dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonlinear Approach Figure  9  and Table  S3  in Multimedia Appendix 1 show, for the nonlinear approach, errors and correlation for AG at the national and regional levels, for forecasts up to 10 weeks. At the national level, in terms of errors, the lowest values are obtained by using only historical data-AR(52). In terms of correlation, for long-term forecasts, the highest values are obtained by using only historical data. At the regional level, in terms of errors, for forecast up to 4 weeks, the lowest values are obtained by using only historical data. However, for long-term forecasts, the most accurate results are obtained by using historical data and both data sources, Google and EHR. Figure  9  and Table  S4  in Multimedia Appendix 1 show, for the nonlinear approach, errors and correlation for influenza at the national and regional levels, for forecasts up to 10 weeks.
At the national level, in terms of errors and correlation, the most accurate values for forecasts up to 2 weeks are obtained by using historical data and both Google and EHR data. For forecasts from up to 3 weeks to up to 5 weeks, most accurate estimates are obtained by using only historical data. For long-term forecasts, results are similar for both models, the one using only historical data and the one using historical data and Google and EHR data. At the regional level, for forecasts up to 4 weeks, in terms of errors, the lowest values are obtained, in most cases, by using only historical data. For long-term forecasts, the most accurate estimates are obtained with the model using historical data and both Google and EHR data. In terms of correlation, in most cases, the highest values are obtained by using historical data and both Google and EHR data.
Principal Findings We adjusted a methodology developed for influenza, to accurately track AG activity. Our method is able to provide forecasts up to 10 weeks for national and regional levels and for emergency and hospitalization stays (Multimedia Appendix 1). To the best of our knowledge, this is a disease and a spatial resolution (French regions and hospitals) for which no forecasting approaches have been explored previously. In this study, we show that external data sources, EHR and Google, contribute to improving AG surveillance, in particular for long-term forecasts, with more important contribution from historical data. Specifically, when we use the linear approach (Elastic Net), in terms of errors at the national level, the lowest values are obtained by using historical data and both Google and EHR data.
These results are consistent for forecasts from up to 1 week to up to 10 weeks (Table  S1  in Multimedia Appendix 1). At the regional level, the model using only historical data is the model producing the lowest errors for short-term forecasts (Table  S1  in Multimedia Appendix 1). However, for long-term forecasts, the inclusion of external data sources (Google and EHR) improves the estimates. We conducted a Diebold Mariano test  [39]  to assess if the forecasts are statistically different when using only historical data or the combination of historical data, Google data, and EHR data (Table  S5  in Multimedia Appendix 1). We can see that at the national level, the estimates are statistically more accurate when using historical data and both Google and EHR data for 3-week and long-term forecasts.
At the regional level, the use of external data sources produces estimates that are statistically more accurate for 7-week and long-term forecasts. As we used a method developed for influenza outbreaks, we compared the results obtained for AG with those obtained for influenza. At the national and regional levels, with the linear approach, for both short-term and long-term forecasts, the most accurate estimates are obtained with the model using historical data and external data sources (Google and EHR data). An understanding of these results can emerge from the time series analysis (Figure  7 ). We show that the seasonality is more important for AG epidemics than for influenza, resulting in historical data capable of providing more information for AG than for influenza. Nonetheless, for long-term forecasts, historical data are not sufficient and external data sources can be used to supplement them.
Thus, it is important to integrate external data to improve long-term estimates. In addition to the linear approach, we conducted the same analysis with a nonlinear approach (RF). At the national level, the results differ slightly from those obtained using the linear approach. In terms of error and correlation, the model using only historical data provides more accurate estimates than the model using historical data, Google data, and EHR data. These results are consistent for real-time estimates and long-term forecasts (Table  S3  in Multimedia Appendix 1). At the regional level, regarding the linear approach, in terms of error for short-term forecasts, the model using only historical data allows to produce the most accurate estimates. For long-term forecasts, the model including external data sources, Google and EHR, decreases the error.
In terms of correlation, for both short-term and long-term forecasts, the model producing the highest values is the model using historical data, Google data, and EHR data. In all cases, the nonlinear approach allows us to obtain high values in terms of correlation and low values in terms of error when compared with those obtained using the linear approach. However, as seen in Figures  5  and 6 , the nonlinear approach tends to smooth the estimates compared with those obtained using the linear approach. This can result in decrease in error and increase in correlation. The fact that we could only access EHR data from Rennes University Hospital, and thus from the Brittany region, prevented us from being able to quantify the added value of nation-wide EHR information. This should be evaluated in future studies by integrating EHR data from different hospitals from all the French regions.
