M Prado","doi":"10.1371/journal.pdig.0000871","DOIUrl":"10.1371/journal.pdig.0000871","url":null,"abstract":"<p><p>Individuals can face various mental and physical health challenges after a cancer diagnosis. Digital health platforms can address some of these challenges by providing self-management tools for improving lifestyle behaviors, while reducing the burden on healthcare systems and enhancing healthcare access to underserved populations. Involving individuals with a history of cancer, termed here as \"cancer survivors\", in the development and evaluation of digital health platforms can improve their effectiveness. This scoping review aimed to explore the state of patient engagement in research on digital health platforms for cancer survivors, including strategies for engagement, characteristics, and identifying gaps and barriers. A systematic search was conducted in OVID Medline, OVID EMBASE, and Scopus from inception until May 2023. The review followed Joanna Briggs Institute's guidance for scoping reviews. Eligible studies actively involved cancer survivors in the development or evaluation of digital health platforms. These studies focused on self-management digital health platforms delivering nutrition, physical activity, and/or mental health interventions. Reporting of patient engagement was evaluated according to the Guidance for Reporting Involvement of Patients and the Public 2 (GRIPP2). The search strategy captured 7 studies using various patient engagement approaches, with patient and public involvement being the most frequently used (43%, n = 3). Studies were conducted in 6 countries and most focused on the development or evaluation of web-based digital health platforms (71%, n = 5). Few studies reported all elements of GRIPP2's reporting checklist (29%, n = 2). We further identified barriers and areas of improvement for patient engagement in digital health research. Patient engagement improves digital health platforms, but few studies have meaningfully included patients, therefore reporting and evaluation of patient engagement is necessary to support its adoption in digital health research projects. In addition to exploring the gaps in patient engagement practices, this scoping review serves as a foundation for future research to advance patient-oriented digital health interventions for cancer survivors.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000871"},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188605","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":"Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review.","authors":"Gerko Schaap, Benjamin Butt, Christina Bode","doi":"10.1371/journal.pdig.0000832","DOIUrl":"10.1371/journal.pdig.0000832","url":null,"abstract":"<p><p>Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem promising in meeting these needs, as they aim to provide tailored interventions based on patient-centred measures. This systematic scoping review explores the suitability and examines key components of a potential JITAI in post-COVID-19 syndrome. Databases (PsycINFO, PubMed, and Scopus) were searched using terms related to post-COVID-19-related symptom clusters (fatigue and pain; respiratory problems; cognitive dysfunction; psychological problems) and to JITAI. Studies were summarised to identify potential components (interventions options, tailoring variables and decision rules), feasibility and effectiveness, and potential barriers. Out of the 341 screened records, 11 papers were included (five single-armed pilot or feasibility studies, three two-armed randomised controlled trial studies, and three observational studies). Two articles addressed fatigue or pain-related complaints, and nine addressed psychological problems. No articles about JITAI for respiratory problems or cognitive dysfunction clusters were found. Most interventions provided monitoring, education or reinforcement support, using mostly ecological momentary assessments or smartphone-based sensing. JITAIs were found to be acceptable and feasible, and seemingly effective, although evidence is limited. Given these findings, a JITAI for post-COVID-19 syndrome is promising, but needs to fit the complex, multifaceted nature of its symptoms. Future studies should assess the feasibility of machine learning to accurately predict when to execute timely interventions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000832"},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183168","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":"Standardization and accuracy of race and ethnicity data: Equity implications for medical AI.","authors":"Alexandra Tsalidis, Lakshmi Bharadwaj, Francis X Shen","doi":"10.1371/journal.pdig.0000807","DOIUrl":"10.1371/journal.pdig.0000807","url":null,"abstract":"<p><p>The rapid integration of artificial intelligence (AI) into healthcare has raised many concerns about race bias in AI models. Yet, overlooked in this dialogue is the lack of quality control for the accuracy of patient race and ethnicity (r/e) data in electronic health records (EHR). This article critically examines the factors driving inaccurate and unrepresentative r/e datasets. These include conceptual uncertainties about how to categorize races and ethnicity, shortcomings in data collection practices, EHR standards, and the misclassification of patients' race or ethnicity. To address these challenges, we propose a two-pronged action plan. First, we present a set of best practices for healthcare systems and medical AI researchers to improve r/e data accuracy. Second, we call for developers of medical AI models to transparently warrant the quality of their r/e data. Given the ethical and scientific imperatives of ensuring high-quality r/e data in AI-driven healthcare, we argue that these steps should be taken immediately.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000807"},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183369","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":"Planning for successful participant recruitment and retention in trials of behavioural interventions: Feasibility randomised controlled trial of the Wrapped intervention.","authors":"Lauren Schumacher, Rik Crutzen, Kayleigh Kwah, Katherine Brown, Julia V Bailey, Stephen Bremner, Louise J Jackson, Katie Newby","doi":"10.1371/journal.pdig.0000875","DOIUrl":"10.1371/journal.pdig.0000875","url":null,"abstract":"<p><p>Randomised controlled trials (RCTs) must have sufficient power if planned analyses are to be performed and strong conclusions drawn. A prerequisite of this is successful participant recruitment and retention. Designing a comprehensive plan for participant recruitment and retention prior to trial commencement is recommended, but evidence concerning successful strategies, and how to go about developing a comprehensive plan, is lacking. This paper