European heart journal. Digital health最新文献

{"title":"Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure.","authors":"Mehran Moazeni, Lieke Numan, Maaike Brons, Jaco Houtgraaf, Frans H Rutten, Daniel L Oberski, Linda W van Laake, Folkert W Asselbergs, Emmeke Aarts","doi":"10.1093/ehjdh/ztad049","DOIUrl":"10.1093/ehjdh/ztad049","url":null,"abstract":"<p><strong>Aims: </strong>Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD).</p><p><strong>Methods and results: </strong>In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods.</p><p><strong>Conclusion: </strong>The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"1 1","pages":"455-463"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41514444","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":"Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study.","authors":"Ali Javed, Daniel Seung Kim, Steven G Hershman, Anna Shcherbina, Anders Johnson, Alexander Tolas, Jack W O'Sullivan, Michael V McConnell, Laura Lazzeroni, Abby C King, Jeffrey W Christle, Marily Oppezzo, C Mikael Mattsson, Robert A Harrington, Matthew T Wheeler, Euan A Ashley","doi":"10.1093/ehjdh/ztad047","DOIUrl":"10.1093/ehjdh/ztad047","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Aims: &lt;/strong&gt;Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods and results: &lt;/strong&gt;We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, &lt;i&gt;P&lt;/i&gt; = 7.1⨯10&lt;sup&gt;-8&lt;/sup&gt;). Hourly stand prompts (+292 steps from baseline, &lt;i&gt;P&lt;/i&gt; = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, &lt;i&gt;P&lt;/i&gt; = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, &lt;i&gt;P&lt;/i&gt; = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital i","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"411-419"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2e/f2/ztad047.PMC10545510.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41170968","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":"The AppCare-HF randomized clinical trial: a feasibility study of a novel self-care support mobile app for individuals with chronic heart failure.","authors":"Takashi Yokota,&nbsp;Arata Fukushima,&nbsp;Miyuki Tsuchihashi-Makaya,&nbsp;Takahiro Abe,&nbsp;Shingo Takada,&nbsp;Takaaki Furihata,&nbsp;Naoki Ishimori,&nbsp;Takeo Fujino,&nbsp;Shintaro Kinugawa,&nbsp;Masayuki Ohta,&nbsp;Shigeo Kakinoki,&nbsp;Isao Yokota,&nbsp;Akira Endoh,&nbsp;Masanori Yoshino,&nbsp;Hiroyuki Tsutsui","doi":"10.1093/ehjdh/ztad032","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad032","url":null,"abstract":"<p><strong>Aims: </strong>We evaluated a self-care intervention with a novel mobile application (app) in chronic heart failure (HF) patients. To facilitate patient-centred care in HF management, we developed a self-care support mobile app to boost HF patients' optimal self-care.</p><p><strong>Methods and results: </strong>We conducted a multicentre, randomized, controlled study evaluating the feasibility of the self-care support mobile app designed for use by HF patients. The app consists of a self-monitoring assistant, education, and automated alerts of possible worsening HF. The intervention group received a tablet personal computer (PC) with the self-care support app installed, and the control group received a HF diary. All patients performed self-monitoring at home for 2 months. Their self-care behaviours were evaluated by the European Heart Failure Self-Care Behaviour Scale. We enrolled 24 outpatients with chronic HF (ages 31-78 years; 6 women, 18 men) who had a history of HF hospitalization. During the 2 month study period, the intervention group (<i>n</i> = 13) showed excellent adherence to the self-monitoring of each vital sign, with a median [interquartile range (IQR)] ratio of self-monitoring adherence for blood pressure, body weight, and body temperature at 100% (92-100%) and for oxygen saturation at 100% (91-100%). At 2 months, the intervention group's self-care behaviour score was significantly improved compared with the control group (<i>n</i> = 11) [median (IQR): 16 (16-22) vs. 28 (20-36), <i>P</i> = 0.02], but the HF Knowledge Scale, the General Self-Efficacy Scale, and the Short Form-8 Health Survey scores did not differ between the groups.</p><p><strong>Conclusion: </strong>The novel mobile app for HF is feasible.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"325-336"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/4a/ztad032.PMC10393880.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9929223","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":"Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.","authors":"Samer S Al-Droubi,&nbsp;Eiman Jahangir,&nbsp;Karl M Kochendorfer,&nbsp;Marianna Krive,&nbsp;Michal Laufer-Perl,&nbsp;Dan Gilon,&nbsp;Tochukwu M Okwuosa,&nbsp;Christopher P Gans,&nbsp;Joshua H Arnold,&nbsp;Shakthi T Bhaskar,&nbsp;Hesham A Yasin,&nbsp;Jacob Krive","doi":"10.1093/ehjdh/ztad031","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad031","url":null,"abstract":"<p><strong>Aims: </strong>There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.</p><p><strong>Methods and results: </strong>De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals.</p><p><strong>Conclusion: </strong>Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"302-315"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5c/4d/ztad031.PMC10393891.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9929222","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":"Corrigendum to: ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story?","authors":"","doi":"10.1093/ehjdh/ztad034","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad034","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/ehjdh/ztad029.].</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"357"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/92/bb/ztad034.PMC10393937.