dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the formula with the log form and the "runiform" command in it (b) lead to more statistically signi cant parameters than the rst formula (a) used with the lnormal Stata command. The formula avoids negative numbers in the random variates and a price equals to zero in the dataset (it helps with the "no drug" alternative)  (1). Additional series of random number functions may be needed in future steps of the model development, especially to investigate correlations issues between alternatives. Several random variables have been tested for prices since this economic model aims to adjust demand models with product prices, usually with data from the supply side. At this stage of the research, the two random prices represent general price parameters for a drug selection  [9]  for this type of Type II diabetic patients without complications; however the second random price variable also aims to capture the price differences paid between categories of insurance plans (these differences result to a large extent from differences in discount practices, in addition to variations in the supply chains and dispensing modes).
1. Economists usually need to use a log form in demand models of care, to incorporate very skewed distributions (long tailed), partly due to age distribution of various patient groups. Distributions of epidemiological data, used for instance in models for disease progression, are also not normal (usual distributions are for instance Weibull, etc. ). The power sample of this dataset is su cient to allow a comparison between public and private insurance: mainly between the federal public plan, Medicare and private plans' categories (the classi cation into insurance categories from the CDC was part of a detailed analysis in previous runs to estimate effects of various cost sharing categories). Therefore, at this stage of development, the model includes two random price variables: one random drug price for each alternative (source: Redbook source) one random interaction variable: drug price x Medicare (the control group is in this case the private insurance category, other models in the future may add other categories such as Medicare advantage, Medicaid, dual eligible etc).
Mixed logit models (e. g. run with Stata software) are very used in the USA, especially since the health law passed under the Obama administration, with the creation of information exchanges on insurance plans. They are mainly used for comparative analysis of health insurance plans, using case speci c variables for main types of health plans and random variables for plan characteristics such as deductibles, copayments types (e. g. tier copays) …. Such analysis helps to represent net price paid by patients or proxies for payment arrangements; they are often used by economists, to analyze the demand side of a medical market. However, in this paper, the random price variable in this economic model is for price adjustment on speci c medical markets ; the categorical variables for insurance in the CDC survey are used to control for major differences of drug prices per main types of insurance plans, especially public versus private.
This can help to examine whether there is a major difference in the coverage of patients under commercial plans and under Medicare, for age groups before 65 (especially between 55 and 65) and after 65. As this kind of modeling seems to be reliable in the statistical runs presented in this paper, it may be developed with additional types of insurance pro les and with more complex price indices for each plan category in further research. Main relevant price data providers have also been approached, a negotiation has started with Iqvia for special legacy of Pharmerit datasets (however, for the sampled case on diabetes, this dataset under-represents Medicare enrollees). Medicaid databases per state and IBM/Redbook were also consulted as possible sources. Additional sources used in main international price studies have also been reviewed and may be used for comparative analysis  [17] [18] [19] [20].
g. Monte Carlo simulation, cited in Stata Docs  [22]. However, only the Halton sequence could be included at this point: a user command of Stata was needed to input the parameters of the mixed logit models, for the implementation of the new speci cation tests, written in a Matlab code, for the application of this code on the diabetic dataset. Stata runs with this user command, were only available with an Halton sequence by default. Iii. Results Of The Simulation With A Mixed Logit Model On The Diabetic Dataset: The rst series of mixed logit models was tested on a drug choice set. The choice set includes three alternatives of treatments in primary care, for type II diabetes without complications (the selection of the three alternatives partly results from the power sample of medical records on Type II diabetic patient): Alternative 1 includes the new oral agents, the oral agent continued/old.
Alternative 2 includes combined drug therapies: insulin and oral agents, insulin and oral agent continued/old, insulin and oral new and continued/old. Alternative 3 is the "no drug" alternative. The sample for this real database on physicians' choices includes 645 medical records of diabetic type II patients (extracted from CDC NAMCS survey). Table  1  provides the descriptive statistics on the sample classi ed in the three alternatives A fourth alternative (injectable only) has been excluded of this analysis, with only 0. 84% of the sample. The mixed logit model was rst tested with Stata command "asmixlogit" on this diabetic dataset, after transformation into a log form format. Table  2  presents some results, from this rst analysis prepared for Ispor, New Orleans [8]. Table  2  The parameters on physicians 'choices, estimated on the real diabetic dataset, with the mixed logit model presented in Table  1 ,are used in the simulation approach presented in this paper ; the simulation is implemented in matlab code.