reports on the application of a six-stage process to develop a recruitment and retention strategy for a future RCT. Stage 1) Rapid evidence review: strategies used in previous trials were identified through database searching. This informed Stage 2) PPI workshop: workshops with public and patient involvement (PPI) group were used to select a sub-set of these strategies based on their potential to be successful and acceptable with the target audience. Stage 3) Focus groups with the target audience: the sub-set was refined through feedback from 15 young people (data subjected to content analysis). Strategies the PPI and focus groups mutually agreed upon proceeded directly to Stage 5; those without consensus proceeded to Stage 4. Stage 4) PPI workshop: PPI members voted on the remaining strategies; those without consensus were discarded. Stage 5) Observation of strategies during feasibility RCT (fRCT): the retained set of strategies were observed in practice in a fRCT in which recruitment and retention data and qualitative feedback from participants was collected. Stage 6) PPI workshop: the fRCT findings were reviewed and strategies for use in the future RCT were finalised. The finalised strategy included set of adverts; schedule of financial incentives; instructions to send survey invite by email, one prompt by SMS prior to data collection, and up to three SMS reminders; procedure to keep participants engaged (e.g., newsletters, personalisation of communications); and procedure if participants fail to complete a research activity (follow-up email/phone call).</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000875"},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183526","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":"AI-driven healthcare: Fairness in AI healthcare: A survey.","authors":"Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe, Xingyu Zhang, Ayesha Kashif, Monique Antoinette Smith, Jun Liu, Wenbin Zhang","doi":"10.1371/journal.pdig.0000864","DOIUrl":"10.1371/journal.pdig.0000864","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000864"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112934","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":"Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation.","authors":"Tianhao Zhu, Kexin Xu, Wonchan Son, Kristofer Linton-Reid, Marc Boubnovski-Martell, Matt Grech-Sollars, Antoine D Lain, Joram M Posma","doi":"10.1371/journal.pdig.0000835","DOIUrl":"10.1371/journal.pdig.0000835","url":null,"abstract":"<p><p>Chest X-ray (CXR) is a diagnostic tool for cardiothoracic assessment. They make up 50% of all diagnostic imaging tests. With hundreds of images examined every day, radiologists can suffer from fatigue. This fatigue may reduce diagnostic accuracy and slow down report generation. We describe a prototype computer-assisted diagnosis (CAD) pipeline employing computer vision (CV) and Natural Language Processing (NLP). It was trained and evaluated on the publicly available MIMIC-CXR dataset. We perform image quality assessment, view labelling, and segmentation-based cardiomegaly severity classification. We use the output of the severity classification for large language model-based report generation. Four board-certified radiologists assessed the output accuracy of our CAD pipeline. Across the dataset composed of 377,100 CXR images and 227,827 free-text radiology reports, our system identified 0.18% of cases with mixed-sex mentions, 0.02% of poor quality images (F1 = 0.81), and 0.28% of wrongly labelled views (accuracy 99.4%). We assigned views for 4.18% of images which have unlabelled views. Our binary cardiomegaly classification model has 95.2% accuracy. The inter-radiologist agreement on evaluating the generated report's semantics and correctness for radiologist-MIMIC is 0.62 (strict agreement) and 0.85 (relaxed agreement) similar to the radiologist-CAD agreement of 0.55 (strict) and 0.93 (relaxed). Our work found and corrected several incorrect or missing metadata annotations for the MIMIC-CXR dataset. The performance of our CAD system suggests performance on par with human radiologists. Future improvements revolve around improved text generation and the development of CV tools for other diseases.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000835"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112940","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":"Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived from cardiopulmonary exercise testing.","authors":"Pietro Arina, Davide Ferrari, Maciej R Kaczorek, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, Ramani Moonesinghe, Mervyn Singer, John Whittle, Evangelos B Mazomenos","doi":"10.1371/journal.pdig.0000851","DOIUrl":"10.1371/journal.pdig.0000851","url":null,"abstract":"<p><p>Accurate preoperative risk assessment is of great value to both patients and clinical teams. Several risk scores have been developed but are often not calibrated to the local institution, limited in terms of data input into the underlying models, and/or lack individual precision. Machine Learning (ML) models have the potential to address limitations in existing scoring systems. A database of 1190 elderly patients who underwent major elective surgery was analyzed retrospectively. Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). Shapley-Additive-exPlanations (SHAP) was subsequently used to analyze feature contributions. Model performance was benchmarked against existing risk prediction scores, namely the Portsmouth-Physiological-and-Operative-Severity-Score-for-the-Enumeration-of-Mortality-and-Morbidity (PPOSSUM) and the Duke-Activity-Status-Index, as well as linear regression using CPET features. A model was also developed for the same task using data directly extracted from the CPET time-series. The incorporation of cardiorespiratory fitness data enhanced the performance of all models for predicting postoperative morbidity by 20% compared to sole reliance on clinical data. Cardiorespiratory fitness features demonstrated greater importance than clinical features in the SHAP analysis. Models utilizing data taken directly from the CPET time-series demonstrated a 12% improvement over the cardiorespiratory fitness models. MOSR model surpassed all other models in every experiment, demonstrating excellent robustness and generalization capabilities. Integrating cardiorespiratory fitness data with ML models enables improved preoperative prediction of postoperative morbidity in elective surgical patients. The MOSR model stands out for its capacity to pinpoint essential features and build models that are both simple and accurate, showing excellent generalizability.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000851"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082571","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}