dataesr/scientific-paragraphs-categorization huggingface dataset:
These issues directly affect the DDD/100 occupied beds indicator calculated for each drug. This study has several limitations. First, as the dataset did not include the hospital level of care (tertiary or secondary) and the source of funding (public or private), it was not possible to assess whether some hospitals had more urgent issues with 57 604 for vancomycin in 2018, or 9 752. 5 for piperacillin/tazobactam and 16 458 for ceftriaxone in the same year (Table  5 ). Variable reporting rates also influenced DDD/100 occupied beds values. Monthly median and interquartile ranges for antibiotic consumption remained unchanged for 2018 and 2019 for ICUs, and for 2019 and 2020 for non-ICU wards (Table  5 ).
Furthermore, data reporting could be made mandatory for secondary level hospitals. It would also be useful to collect data on a few additional variables at service/ward level, such as the number of patients attended and the sources of financing. Finally, knowing the total number of patients treated in ICU and non-ICU services in the hospitals that report to SIVIGILA would allow a comparative analysis of national and hospital consumption. In conclusion, the progress in the number of ICU and non-ICU services in the country providing the data required by the hospital antibiotic consumption surveillance program is noteworthy. However, the quality of DDD data is sensitive to external factors such as the COVID-19 pandemic in 2020, changes in antibiotic prescription practices, and the accuracy of data-entry work.
This highlights the critical need to strengthen the SIVIGILA system for antibiotic consumption, to guarantee data quality, to improve data completeness, and to support hospital staff. In this regard, there is room for improvement at the interface and the issues directly affect the DDD/100 occupied beds indicator calculated for each drug. Author contributions. ML, AM, YCB, FP, and KV contributed to the conception and design of the study. ML and AM collected the data. ML, KV, DN, and PT did the data analysis and interpretation. ML and YCB drafted the manuscript. AM, PT, DN, KV, and FP contributed to the critical review and revision of the manuscript. All authors reviewed and approved the final version. Another limitation of this study is related to the health emergency caused by COVID-19 in 2020.
Acknowledgment Variations in antibiotic consumption (DDD/100 occupied beds per day) are sensitive to hospital reporting performance. According to the INS annual report for 2020, while the number of reporting hospitals increased, the total number of beds was considerably lower, especially for non-ICU (13% of total beds reported during 2018 and 2019, as shown in Table  1 ). Of the ICU beds, only 61% were notified in 2020, compared to the number of beds included in the previous annual reports  (17). The 2020 underreporting is even greater considering that in 2020 adult ICU bed capacity increased from 5 346 to 6 149 beds nationwide  (18). This finding could be related to the overwork due to the COVID-19 pandemic in 2020. Regarding the consumption trends observed between 2018 and 2020, the variations in the consumption of piperacillin/ tazobactam, ertapenem, ceftriaxone, and cefepime from 2019 to 2020 could be related to an increase in prescriptions for patients with moderate or severe symptoms related to secondary infection and coinfections in response to SARS-CoV-2 virus, in ICU mainly and non-ICU services  (19).
6 d 4 564 80. 0 d Number of beds reported 34 201 NA 33 090 NA 4 551 NA NA, not applicable; (…), no data available. Notes: Rev Panam Salud Publica 47, 2023 | www. paho. org/journal | https://doi. org/10. 26633/RPSP. 2023. 63 a % calculated with total number of units/wards as denominator. b % calculated with total number of units/wards reporting at least once in the year as denominator. c Number of expected reports = Number of units/wards reporting at least once in each year * 12. d % calculated with number of expected reports as denominator. e Non-ICU: Includes all other hospital wards except for ICU and emergency. Source: Prepared by the authors based on the study results.
