dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , managing the capacity of the hospitals, and a challenging balance between the two necessities needed to be pursued  (Yang et al. , 2021). This paper presents a hybrid method that combines AI techniques, particularly a Random Forest method, with the Discrete Event Simulation (DES) methods. We aim to predict the demand for intensive beds by modelling the flow of the patients affected by Covid-19 within ICU departments and improving several intervention targets such as waiting time for a bed. First, we consider which predictors should be adopted to estimate the likelihood of ICU admission, adopting a statistical approach to test their significance. This is essential for ensuring that the framework can get results as close as possible to reality  (Vickers et al. , 2011). Once the choice of the predictors has been confirmed and a dataset of data for patients has been coherently constructed thanks to an appropriate preprocessing phase, a Random Forest model, recognised as one of the most accurate AI techniques for predictions, allows estimating the probability of the patients to be admitted to the ICU.
Those probability values are the input of the DES model that simulates the inflow of patients in the ICU and their potential of becoming critical (importance of doing that). Finally, statistical validation tests are conducted to verify the validity of the DES model proposed. The combination of those methods has the intention of reaching the following aims: • The accurate selection of the indicators and the test of their statistical significance allows to robustly base the framework and to overcome difficulties from missing data or analyzing indicators that are not relevant  ( Štěpánková et al. , 2003). • The use of the RF method permits accurately predicting the likelihood of developing severe disease and it has been selected for its beneficial aspects outperforming other AI techniques. The RF can deal with large databases and detect complex non-linear relationships in addition to the interaction between the features  (Simsekler et al.
It has been adopted in a variety of contexts (for a review see e. g. Belgiu & Drȃgut ¸, 2016). In the healthcare contexts, it has been applied to predict the occurrence of disease  (Khalilia et al. , 2011)  and it is appreciated for the speed to handle such problems  (Fawagreh & Gaber, 2020)  and the possibility of dealing with incomplete databases  (Khalilia et al. , 2011)  which are expected in a pandemic scenario. In the management of the supply chain context, RF has also been employed in several fields including forecasting products' backorder  (Islam & Amin, 2020). One of the fast-growing trends  (Ordu et al. , 2021)  is represented by the possibility of combining hybrid models that integrate several techniques such as AI and DES embedded in a proper framework. For instance, DES methods have also been integrated with other methodologies.
Predicting intensive beds occupancy from patients affected by Covid-19 Among the issues related to the definition of patients' demand, we find the prediction of the patient's outcome once they are admitted to the hospital. Indeed, identifying such patterns could help decisionmakers to deal with the suddenly increased number of patients  (Heldt et al. , 2021). In some cases, the authors collected historical data on patients to analyse the most common characteristics that guide them to a critical illness or a lengthy stay in hospital. For example,  Petrilli et al. (2020)  identified the most common factors in a group of >5000 patients. In addition to that, studies were also focusing on the main characteristics of the population affected and on their geographical location  (Di Castelnuovo et al.
, 2020). Within this aim, several machine learning techniques have been adopted and compared to predict which clinical factors, parameters, and characteristics of the patients can suggest a severe course for the patients  (Alakus and Turkoglu, 2020). For example,  Pourhomayoun and Shakibi (2021)  developed several different types of machine learning methods including Support Vector Machine, Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbour to determine the health risk and predict the mortality risk of patients with COVID-19 with a quite consistent database of patients' data collected worldwide. In this regard, the decision of which indicator to adopt is quite diversified. For instance,  Sun et al. (2020)  use the support machine vector method to analyse >200 potential indicators on >300 cases to identify 36 significant factors, while  Mauer et al.
Conceptual framework Conceptually, what is being recommended is that AI and DES may operate as the pillars underpinning a decision-making framework that allows ICUs to respond quickly and effectively against the expected volume of Covid-19 admissions from the downstream services. Fig. 1  depicts the conceptual model suggested for this aim but with a special focus on bed inventory planning management. This framework starts with efficient database management where medical records, process variables, and demand data can be stored under high-quality standards, thereby supporting the informative nature of the hospital data administration systems. Extracting these data will propel the development of AI solutions capable of predicting the expected volume of ICU admissions within the next few hours after ED arrival.