In order to use the new speci cation test proposed by Professors Hahn, Hausman and Lustig (2020)  [4] , the original code programmed by Dr Lustig had to be modi ed for the purpose of this speci c medical application. The Independence of Irrelevant Alternatives (IIA) property can then be examined for physicians' choices, at an individual level, for the drug choice set including Alternatives 1, 2 and 3. The test compares estimated parameters between the choice set of three alternatives, including the "no" diabetic drug, and a reduced choice set of two alternatives out of three; the removed alternative is either the rst alternative, the second or the third one; Normally, if the speci cation test is veri ed, coe cients of the parameters between the choice set of three versus a reduced choice set of two alternatives, will be quite similar, whatever the alternative which is removed from the reduced choice set.
The statistical runs using a mixed logit model on the drug choice set are simulations with Halton sequences of 50 draws or 200 draws on the real dataset of diabetes type II. The results provided in this paper are only on the 200 draws simulations, since the Log likelihood Ratio is signi cant (Prob > Chi2 = 0. 05) only with runs with 200 draws, and not with the default number of 50 draws. drug) are similar for the choice set with three alternatives and the reduced choice set of 2 alternatives. When alternative three (no drug case) is removed, the comparison of the two maximum likelihood functions does not work at this point; additional modeling steps are investigated; in particular since the reduced choice set in this case includes the two alternative strategies with pharmacotherapies, different generators of random prices may be needed for the two different drug therapies, in order to avoid correlation issues (e.
Can we discuss at this point, the independence of irrelevant alternatives property at individual level of physicians? In the pre diabetic stage, it may not be relevant to be on a drug therapy (e. g. controversy between pharmacists and physicians at the time of the study on prescribing an oral agent versus aggressive preventive strategy). It may explain that when the "no drug alternative" is removed, the new test comparing a reduced choice set comparing two different clinical strategies with drug treatment and a choice with no drug, re ect irrelevant alternatives, for some individuals. However, further statistical analysis is needed to explore potential estimation issues for implementing the new test for all alternatives on this diabetic dataset.
PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches.
Mathematical and computational modelling of biological processes can be used to enhance quantitative understanding of biomedical phenomena, such as cancer progression  [11] , potentially incorporating patient-specific data to enrich the scope of therapeutic target identification. Models can describe the growth of solid tumours using discrete or continuous representations, with or without accounting for stochasticity  [12]. PRIMAGE (predictive in silico multiscale analytics to support cancer personalised diagnosis and prognosis, empowered by imaging biomarkers) is a funded Horizon 2020 project (RIA, topic SC1-DTH-07-2018) where a combination of these approaches ensures the best of both worlds. The PRIMAGE project focuses on the further development of in silico tools for a more personalised clinical management of childhood cancer by targeting clinical endpoints (CEPs), considering the progression of the growth of the tumour post-diagnosis, but not including the initial oncogenic processes active during embryogenesis.
The University and Polytechnic Hospital La Fe in Spain is the project coordinator institution. On the clinical side, the PRIMAGE consortium has tried to reunite leading European clinical centres together with key opinion leaders specialised in NB and DIPG (as these cancers are the context of the application proposed for the validation works of PRIMAGE in silico tools during this project) (Table  1  The consortium partners ensure complementarity and bring the necessary combination of skill, knowledge, technology, and motivation, comprising a highly motivated team, fully committed to turning PRIMAGE into a case study in the use of existing datasets, serviceoriented architectures and in silico technologies for better diagnosis and treatment of oncology diseases. An advisory board consisting of a recognised group of experts in the fields of paediatric oncology, imaging biomarkers and related information and communication technologies, General Data Protection Regulation and.
Hybrid cloud resources, composed of both private and public cloud sites (based on the Europen open science cloud, EOSC, services), which will host PRIMAGE data repositories. Computational tasks can be deployed and coherently managed by a single access tool, such as the Atmosphere platform (project number 777154, European Commission)  [29]. An integration middleware, consisting of a set of protocols and interfaces between HPC/storage (models), private and public cloud computing/ storage (repositories, sandboxed processing), and external data sources (anonymised clinical and biobanking data). The middleware will be put in place to achieve an adequate level of solution coherency. Upper layer service exposing the features of the underlying infrastructure to researchers as a convenient graphical user interface, to manage definition, execution, and comparison of results of computationally intensive modelling pipelines.