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10309332","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":"Mobile health for cardiovascular risk management after cardiac surgery: results of a sub-analysis of The Box 2.0 study.","authors":"Tommas Evan Biersteker,&nbsp;Mark J Boogers,&nbsp;Martin Jan Schalij,&nbsp;Jerry Braun,&nbsp;Rolf H H Groenwold,&nbsp;Douwe E Atsma,&nbsp;Roderick Willem Treskes","doi":"10.1093/ehjdh/ztad035","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad035","url":null,"abstract":"<p><strong>Aims: </strong>Lowering low-density lipoprotein (LDL-C) and blood pressure (BP) levels to guideline recommended values reduces the risk of major adverse cardiac events in patients who underwent coronary artery bypass grafting (CABG). To improve cardiovascular risk management, this study evaluated the effects of mobile health (mHealth) on BP and cholesterol levels in patients after standalone CABG.</p><p><strong>Methods and results: </strong>This study is a <i>post hoc</i> analysis of an observational cohort study among 228 adult patients who underwent standalone CABG surgery at a tertiary care hospital in The Netherlands. A total of 117 patients received standard care, and 111 patients underwent an mHealth intervention. This consisted of frequent BP and weight monitoring with regimen adjustment in case of high BP. Primary outcome was difference in systolic BP and LDL-C between baseline and value after three months of follow-up. Mean age in the intervention group was 62.7 years, 98 (88.3%) patients were male. A total of 26 449 mHealth measurements were recorded. At three months, systolic BP decreased by 7.0 mmHg [standard deviation (SD): 15.1] in the intervention group vs. -0.3 mmHg (SD: 17.6; <i>P</i> < 0.00001) in controls; body weight decreased by 1.76 kg (SD: 3.23) in the intervention group vs. -0.31 kg (SD: 2.55; <i>P</i> = 0.002) in controls. Serum LDL-C was significantly lower in the intervention group vs. controls (median: 1.8 vs. 2.0 mmol/L; <i>P</i> = 0.0002).</p><p><strong>Conclusion: </strong>This study showed an association between home monitoring after CABG and a reduction in systolic BP, body weight, and serum LDL-C. The causality of the association between the observed weight loss and decreased LDL-C in intervention group patients remains to be investigated.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"347-356"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5b/33/ztad035.PMC10393886.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9935780","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":"An augmented reality-based method to assess precordial electrocardiogram leads: a feasibility trial.","authors":"Peter Daniel Serfözö,&nbsp;Robin Sandkühler,&nbsp;Bibiana Blümke,&nbsp;Emil Matthisson,&nbsp;Jana Meier,&nbsp;Jolein Odermatt,&nbsp;Patrick Badertscher,&nbsp;Christian Sticherling,&nbsp;Ivo Strebel,&nbsp;Philippe C Cattin,&nbsp;Jens Eckstein","doi":"10.1093/ehjdh/ztad046","DOIUrl":"10.1093/ehjdh/ztad046","url":null,"abstract":"<p><strong>Aims: </strong>It has been demonstrated that several cardiac pathologies, including myocardial ischaemia, can be detected using smartwatch electrocardiograms (ECGs). Correct placement of bipolar chest leads remains a major challenge in the outpatient population.</p><p><strong>Methods and results: </strong>In this feasibility trial, we propose an augmented reality-based smartphone app that guides the user to place the smartwatch in predefined positions on the chest using the front camera of a smartphone. A machine-learning model using MobileNet_v2 as the backbone was trained to detect the bipolar lead positions V1-V6 and visually project them onto the user's chest. Following the smartwatch recordings, a conventional 10 s, 12-lead ECG was recorded for comparison purposes. All 50 patients participating in the study were able to conduct a 9-lead smartwatch ECG using the app and assistance from the study team. Twelve patients were able to record all the limb and chest leads using the app without additional support. Bipolar chest leads recorded with smartwatch ECGs were assigned to standard unipolar Wilson leads by blinded cardiologists based on visual characteristics. In every lead, at least 86% of the ECGs were assigned correctly, indicating the remarkable similarity of the smartwatch to standard ECG recordings.</p><p><strong>Conclusion: </strong>We have introduced an augmented reality-based method to independently record multichannel smartwatch ECGs in an outpatient setting.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"420-427"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d9/3d/ztad046.PMC10545517.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41123451","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":"Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival.","authors":"Thomas Lindow,&nbsp;Maren Maanja,&nbsp;Erik B Schelbert,&nbsp;Antônio H Ribeiro,&nbsp;Antonio Luiz P Ribeiro,&nbsp;Todd T Schlegel,&nbsp;Martin Ugander","doi":"10.1093/ehjdh/ztad045","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad045","url":null,"abstract":"<p><strong>Aims: </strong>Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age.</p><p><strong>Methods and results: </strong>Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [<i>n</i> = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice.</p><p><strong>Conclusion: </strong>A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"384-392"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ad/fe/ztad045.PMC10545529.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41143601","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":"Performance of artificial intelligence in answering cardiovascular textual questions.","authors":"Ioannis Skalidis, Aurelien Cagnina, Stephane Fournier","doi":"10.1093/ehjdh/ztad042","DOIUrl":"10.1093/ehjdh/ztad042","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"364-365"},"PeriodicalIF":3.9,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e2/58/ztad042.PMC10545520.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41175901","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":"Use of large language models for evidence-based cardiovascular medicine.","authors":"Ioannis Skalidis, Aurelien Cagnina, Stephane Fournier","doi":"10.1093/ehjdh/ztad041","DOIUrl":"10.1093/ehjdh/ztad041","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"368-369"},"PeriodicalIF":3.9,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2a/60/ztad041.PMC10545494.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41179694","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}