The general procedure is described below (Fig. 2 ): Step 1. Identification of potential ICU admission predictors in Covid-19 patients: A list of features probably influencing the probability of ICU admission is established considering the pertinent scientific literature and doctors' experience during the struggle against Covid-19. The significant variables will be further used to predict the probability of ICU admission after a few hours of ED arrival. Step 2. Dataset construction: The dataset is built by extracting highquality and appropriate patient data associated with the previously identified features. These data are usually reported and stored in information systems according to predefined data management protocols. Nevertheless, the healthcare database continues to evidence incomplete and missing records  (Bihri et al.
, 2022; Iwendi et al. , 2020). The margin function (mg(X, Y) ) for each decision tree is expressed in Eq. (1): mg(X, Y) = av k I(h k (X) = Y ) -max j∕ =Y av k I(h k (X) = j) (1) The generalization error is given by Eq. (  2 ): PE * = P X,Y (mg(X, Y) < 0) (2) It is known that the number of decision trees increases for all tree sequences considering that RF h k (X) = h(X, Θ k ). Therefore, followed by the strong law of large numbers and the tree structure, the prediction accuracy for each tree is given by Eq. (  3 ) whose result explains why RF does not overfit as more trees are added  (Breiman, 2001) : P (X,Y) (P θ (h(X -θ) = Y ) -max j∕ =Y P θ (h(X -θ) = j )〈0) (3) RF has been proven to outperform other methods regarding prediction accuracy while providing an autonomous representation of interactions in large datasets  (Patel et al.
Therefore, part of the model uncertainty derived from this application will be intrinsically linked to the insights provided by the RF model (likelihood of ICU admission). Following this, input data analysis (randomness, homogeneity, and goodness-of-fit) will be performed on the process variables identified in the previous phase. o Randomness: Run tests (α = 0. 01) will be carried out to verify whether the variable is independent. If the resulting p-value is higher than the significance level α, the variable is then concluded to be randomly distributed. Otherwise, it is identified to be dependent on other factors. The resulting p-value is based on K which is the comparison reference score helping to elucidate the observed number of auto-correlations. o Homogeneity: ANOVA tests (α = 0. 01) will be executed to detect potential subgroups of data within the variable dataset.
In case of different probability expressions capable of describing the variable, the modelers should select the one with the major p-value. (iii). DES Development: In a healthcare context, the use of DES is appropriate for discovering a wide range of operational problems such as bottlenecks and inefficient use of resources  (Casier et al. , 2015; Vázquez-Serrano et al. , 2021). Besides, it allows contrasting different scenarios without a real implementation (What-If analysis), therefore making it possible to explore their consequences in the real-world system  (Amantea et al. , 2020; Barrios et al. , 2015; Castanheira-Pinto et al. , 2021; Garcia-Vicuña et al. , 2021). In this step, the design, development, and simulation of the DES model are carried out through a simulator (e. g. Arena Rockwell Software) which animates the Covid-19 patient journey within the hospital, thereby facilitating engagement with the different stakeholders and underpinning the model validation.
Study cohort and data extraction The initial dataset, constructed under the "COVID DATA SAVE LIVES" project, contains the anonymized healthcare records of 4479 patients between the ages of 1 and 106 years. The use of these data counts on the formal approval by the Ethical Research Committee of the Spanish hospital chain. Specifically, the individual patient data was gleaned from the electronic hospital health records and included information on diagnosis, ICU admissions, lab results, treatments, vital signs, and type of discharge. These patients were admitted to the emergency departments of the hospital group between 5 February 2020 and 13 February 2021. The Covid-19 diagnosis was reached considering the prior assessment by a specialist and/or clinical findings of a Reverse Transcription Polymerase Chain Reaction (RT-PCR) test.