However, it is interesting that data from a hospital in Rennes can improve AG forecasting at the national level, even if, as we described previously, EHR data seem more important for the regional level. Data retrieved from Google Correlate are normalized by Google in a (frequently) distinct sample and over different time periods depending on the data request. This prenormalization can affect our results, but as shown in the study by Arena et al  [15] , the process of dynamic training minimizes the impact of this instability. It would be interesting to test other approaches that gave good results for influenza, for example, an ensemble method that combines the power of the linear and the nonlinear approaches  [14]  or other machine learning methods such as Support Vector Machine or neural networks.
We tested a long short-term memory model to forecast gastroenteritis up to 10 weeks. We obtained root mean squared error=2. 96 for real-time forecasting. We believe that these results are really promising and could be further studied in the future by developing a neural network combining long short-term memory for historical data and another neural network for external data sources such as Google data or EHR data. In addition, other methods could be tested to obtain more information from external data sources as transformations of the input variables. Variable transformations could be tested on external data sources to check whether we could get more information. Finally, it could be meaningful to first remove the multicollinearity of our predictive variables with traditional methods such as the Variance Inflation Factor and then select the most important variables with a stepwise regression to run a linear regression on the remaining variables.
Acknowledgments The authors would like to thank the  French Agence Nationale de Recherche  for funding this study through the Integrating and  Sharing Health Data  for  Research project  (grant  ANR-15-CE19-0024 ). The authors also thank the  French Sentinel network and Google  search engine for making their data publicly available. MS and CP were partially funded by the  National Institute of General Medical Sciences  of the  National Institutes of Health , under award number  R01GM130668. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Digoxin is beneficial as a second-line remedy in those with a suboptimal response to betaor calcium channel blockers. In a small randomized open label study in older patients the majority of whom had HFpEF, digoxin favoured better than bisoprolol in respect of symptom relief and quality of life scores and was associated with a greater adherence to treatment at 6 months. 140 Digitoxin is a potential alternative to digoxin and is currently being evaluated in a randomized placebo-controlled trial (ClinicalTrials. gov Identifier: NCT03783429). As a last resort amiodarone is advocated, especially in patients with HFrEF. 134 f the two prime treatment strategies for AF rhythm control is intuitively more attractive as it offers physiological rate control, normal atrial activation and contraction, the correct sequence of AV activation, normal haemodynamic and AV valve function, and theoretically eliminates one (stasis) or more (endothelial abnormality or increased thrombogenic blood constituents) of Virchow's triad of elements that encourage thrombosis within the atria and embolization of blood clots to potentially critical parts of the circulation.
136 In acutely hospitalized frail elderly AF patients, OAC non-use was associated with significantly higher adjusted rate of composite outcome of ischaemic stroke or bleeding compared with OAC use [HR 4. 54 (95%CI, 1. 83-11. 25)],  138 and in a community-dwelling cohort of older AF patients using OAC, the stroke incident rate was higher than that of major bleeding (1. 73 vs. 0. 9/100 person-years). 161 In a retrospective, propensity scorematched analysis of a US administrative dataset of frail AF patients, stroke or systemic embolism rates were significantly lower with rivaroxaban vs. warfarin [1. 78 vs. 2. 61, HR 0. 68 (95%CI 0. 49-0. 95)] and similar with apixaban (1. 68 vs. 2. 15) and dabigatran (2. 06 vs. 2. 20) vs. warfarin, while major bleeding rates were similar with all four OAC drugs and intracranial bleeding was significantly lower with all three NOACs compared with warfarin.
Left atrial appendage occlusion In general, the most common justification for left atrial (LAA) closure over systemic OAC is high bleeding risk (e. g. due to falls, liver and kidney dysfunction, polypharmacy and drug interaction) or absolute contraindications to systemic OAC, with frail patients being particularly vulnerable. The decision-making is hindered by insufficient highquality prospective data comparing LAA occlusion devices with NOACs and the need for antithrombotic therapy after implant and by lack of systematic experience of such an intervention in frail patients, although, according to recent Medicare database analysis, nearly half the patients who underwent LAA closure were considered frail at intermediate or high levels of frailty. 164 At present, the ESC Guidelines restrict their recommendations for LAA closure therapy to patients with contraindications for long-term OAC, for example, intracranial bleeding without a reversible cause and to patients undergoing cardiac surgery who could have simultaneous surgical LAA occlusion or exclusion.
134 lthough the peri-procedural complications and mortality associated with percutaneous LAA closure is likely to be higher in frailty, this therapy may benefit frail patients in the long-term as analysis of the Medicare database has recently demonstrated. It included 21 787 patients aged 65 years and older who underwent LAA closure, 10 740 (49. 3%) of whom were considered frail based on the Hospital Frailty Risk Score (HFRS) >5; 33. 5% were regarded as intermediate (HFRS 5-15) and 15. 8% as high risk (HFRS >15). HFRS was calculated on the basis of 109 International Classification of Diseases, Tenth Revision, Clinical Modification secondary diagnosis codes from all hospitalizations occurring at least 1 year before the date of admission for the index hospitalization or using secondary diagnosis codes during the index admission.