0 0-14. 5 0. 0 0. 0-0. 0 0-51. 6 0. 0 0. 0-0. 0 0-135. 5 Non-ICU … … … 0. 0 0. 0-0. 4 0-128. 5 0. 0 0. 0-0. 6 0-137. 1 Meropenem ICU 23. 4 11. 6-38. 3 0-28 167 15. 2 7. 4-25. 7 0-147. 6 15. 3 7. 2-26. 4 0-198. 2 Non-ICU … … … 2. 7 0. 3-5. 7 0-149. 9 2. 8 0. 4-5. 9 0-147. 7 Ciprofloxacin ICU … … … 0. 0 0. 0-0. 0 0-8. 8 … … … Non-ICU … … … 1. 6 0. 2-5. 1 0-319. 3 1. 2 0. 1-4. 2 0-206. 9 Vancomycin ICU 9. 8 5. 1-16. 8 0-57 604 9. 4 4. 6-16. 2 0-148. 9 9. 2 4. 4-16. 7 0-148. 8 Non-ICU … … … 2. 1 0. 2-4. 4 0-134. 4 2. 2 0. 3-4. 7 0-147. 4 DDD, defined daily dose; ICU, intensive care unit; (…), no data available. Source: Prepared by the authors based on the study results. Rev Panam Salud Publica 47, 2023 | www. paho. org/journal | https://doi. org/10. 26633/RPSP. 2023. 63.
2015; Strassburg et al. 2020; Ladouceur et al. 2022). However, unlike protected area design, little progress has been made to provide decision support tools to help plan restoration efforts. Although conservation planning decision support tools have been applied to ecological restoration, they were not designed for this purpose and might fail to achieve important objectives for ecological restoration planning. For example, decision support tools designed to inform protected areas establishments-such as Marxan  (Ball et al. 2009) , Zonation  (Moilanen et al. 2009) , or the prioritizr R package  (Hanson et al. 2022 )-are not well suited for restoration objectives, such as improving overall habitat quality, reducing habitat fragmentation, or restoring ecological connectivity. Indeed, these tools are primarily focused on ensuring adequate representation of species and ecosystems within protected area networks  (Margules & Pressey 2000).
Package Overview The restoptr R package provides an expressive, flexible, and extensible framework to address a wide range of ecological restoration planning problems. To achieve this, the package generalizes and extends the methodology originally proposed in  Justeau-Allaire et al. (2021). Specifically, it provides an automated preprocessing toolchain to prepare input data for optimization, and it also provides a user-friendly application programming interface (API) which is extensively documented with many examples available online (https://dimitri-justeau. github. io/restoptr/). After the user has supplied geospatial land cover data and specified a problem formulation, the package can identify priority areas for restoration. These priority areas satisfy constraints and are guaranteed to be optimal (or nearoptimal) for the optimization objective.
Several parameters are available to configure the solving process, such as the number of alternative solutions that must be identified, the solving time limit, and the optimality gap (allowed distance to the optimal value). For performances reasons, the solving process is delegated to restopt, a dedicated Java library which relies upon the Choco CP solver (Prud'homme & Fages 2022). The communication between R and Java is handled by the rJava R package  (Urbanek 2021). The workflow implemented by restoptr is illustrated in Figure  1.
Automated Data Preprocessing Toolchain Preparing geospatial data for spatial combinatorial optimization problems, such as restoration planning ones, is prone to errors. Indeed, when several datasets are used, it is necessary to ensure that they have the same geographic coordinate system and the same resolution. In particular, since the solving time of such problems is highly dependent on spatial resolution, it is often necessary to resample the datasets to obtain a resolution that offers a compromise between accuracy and performance. To support users in this process, we implemented an automated data preprocessing toolchain that substantially reduces the risk of errors in data preparation, but also makes the entire processing chain more flexible and easier to use. Based on the terra R package  (Hijmans et al.
Restoptr R API in a Nutshell We designed the API of the restoptr R package based on three key principles: ease of use, flexibility, and extensibility. To initialize a new problem, the restopt_problem() R function is used with the following inputs: (i) a habitat raster (in the form of a terra raster object), (ii) an aggregation factor, and (iii) a habitat threshold (as described earlier). For example, with a terra raster object named habitat, an aggregation factor of 2, and a habitat threshold of 0. 75, a restopt_problem() object can be created as follows: > problem <-restoptr_problem (habitat, 2, 0. 75) This problem object contains all the preprocessed data. If needed, this data can be accessed from the object. For example, the restorable area data can be plotted using the following command:.
> plot(get_restorable_area(problem)) This problem can then be tailored by adding constraints and an optimization objective. To provide a concise API, the pipe operator %>% of the magrittr R package can be used  (Bache et al. 2022). For example, adding a connectivity constraint can be added as follows: > problem <-problem %>% add_connected_constraint() If for certain reasons some areas are not available for restoration (e. g. private land), it is possible to use the add_locked_ out() function with geospatial (raster or vector) data indicating these forbidden areas: > problem <-problem %>% add_locked_out_constraint(locked_out) A similar procedure is used to specify the optimization objective. For example, to specify the maximization of the effective mesh size  (Jaeger 2000)  as the optimization objective: > problem <-problem %>% set_max_mesh() The optimization process can also be customized with settings.