Sampling and labeling The final sample selected for the prediction model was reduced to 1049 patients since the presence of incongruent data in some of the features. In this case, missing information for each potential predictor was imputed by using the median value across the levels of the response variable in the cohort  (Batista & Monard, 2003; Cheng, Joshi, et al. , 2020)  In some features (Systolic blood pressure, Diastolic blood pressure, Heart rate, Body temperature and Blood oxygen saturation), two measures were gathered at different instances to evidence the evolution of the patient along the time (time-series data). The primary outcome of this intervention was the likelihood of ICU transfer. In this case, the labeling was the following: indicate with "1 ′′ if the Covid-19 patient was transferred to the ICU; otherwise "0".
The Random Forest model: Training, testing, and validation The cohort was randomly divided into training and test subsets according to a 70/30 proportion. The ICU transfer ratio was 8. 89 % which generated an imbalance between the positive class (patients who were not transferred to the ICUs) and the negative label (patients who were moved to the ICU). We then undertook random undersampling on the training dataset to balance both classes and avoid bias in the prediction model  (Hsu et al. , 2015; More & Rana, 2017 ; Sawangarreerak & Thanathamathee, 2020) (Fig. 6 ). We employed the Rstudio software version 4. 1. 2 to apply RF and reach the prediction model. Following this, we carried out a 10-fold cross-validation to adjust the hyperparameters and employed these libraries: epiR (v. 2. 0. 44), Caret (v.
DES development and validation After completing the input-data analysis depicted in the previous sub-section, we proceeded with creating a virtual model of the healthcare system using Arena® 16. 10. 00 software. The Covid-19 patient journey described in Fig. 8  and the mathematical expressions obtained through the Goodness-of-fit tests were included in this representation. As all the healthcare units involved in this pathway operate constantly, the length of replication was defined to be 15 days with 24 h per simulated day. Also, a warm-up time length of 100 days was established considering an approximate blocking probability of 0 which denotes a stable state of the computational model. Following this, the simulated system was initially iterated 10 times to estimate the final number of replications that will be taken into account in the validation stage.
The waiting times in EDs for I-II/III-V triage levels as well as the waiting time in ICU were measured in each iteration. In this case, 2,183 replications were deemed necessary to fully mimic the current variation of the real Covid-19 healthcare system. Lately, the equivalence hypothesis was assessed by a 1-sample sign test (α = 0. 05) given the asymmetric distribution of the data in each indicator (p-value < 0. 005; AD > 4.
First, the RF model may be restricted to the Spanish healthcare system given the considerable differences that may exist among the countries regarding their healthcare structure. Second, confounding factors depending on each patient profile were not explored and remain an open challenge to tackle in future studies. Furthermore, the effectiveness of treatments provided in ED wards and hospitalization units was not deemed. Also, it is good to mention that the database used in this study only contained lab test results, prescribed treatments, sociodemographic features, and vital signs measurements collected in the emergency department. In this regard, a limitation is the lack of data related to the past medical history of Covid-19 patients which may increase the performance of the RF model. Finally, the RF algorithm did not study other blood indicators highlighted as good biomarkers of ICU admission due to a lack of high-quality data.
On the other hand, the DES models are based on real high-quality process and demand data collected from the hospital chain database which increases its equivalence with the real ICU. In particular, the availability of demand data was identified as a limitation in  Wood et al. (2020)  where the authors had to employ projections from the UK government for modeling the number of admissions and expected times between arrivals. Likewise, the lack of cleansed data in the DES model provoked some validation inconsistencies in Le  Lay et al. (2020)  when comparing their virtual representation with the realworld healthcare system. Although important DES-based efforts have been reported in different studies  (Caro et al. , 2021; Irvine et al. , 2021; Melman et al. , 2021; Rees et al. , 2020; Wood et al.