181  The prevalence of AHREs or AF burden, among patients implanted with CIEDs varies, depending on underlying heart disease, periods of observation, and above all previous history of clinically overt atrial tachyarrhythmias, including AF. 183  An analysis of all the data from the literature reveals that that AHREs with a duration >5-6 min are common in patients implanted with CIEDs, with an incidence between 10 and 68%. 184  The association between CIED-detected atrial tachyarrhythmias of variable durations and stroke or systemic thromboembolism has been evaluated by several studies that overall collected data on >22,000 patients. It  s been shown that AHRE burden with a duration ≥5-6 min is significantly associated with increased risk of stroke or systemic thromboembolism, with a hazard ratio ranging between 2 and 9.
Acknowledgement The authors thank the EHRA Scientific Document Committee:  Dr Nikolaos Dagres ,  Prof. Thomas Deneke ,  Prof. Arthur Wilde ,  Prof. Frank R. Heinzel ,  Prof. Christian Meyer ,  Prof. Lucas Boersma ,  Prof. Radoslaw Lenarczyk ,  Prof. Luigi di Biase ,  Dr Elena Arbelo ,  Dr Avi Sabbag ,  Prof. Pierre Jais ,  Prof. Milos Taborsky , Asso. Prof. Markus Stühlinger.
Background: European national disparities in the integration of data linkage (ie, being able to match patient data between databases) into routine public health activities were recently highlighted. In France, the claims database covers almost the whole population from birth to death, offering a great research potential for data linkage. As the use of a common unique identifier to directly link personal data is often limited, linkage with a set of indirect key identifiers has been developed, which is associated with the linkage quality challenge to minimize errors in linked data. Objective: The aim of this systematic review is to analyze the type and quality of research publications on indirect data linkage on health product use and care trajectories in France.
Methods: A comprehensive search for all papers published in PubMed/Medline and Embase databases up to December 31, 2022, involving linked French database focusing on health products use or care trajectories was realized. Only studies based on the use of indirect identifiers were included (ie, without a unique personal identifier available to easily link the databases). A descriptive analysis of data linkage with quality indicators and adherence to the Bohensky framework for evaluating data linkage studies was also realized.
Results: In total, 16 papers were selected. Data linkage was performed at the national level in 7 (43. 8%) cases or at the local level in 9 (56. 2%) studies. The number of patients included in the different databases and resulting from data linkage varied greatly, respectively, from 713 to 75,000 patients and from 210 to 31,000 linked patients. The diseases studied were mainly chronic diseases and infections. The objectives of the data linkage were multiple: to estimate the risk of adverse drug reactions (ADRs; n=6, 37. 5%), to reconstruct the patient's care trajectory (n=5, 31. 3%), to describe therapeutic uses (n=2, 12. 5%), to evaluate the benefits of treatments (n=2, 12. 5%), and to evaluate treatment adherence (n=1, 6. 3%). Registries are the most frequently linked databases with French claims data.
No studies have looked at linking with a hospital data warehouse, a clinical trial database, or patient self-reported databases. The linkage approach was deterministic in 7 (43. 8%) studies, probabilistic in 4 (25. 0%) studies, and not specified in 5 (31. 3%) studies. The linkage rate was mainly from 80% to 90% (reported in 11/15, 73. 3%, studies). Adherence to the Bohensky framework for evaluating data linkage studies showed that the description of the source databases for the linkage was always performed but that the completion rate and accuracy of the variables to be linked were not systematically described.
In France, since the 2016 law on the modernization of the health system, the use of existing health data and their pooling have been promoted. As a result, French medicoadministrative databases (MADs) were linked together in a single French national health data system (the Système National des Données de Santé  [SNDS] ) in 2017 based on a reliable identification of individuals by the numéro d'inscription au repertoire (NIR), de-identified using 2 successive hash scrambling operations  [13, 14]. The French national health data system groups together (1) data on claims for reimbursement of outpatient care (the Système National d'Information Inter-Régimes de l'Assurance Maladie [SNIIRAM]),  (2)  medicoadministrative data on hospitalization in public and private hospitals (the Programme de Médicalisation des Systèmes d'Information  [PMSI] ), (3) the national death registry (the Center d'Epidémiologie sur les causes médicales de Décès [CépiDc]), and (4) medicosocial data (the Caisse Nationale de Solidarité pour l'Autonomie [CNSA])  [13] [14] [15].