It is possible to configure the number of solutions to output, the time limit, or an optimality gap. For example, the following code can be used to specify that, if possible, 50 solutions should be obtained: > problem <-problem %>% add_settings (nb_solutions = 50) Finally, once the problem is well defined, the solve() function can be used to start the solver and retrieve solutions: > solutions <-solve(problem) Table  1. Functions available in the restoptr package for specifying constraints and objectives. These functions can be combined to identify priority areas that meet particular criteria or optimize a particular metric.
Solving Procedure Once a restoration planning problem has been described, the solve() function relies on the rJava R package  (Urbanek 2021)  to translate the restoptr_problem() object into a constrained optimization problem using the restopt Java library (Justeau-Allaire 2022). This library is dedicated to the solving procedure and relies on Choco-solver, a state-of-the-art CP library (Prud'homme & Fages 2022). When the solving process is complete, the solutions are sent back to the R session, and restoptr converts them into raster, including metadata about the solutions' quality and characteristics. Compared with the original approach, we substantially improved the solving performances and memory footprint by rewriting the model and relying on global domain views, a new CP technique well suited to restoration planning problems that rely on set and graph variables (Justeau-Allaire & Prud'homme 2022).
Case Study In New Caledonia, many vegetation areas are threatened by mining activity, which is the pillar of the archipelago's economy. However, mining companies have a legal obligation to invest in the ecological restoration of their operating areas. To ensure that this obligation is efficiently fulfilled, it is essential to inform both local authorities and mining companies of ecological restoration plans that are both cost-efficient and ecologically relevant. To help achieve this, historical landscape data can play an important role to assess habitat loss and changes in landscape patterns. In particular, such data can be used to define relevant ecological restoration targets based on the past state of a landscape. In this case study, we showcase the usage of the restoptr R package by identifying reforestation plans in a mining area located in the north of the main island of New Caledonia: the Mount Kaala area (see Supplement S1 for R source code).
Discussion The restoptr R package is a free and open-source decision support tool that takes advantage of CP to provide a generic framework for ecological restoration planning. It complements the existing conservation planning tools and opens perspectives include ecological restoration in land-use planning. Designed for ease of use and flexibility, restoptr is extensively documented and provides a broad catalogue of constraints and optimization objectives that can be composed to formulate a wide range of ecological restoration planning problems. This catalogue includes advanced landscape indices for optimizing ecological connectivity, and constraints to explicitly account for socioeconomic factors. In the case study, we demonstrated how restoptr can be used with historical data to set relevant targets over landscape indices.
Acknowledgments This work was funded by the  ADMIRE  project ( IAC, Cirad, North Province of New Caledonia ), and the  RELIQUES  project ( IAC, IRD, Cirad, CNRT ). The authors thank the members of the  ERMINES  project and the  AMAP lab team in Noumea  for sharing their knowledge about mining areas in New Caledonia, and  Mégane Paul  for the dig-.
This approach is based on Analytic Hierarchy Process (AHP), and a set of user-ranked patterns to build a preference matrix, which compares the importance of measures according to the user-specific interestingness. A sensitivity based heuristic is proposed for the active learning mode, in order to insure high quality results with few user ranking queries. Experiments conducted on well-known datasets show that our approach significantly reduces the running time and returns precise pattern ranking, while being robust to user-error compared with state-of-the-art approaches.
• A new generic version of the learning algorithm to learn both in active and passive modes. • Proposition of a sensitivity based heuristic for selecting patterns when learning in the active mode. • Implementing three different user simulations. • Validation of the whole approach with its various settings (i. e. , passive and active modes, user simulations, etc. ) on large and new datasets. • Showing the robustness of the approach in an interactive learning process, considering user mistakes and user changing its ranking criterion. To assess the interest of our approach, we consider the association rules mining problem as a case study. Experiments conducted on a wide range datasets show that our approach significantly reduces the running time, while ensuring accurate pattern ranking compared to the state-of-the-art approaches.