This is even sharpening in presence of a rapidly evolving virus forcing the logistics operations to function more quickly, flexibly, coordinated, and integrated. AI-related applications have emerged as an alternative to tackle the above-mentioned inefficiencies by taking advantage of the large amounts of medical records stored in hospital databases. In this regard, data managers are expected to ensure the data's high quality and completeness. For instance, in our case study, some features (diuresis and glucose) could not be examined and further utilized due to registration errors. Likewise, it has been perceived that sensitive patient data are not gathered constantly over time thereby impeding the proper monitoring of patient's health and the creation of more accurate operational healthcare models.
Such forecasting allows anticipatedly administering the capacity of upstream services thus facilitating the creation of operational scenarios responding to the demand adequately. In this case, the AI model predicts how probable a Covid-19 patient is to be admitted to ICU during the next few hours. With this information, the ICU administrator can establish the expected transfers within a time frame and evaluate if the installed capacity is enough for providing in-time intensive care. The implementation of these data-driven applications should be user-friendly to diminish delays and interpretation failures. It is additionally important to consider features that can be gleaned quickly to decrease resistance to change during the introduction process of the AI models. This is the case of our RF model which considers sociodemographic (Sex and Age) and clinical indicators (Heart rate, diastolic and systolic blood pressures, body temperature, oxygen saturation, and D-dimer concentration) that are commonly measured in the ED settings and can be imported in real-time from the databases.
Acknowledgments The authors acknowledge the support of the  Academy of Finland  project '' Theory of computational logics' ', numbers  324435 ,  328987 ,  352419 ,  352420 ,  352419 ,  353027. Antti Kuusisto  also acknowledges the  Academy of Finland  project '' Explaining AI via Logic '', number  345612. Raine Rönnholm  ackhowledges a grant from the  Jenny and Antti Wihuri Foundation, Finland.
Materials and methods The two models used in this paper -AROPAj and NLU -rely on different modeling strategies (supply-side model vs partial equilibrium model) and use different datasets (see below). Each model has its strengths and weaknesses, AROPAj including a comprehensive description of farm types and crop choices and NLU providing a global perspective with price feedbacks and details on the nitrogen balance. Thus, comparing their results gives us a more comprehensive and robust picture of the consequences of a N-reducing policy. AROPAj is built on data from the Farm Accountancy Data Network (FADN) and models agricultural supply by (groups of) farmers, representative at the regional level 2. Each agent in the model maximizes its gross margin, the difference between production revenues and variable costs.
Data Collection The proposed hypotheses were tested using data collected from knowledge workers in Europe and Africa. This decision was based on a global analysis of ongoing discourses between professionals and academics  (Bieńkowska et al. , 2022; Kark et al. , 2021; Rimol, 2020) , which seem to suggest that the profound changes in work patterns leading to post-crisis phenomena like the great Resignation could vary from one economic context to the other depending on how affected they were by the crisis. Europe and Africa were chosen because they were statistically affected by COVID-19 very differently -Africa registered significantly fewer cases than Europe (Worldometer, 2021). Therefore, the impact of the pandemic on both continents may not have affected their remote work behaviours in the same way.
96%. Both results are less than the 50% threshold  (Podsakoff et al. , 2003) , indicating no risk of common method bias. The sample size was determined using the G*Power 3 software, which is a tool that helps to statistically compute appropriate sample sizes  (Faul et al. , 2007). With an effect size of 0. 40 and an error probability of 5%, this study's recommended calculated sample size was 144 participants per region. On this basis, data was collected from 250 knowledge workers from Europe and Africa each. Two hundred thirty-one valid responses were obtained from Africa (92. 4%) and 216 from Europe (86. 4%). Most participants from Europe and Africa were between 25 and 44 years of age. Most participants from both continents held a master's degree or equivalent certificate and had used digital platforms to perform some work-related tasks from their homes during the pandemic.