The SNDS covers almost 99% of the French population, making it 1 of the largest databases of continuous homogeneous claims in the world  [14]. Between 2007 and 2016, more than 400 scientific publications based on national health insurance data were identified in Medline, mainly on the real-life evaluation of drugs  [13]. The consumption of reimbursed health products (ie, drugs and medical devices) for outpatients is recorded in the SNDS but not treatments used in hospitalized patients (except for expensive reimbursed drugs). Moreover, limited clinical data (no diagnosis, except in the case of hospitalization or chronic disease), no biological test results, scarce sociodemographic data, and no information about occupation  [14, 16]  highlighted the potential for further linkage with other databases.
Clinical cohorts, disease-specific or population-based registries, and hospital data warehouses represent other databases of interest for data linkage in which reimbursed or nonreimbursed health products used in real life can be recorded, often in a heterogeneous way, depending on the initial purpose of the database. All these databases represent important tools for providing information about the safety and benefits of approved health products, with data on real-life use, rare outcomes, and long-term effects that were undetectable in randomized controlled trials  [17]. Registries are particularly interesting for rare diseases, with more than 600 registries in Europe, for example, allowing development of clinical research in the field of rare diseases and so patient care improvement  [18].
As these databases potentially represent different points in patient management, linking them can also make possible the reconstruction of care trajectories to better understand therapeutic management throughout the patient's life. Data linkage involves being able to match patient data, sometimes anonymized, between databases. The use of a common unique identifier to directly link personal data is often limited because it does not exist, is not regulatory available, or contains errors  [19]. The national individual identifier "NIR" in French MADs is highly protected by privacy rules, leading to complex and lengthy formalities, which discourage potential applicants from using it  [13, 14, 20, 21]. Linkage with a set of indirect key identifiers has therefore been developed with deterministic and probabilistic approaches  [2, 4, 22].
Search Strategy A comprehensive search was performed by 2 authors (FR and SC) according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Multimedia Appendix 1)  [24]  for all papers published up to December 31, 2022, in 2 databases (PubMed/Medline and Embase databases). The search was conducted using a combination of various keywords from 3 categories. The first category included keywords related to different databases potentially used: SNDS, SNIIRAM, PMSI, CépiDc, electronic health record (EHR; Medical Subject Headings [MeSH] term), clinical data warehouse, claims database, registry (MeSH term), cohorts, and analyses (MeSH term). The second category of keywords was related to linkage: data linkage (MeSH term) and medical record linkage (MeSH term).
Study Selection To be eligible for inclusion, studies had to be published in English or French, be human studies, be peer-reviewed papers, focus on health products (drugs or medical devices) or care trajectories, and involve the use of linked French data defined as the linking of 2 or more French data collections at the patient level (Table  1 ). Only studies based on the use of indirect identifiers were included (ie, without a unique personal identifier available to easily link the databases-the NIR for French databases). Studies on data linkage between the French MADs making up the SNDS were not identified (eg, between SNIIRAM and the PMSI). A manual review of the reference lists of all selected papers was performed to identify any other relevant studies. A full-text search was performed when it was not possible to determine from the abstract whether linked data were used.
Data Extraction and Analysis The full text of all the eligible papers was screened, and the following criteria were collected: date of publication, research area (type of health products, care trajectories), type of database linked (MAD, registry [set of disease events occurring in a well-characterized population over a given period of time]  [25] , cohort [group of individuals sharing a certain number of features and who are followed longitudinally, at the individual level, according to a pre-established protocol]  [26] , other databases), number of patients in the different databases, and linkage method (deterministic, probabilistic, or alternative approaches)  [27]. Data linkage quality was assessed using analysis quality indicators and adherence to the Bohensky framework for evaluating data linkage studies  [4]  on selected studies published since 2011 (ie, the date of publication of this guide).
Literature Search Findings The systematic literature search identified 1155 papers. Screening against the inclusion and exclusion criteria retrieved 39 (3. 4%) papers using health database linkage, of which 16 (41. 0%) focused on health care product use and care trajectories for the final analysis (Figure  1 ). Most papers were recent, with over 87% (n=14) of these papers published since 2017 (Multimedia Appendix 2  [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] ). In 1 (6. 3%) case included in this review, the publication corresponded to a study protocol in which the data linkage was planned with varying degrees of accuracy  [28]. Data linkage was performed at the national level (ie, for all patients with a disease or who received therapy in France) in 7 (43.
8%) cases  [27] [28] [29] [30] [31] [32] [33]  or at the local level in 9 (56. 2%) studies  [34] [35] [36] [37] [38] [39] [40] [41] [42]. The number of patients included in the different databases (medicoadministrative, registries, cohorts, etc) varied greatly, ranging from 713 patients  [34]  to 75,000 patients  [39, 41]. The number of patients resulting from data linkage was also variable, ranging from 210 patients  [34]  to 31,000 linked patients  [27].