Pattern mining and interestingness measures Data can be abstracted as a triplet (O, A, L) in which O is a set of objects, A is a set of attributes and L is the language used to express objects on attributes. This framework is inspired from inductive databases concepts  [15, 16]. Following the language L, for instance, we can get classical datasets: (1) when L is a binary relation between attributes and objects (i. e. , L ⊆ O × A), we get the classical itemset mining framework; (2) when L expresses each object as a sequence of attributes, we get a sequential dataset; (3) when L expresses each object as a graph where nodes and vertices are labeled, we get a dataset of graphs. Data mining tasks aim at discovering interesting regularities, namely patterns, between objects in large-scale datasets.
Running Example To illustrate our approach, we consider an initial set of patterns P = {P 1 ,. , P 10 }, five interestingness measures M = {M 1 ,. , M 5 } and a user-ranking r u. Columns of Table  1  show the user ranking for P, the rankings given by M i on P and the AHPRank results. It is important to stress that no measure perfectly matches the user ranking. The data in Table  1  is used by our algorithm to build the AHP Matrix and to learn a weight vector w over M. For the sake of simplicity, let us call AHPRank in the passive mode with S = {S a = P 3 , P 1 , P 5 , P 2 , P 4 }. Here, the user prefers P 3 u P 1 u P 5 u P 2 u P 4. AHPRank learns weights by calling learnWeights on S a and by computing the correlation between the user rankings of S a and the ones of the measures M i. The matrix provided in Equation  (7)   values of each measure pairs.
Mining associations rules (ARs) We evaluate our generic approach on a concrete pattern mining task, the association rule mining, one of the most important and well studied task in data mining, first introduced in  [30]. An association rule is an implication of the form X → Y , where X and Y are itemsets such that X ∩ Y = ∅ and Y = ∅. X represents the body of the rule and Y represents its head. The frequency of an itemset X in a dataset, denoted by f req(X), is the number of transaction of the dataset containing X. The frequency of a rule X → Y is the frequency of the itemset X ∪ Y , that is, f req(X → Y ) = f req(X ∪ Y ). Several interestingness measures of ARs have been introduced like support, confidence, interest factor, correlation and entropy. Tan et al. [31]  made an interesting study on the usefulness of the existing measures related to the application type.
χ 2 is a rather complex function to approximate with non-trivial correlations, it is also a quality measure suggested in  [5]. Here, we use χ 2 as the target user-specific ranking function over ARs. Given some association rule X → Y , the χ 2 value is as follows: χ 2 (X → Y ) = (f req(X → Y ) -f req(X)f req(Y ) N ) 2 f req(X)f req(Y ) N. ( 9 ) with N the number of transactions of the dataset.
Implementation settings We have implemented in Java our AHPRank approach with its two modes: the passive mode denoted by AHPRank. 0 and the active mode denoted by AHPRank. 1. The code is publicly available at github. com/lirmm/AHPRank. We compared our approach to the state of the art, the SVM based approach RankingSVM  [5]. We denote by RankingSVM. 0 the passive version and by RankingSVM. 1 the active one following  [3]. All experiments were conducted on an Intel core i7, 2. 4Ghz with 16Gb of RAM using an overall timeout of one hour.
Passive learning results In this section, we address the two first research questions (RQ1 and RQ2). To that end, we perform a 5-folds crossvalidation on #Rules for each dataset. In each fold, we randomly select 20% of #Rules to form the training data and use the remaining 80% ARs for evaluation (testing data). We have chosen this way of using k-folds cross-validation in order to see how effective are the approaches in learning from relatively small training sets. 4 : Correlation results with user ranking for using the emulators. A) Analysing the different user feedback emulators. Table  4  reports the rank correlation ρ between the user-specific ranking functions (RAND-EMU, LEX-EMU and CHI-EMU) and the seven interestingness measures. We also report VBM, the Virtual Best Measure, which returns the best rank correlation ρ provided by one of the seven measures.
We observe from table 4 that given a measure, the results are chaotic. For instance, Lambda measure is highly correlated to CHI-EMU on T40 dataset (ρ = 98%) but it is weakly correlated on Connect (ρ = 29%) dataset. The same observation can be made with RAND-EMU and LEX-EMU functions. However, the theoretical construction of VBM is of a high accuracy (mean of 91%), which brings us to believe that a weighted aggregation of the selected measures can lead to a good trade-off. B) Comparing AHPRank. 0 with RankingSVM. 0. Table  5  is dedicated to the k-folds cross-validation results of RankingSVM. 0 and AHPRank. 0 using averaged rank correlation ρ, recall values (R@10% and R@1%) and CPU time in seconds averaged over the folds. Under a time contrast of one hour, RankingSVM. 0 is able to deal with a training set not exceeding 100K rules, and thus we are able to compare it with our approach only on Hepatitis.