g. cardiology, oncology) -Ensure diverse representation of patients included in clinical trials -Consider ethnic and sex/gender aspects in clinical trials, drug metabolism, patient reported outcomes Pharmacological and medical technology sectors -Develop therapeutic options that are affordable for all -Develop and study approaches to prevent CKD or progression of CKD -Being transparent on investments in therapy development and real cost of therapies -Apply fair prices and fair profit principles -Register and distribute therapeutic products (e. g. peritoneal dialysis fluids) in all countries Governments, administrations, insurers -Acknowledge CKD as a public health problem -Collect reliable data on disease burden, costs and inequities related to CKD to support priority setting -Negotiate fair price setting of medications -Favour affordable therapies and therapies with high value-for-money, without further exacerbation of inequities -Aim for progressive expansion of universal health coverage -Generate a shift of paradigm from focus on cure towards focus on screening and prevention -Achieve harmonization among countries by support and exchange of information -Apply reimbursement models that disincentivize cherry picking -Stimulate and finance research on health inequities and health illiteracy.
Acknowledgements The  European Kidney Health Alliance  (EKHA) is a not-for-profit organization defending the case of the kidney patients and the nephrological community at the level of the European Commission. The EKHA network has five full members (the  European Renal Association , the  International Society of Nephrology , the  European Kidney Patients Federation , the  European Dialysis  and  Transplant Nurses Association-European Renal Care Association  and the  Dutch Kidney Foundation ) next to 27 National or Regional Societies as affiliated members. European Kidney Health Alliance  is the recipient of support by the  European Union  in the context of the  Annual Work Program 2022   on prevention of noncommunicable diseases of EU4Health, topic ID EU4H-2022- PJ02 , project #  101101220   PREVENTCKD  Disclosures: RV is advisor to  AstraZeneca ,  Glaxo Smith Kline ,  Fresenius Kabi ,  Novartis ,  Kibow ,  Baxter ,  Nipro ,  Fresenius Medical Care  and Nextkidney.
We find empirically that the real GDP multiplier averages 1. 4 during the first six years after the shock and importantly, 39% of the increase in real GDP is driven by TFP gains. Our estimates reveal that the aggregate multiplier is uniformly distributed across sectors, i. e. , in accordance with the sectoral value added share. The muted response of the value added share of non-tradables to the government spending shock at any horizon is puzzling since according to the data taken from the World Input-Output Database, government purchases are concentrated in non-traded industries. A rationale behind this finding lies in the technology channel of fiscal transmission. We find that technology improvements are concentrated in the traded sector which offset the impact of the biasedness of the government spending shock, thus leaving the value added share of non-tradables unchanged.
The change in the value added share of sector j is determined by the reallocation of productive resources across sectors and the TFP growth differential caused by a government spending shock, see Online Appendix B. 1 for a formal derivation. Using data from the World Input-Output Database (WIOD)  [2013] ,  [2016] , we constructed time series for sectoral government consumption and find empirically that the non-traded sector receives on average 80% of government consumption (see column 4 of Table  1 ). 4  As demonstrated in Online Appendix R. 1, see also  Cardi et al. [2020] ,  Proebsting [2022] , when the intensity of the non-traded sector in the government spending shock, denoted by ω G N , is higher than the share of non-tradables in GDP (which averages 64%, see column 1 of Table  1 ), the demand shock moves productive resources toward the non-traded sector, as long as technology is kept fixed.
Data Construction Before presenting evidence on fiscal transmission across sectors, we briefly discuss the dataset we use. We take data from EU KLEMS  [2011] ,  [2017]  and OECD  STAN [2011] ,  [2017]. Our sample contains annual observations and consists of a panel of 18 OECD countries. The period runs from 1970 to 2015. In Online Appendix D, we detail the source and the construction of time series for value added at constant prices, Y j it , hours worked, L j it , the hours worked share, ν L,j it , the value added share at constant prices, ν Y,j it , the labor income share, s j L,it , of sector j = H, N. Classification of industries as tradables or non-tradables. Since our primary objective is to quantify the role of the technology channel in determining the sectoral effects of a government spending shock, we describe below how we construct time series at a sectoral level.