Data repositories Stored clinical data (imaging, clinical, pathology and molecular) from 2002 onwards will be collected by the clinical centres and the paediatric oncological associations involved in PRIMAGE project. Clinical, molecular and imaging data in PRIMAGE will be undertaken under the strictest administrative and contractual procedures to ensure legal and ethical compliance under the General Data Protection Regulation in Europe. All cases included within the data repository, both for model development and platform validation, will have to be approved by the Ethics Committees of their respective centres. Even more, the observational in silico trial design for the internal platform validation, including new data from 2020 to 2022, will be constructed in parallel to standard clinical practice after signed consent is obtained.
Relevant data will be used following extraction, anonymisation and curation for computational simulation developments and testing the integrated PRIMAGE DSS platform. The datasets used for the training and the development of the different in silico models will differ from the datasets used for the later validation of the integrated platform. Therefore, the use of clinical data (in the big data domain) for model training and model testing will be done in two phases: Phase 1: compilation of clinical, molecular and imaging data for PRIMAGE training, knowledge extraction, and multiscale testing of the in silico models for tumour growth, advanced visualisation solutions, identification and analysis of imaging biomarkers and training of predictive models for CEPs. Phase 2: extensive in silico testing of the integrated PRIMAGE DSS platform will be undertaken using cases from the same retrospective dataset, split by cross validation from the curated initial clinical dataset to ensure that these specific datasets are not used in the training phase.
Big data techniques will be applied to generate new knowledge from the advanced in silico tools. The use of retrospective data for training models, testing and validation will require extensive curation and quality control procedures. Automated tools will be implemented in this project to streamline the processes of extracting, mapping data, controlling the quality and homogenisation, translation, and completion of data feeds. Allocation of significant human resources is also foreseen, as human intervention is essential to achieve excellence in the training data sets. Clinical data used in the international staging and stratifying criteria for NB and DIPG, including age, disease extension, image defined risk factors, histological type, grade of tumour differentiation, genetic, and molecular features will be used at the models construction phase.
Imaging biomarkers will be extracted and validated for their use in cancer management, in combination with already available biological biomarker panels. PRIMAGE is defined as an observational in silico study that will be validated also in silico in data recorded from new clinical observations. Image radiomics and dynamic parameters will be obtained from standard of care real-world ultrasound, computed tomography, magnetic resonance, positron emission tomography, and 123 Imetaiodobenzylguanidine scintigraphy or single-photon emission computed tomography images. Experimental and computational methodologies will be used for the identification and validation of novel imaging biomarkers and the development of shared instruments for knowledge extraction from imaging biomarkers, clearly focused on improving disease diagnosis and follow-up.
Computational strategy This project proposes a dataflow strategy to enable the proposed multiscale model to be executed using available HPC resources, in an effective and robust manner, considering that the proposed tumour model will need to be coupled to hundreds of thousands of tissue models, each coupled to also hundreds of thousands cell models. In the proposed dataflow strategy, each single-scale model is described as a black box that takes as input an array of input sets and produces as output an array of output sets. Bi-directional resampling modules are proposed between the database that contains the input sets as computed, and those as required by the next model. Thus, resampling modules are used on value sets that are computed at lower scales and homogenised at upper scales, and on value sets that are computed at upper scales and then particularised at lower scales, at each scale transition.
The proposed workflow for the implementation of the described dataflow approach is: ▪ Implementation of the software infrastructure to manage the multiple databases. This infrastructure includes frameworks for coupling of the models at different scales via resampling functions, and repositories for the three single-scale models as they develop. ▪ Models execution without resampling. At this stage, no resampling will be provided as the software developments are focused on delivery and validation of the dataflow architecture to execute the multiscale model under suboptimal conditions, as efficiently as possible (using acceleration techniques). ▪ Models execution with resampling. Tissue-to-organ and cell-to-tissue modules will be incorporated in the dataflow, using progressively sophisticated multidimensional sampling techniques.
A functional version of the PRIMAGE platform will be extensively evaluated in order to assess the platform's performance, thus its capacity to guide clinical decisions in a precise, reliable, and relevant manner. The datasets used for the validations includes imaging data, clinical data and genetics and other molecular data (Table  2 ). The internal validation of PRIMAGE platform will be performed using datasets provided by the clinical centres and organisations involved in PRIMAGE project which never were used for the models and platform development. In addition, in order to guarantee its correct performance under general conditions when using any datasets from the real world, PRIMAGE platform will be externally validated using data from clinical centres out of PRIMAGE environment. To get access to this data, other hospitals not involved in the project as partners have already been invited to participate by providing NB and DIPG cases to the PRIMAGE platform as independent collaborative international centres.