In terms of ranking accuracy, we observe that RankingSVM. 0 outperforms AHPRank. 0 for all user feedback emulators. However, AHPRank. 0 remains competitive with an acceptable accuracy. Same observations can be made in terms of recall at the 10% and 1% top of the ranking (R@10% and R@1%). AHPRank. 0 is able to reach a high correlation with the user ranking functions on most of the datasets. However, we can also observe a weak correlation with CHI-EMU on Connect, where a high correlation is observed on that dataset with RAND-EMU and LEX-EMU. This stems from the fact that RAND-EMU and LEX-EMU are linear functions expressed with the given 7 interestingness measures, which explains the high accuracy. However, CHI-EMU is a complex function and the statistics extracted from Connect on the 7 measures are not sufficient to learn such a function.
B) Comparing AHPRank. 1 with RankingSVM. 1. Table  7  shows a comparison between RankingSVM. 1 and AHPRank. 1 on the 5 datasets. We report ρ, R@10% and R@1% within stopping criterion T at 10, 50 and 100 queries. We also report the averaged latency time between two queries Time for RankingSVM. 1, knowing that Time never exceeds the latency bound of 0. 1 seconds. The findings are the same for RAND-EMU, LEX-EMU and CHI-EMU functions. The main observation that we can draw from table 7 is that AHPRank. 1 outperforms RankingSVM. 1. Let us take a close look at Hepatitis results. For this dataset, AHPRank. 1 is able to rank 500K patterns with an accuracy of 78% using only 10 queries (73% observed with RankingSVM. 1). The accuracy increases to 95% when learning on 50 queries for AHPRank. 1 and 94% for RankingSVM.
1. AHPRank. 1 reaches 98% of accuracy with 100 queries while RankingSVM. 1 remains stable at 94%. In terms of recall values, AHPRank. 1 is able to discover the most relevant patterns on the first 50K and 5K patterns (out of 500K) with an accuracy of, respectively, 78% and 66% over 10 queries (61% and 44% for RankingSVM. 1). Let us now take a close look at Retail dataset. We observe that AHPRank. 1 reaches a high accuracy over 10 queries and such accuracy remains stable over the following 90 queries. The RankingSVM. 1 results are less impressive on Retail and particularly on the recall metric. But the main difference is on the waiting time Time , where RankingSVM. 1 can keep the user waiting more than 10 seconds between two queries. Same observation can be made on the other datasets where RankingSVM.
The reported results are averaged on 10 runs and on the whole set of datasets on learning CHI-EMU and by submitting 20 queries to the user. We observe that AHPRank. 1 is quite stable and robust even with 40% of mistakes (8 mistakes out of 20). The overall correlation between the learned function and the user one ρ is at 77% without mistakes and becomes 70% under Err= 40%. However, RankingSVM. 1 is stable till Err= 20% and then, the accuracy is highly impacted where ρ decreases from 71% to 54%. In terms of recall, we observe a decline not exceeding 2% at R@10% and 8% at R@1% under AHPRank. 1. Using RankingSVM. 1, the decline is exceeding 10% at R@10% and R@1%. In terms of CPU time and between two queries, we observe a waiting time ranged between 6 and 12 seconds under RankingSVM. 1, where under our approach is never over 0.
Note that with x = 0, LEX2RAND(0) is equivalent to RAND-EMU. results of 10 runs on all datasets over 20 queries. The main observation that we can draw is that AHPRank. 1 is stable in front of a changing linear function to learn even with a changeable mind in the middle of the learning (10 out of 20 queries). However, a target alteration during the learning process can drastically impacts the accuracy of RankingSVM. 1 (decline of 44% in terms of ρ). In terms of CPU time, our approach is 50 times faster than RankingSVM. 1. To sum up, our main finding is that AHPRank represents a good trade-off between ranking accuracy and running time. AHPRank is able to reach a good overall ranking accuracy within a time being linear on the size of the training data and thus, not exceeding few seconds, which makes it suitable for industrial exploitation.