Clinical data Use of natural language processing tools for automated extraction of relevant pathological data, including data on patient response to specific treatment will be extracted from the electronic health record. Data will then be structured, curated, and stored. treatment, within hospital boundaries. The construction of in silico clinical trials will allow decision-making from causal inference from observational databases emulating a pragmatic target trial if methodological pitfalls are avoided  [2]. This project will bring major advancements in the validation of novel imaging biomarkers; it will create advanced computational models for tumour growth simulation, given response to specific CEPs. A very limited number of imaging biomarkers have been used in routine clinical practice to guide clinical decisions  [7] [8] [9].
Conclusion At the end of the project, the developed open cloudbased platform will support phenotyping (diagnosis), treatment stratification (prediction) and patient-specific CEPs determination (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine learning approaches. The decision support prototype will be constructed and validated on NB and DIPG cancers. The results will be available for the scientific community and ready for transfer learning to other malignant solid tumours. Data infrastructures, imaging biomarkers, and predictive models for in silico medicine research will be validated during this project. Given the rarity of these tumours, international cooperative networking is essential to agglutinate relevant in silico data from clinical trials and large real-world data repositories, facilitating identification of effective clinical tools.
Metrics assessed in PRIMAGE platform Provision of authentication and authorisation and analysis of vulnerabilities from public databases. Assessment of the platform's robustness to preserve data integrity according to GDPR, evaluating the privacy risk (e. g. , as the capability of a model to infer information previously anonymised), and managing the fine-grain consent as GDPR requires. Correctness/reliabilityAssess the correctness of the predictive results for the established clinical end points using testing datasets from clinical data repositories for over 2000 neuroblastoma patients and over 500 diffuse intrinsic pontine glioma patients with complete diagnosis and follow-up data, including treatment and outcomes. fatigue of integrity or potential to induce to use errors Relevance Interviews to assess users' own judgement of helpfulness of the platform to guide them beyond obvious decisions for a given data available set Added value Statistical assessment of occurrence of correct predictions for clinical end points that current diagnosis/prognosis standard protocols could not predict correctly Usability/user friendliness Observation of use patterns, users' eye tracking, interviews to users.
). The University and Polytechnic Hospital La Fe in Spain, the Children's Cancer Research Institute in Austria, the University Hospital Cologne in Germany, and the Pisa University Hospital in Italy constitute the clinical team. All of them belong to the European Research Network in NB and DIPG, facilitating PRIMAGE access to their datasets for further secondary used studies, in addition to their own biobanks and databanks of existing DIPG and NB cases. Moreover, the European Society for Paediatric Oncology (SIOPE) joined PRIMAGE, leading its dissemination and communication activities. The knowledge and expertise of universities, research centres and private companies are essential for success- ful development of PRIMAGE. Partners include Quanti- tative Imaging Biomarkers in Medicine (QUIBIM SME), Institute for Molecular Imaging Technologies and Mech- anical Engineering Department at Valencia Polytechnical University, Chemotargets SME, and Matical Innovation in Spain; the Department of Computer Science at Uni- versity of Konstanz in Germany; Medical Imaging Tech- nologies (Medexpim) in France; the University of Sheffield in United Kingdom; Simulation, Modelling and Engineering software (Ansys group) in France; Akademia Gorniczo-Hutnicza Im (Cyfronet) in Poland; and the De- partment of Industrial Engineering at the University of Bologna in Italy.
INTRODUCTION Nowadays, the number of molecules available to medicinal chemists is huge. The ZINC database merges commercial catalogs proposed by numerous chemical suppliers and contains more than 1. 4 billion compounds  1. It includes both already synthesized or in-stock compounds, and tangible molecules. Despite being just a tiny fraction of the estimated number of possible drug-like molecules (around 10 33 structures)  2  , the currently known chemical space is far from being fully studied and apprehended by medicinal chemists. For example, ChEMBL  3  , containing biologically studied compounds extracted from the scientific literature is a thousand times smaller than ZINC. Thus, while chemical suppliers compete to enumerate the higher number of new virtual molecules  4  , already existing compounds are largely unexplored from a drug discovery perspective.