dataesr/scientific-paragraphs-categorization huggingface dataset:
Classifier Training We consider Ω as a set of four different regions {A, B, C, D}. As we investigate the ability of the model to detect vandalism in different places, three models are trained on different datasets: the d A dataset, the d B dataset, and a dataset grouping the d A and d B datasets. Using those sets allows the evaluation of the possibility to answer our different questions related to the ability of our approach to detect vandalism in the area over which the data are learned and also to generalize the detection to different areas. The processed data are split into training and testing datasets as: p x = p train x ∪ p test x where p train x is the training set from area x and p test x the testing set. The testing set should contain data that the classifier did not encounter during the training phase; therefore: p train x ∩ p test x = ∅ Let C x be a classifier trained on p train x.
4. 3. 3 Classifier Training We consider Ω as a set of four different regions {A, B, C, D}. As we investigate the ability of the model to detect vandalism in different places, three models are trained on different datasets: the d A dataset, the d B dataset, and a dataset grouping the d A and d B datasets. Using those sets allows the evaluation of the possibility to answer our different questions related to the ability of our approach to detect vandalism in the area over which the data are learned and also to generalize the detection to different areas. The processed data are split into training and testing datasets as: p x = p train x ∪ p test x where p train x is the training set from area x and p test x the testing set. The testing set should contain data that the classifier did not encounter during the training phase; therefore: p train x ∩ p test x = ∅ Let C x be a classifier trained on p train x.
I. Introduction Health data constitute a significant source of medical information and can be used after suitable processing for diagnosis and treatment. They comprise a huge number of records, which may conceal significant patterns and dependencies. The vast amounts of data generated as well as the increased complexity of the health field make it hard to extract hidden information. A data warehouse is a proper solution for data integration because it permits the management and querying of data in various ways and forms. Retrieving data from multi-dimensional data warehouse supports the revelation of hidden and important information. Thus, applying data mining techniques to data warehouses that store healthcare data for knowledge discovery is appropriate because such data is already cleaned, grouped, and stored  [1].
In this study, we developed a clinical data warehouse for lung and ovarian cancer data provided by the United States National Cancer Institute (NCI; https://www. cancer. gov). These data include demographic information of the patient, past medical history, general health status, treatment modalities, and cancer characteristics  [7]. The main contribution of this research is that for the first time the constellation schema is used to combine data from different cancer types to extract useful information related to hidden associations among medical features by data querying and analysis. The fact constellation schema consists of multiple fact tables, which share dimension tables. In fact, the fact constellation schema consists of more than one-star schema at a time by providing a flexible schema in which complex queries can be used to access data from the data warehouse.
Sheta and Eldeen  [16]  evaluated the architecture of a healthcare data warehouse specific to cancer diseases. They built the cancer data warehouse to integrate an operational database and medical files. Analysis of the data is facili-tated by using OLAP (online analytical processing) cubes and viewing multilevel details of the data. According to the model developed in  [11] , the naive Bayes method is the most effective way to predict patients with lung cancer. Arous et al. [8]  integrated two different operational health systems that include pancreatic cancer data. They explained the challenges they faced because the data types in the databases differed. Ramachandran et al. [17]  developed a clinical data warehouse to identify potential cancer patients, which uses classification, clustering, and prediction data mining technologies.
After the preprocessing step, the data is clustered using a K-means clustering algorithm. Their research can help detect a person's predisposition to develop cancer before going for clinical and lab tests. The remainder of this paper is organized as follows. Section II describes the methods applied related to the data warehouse domain and the research procedures used. Section III presents the results and describes the implementation of the proposed clinical data warehouse. Finally, Section IV provides our interpretation of the results, concluding remarks, and future research plan.
II. Methods In this study, a clinical data warehouse for both lung and ovarian cancer data was developed for data mining and clinical decision support. The data required for the study were obtained from the NCI's prostate, lung, colorectal, and ovarian cancer screening trial. Three major tasks had to be performed to prepare the data, namely, data acquisition, storage, and information delivery as depicted in Figure 1  [16]. Pre-processing of data is an important phase in the implementation of data warehouse applications. For data quality control, it is vital to pre-process the data to ensure that it is clean and meaningful. Lung and ovarian cancer data were cleaned to remove mistakes, repetitions, and stored in the data warehouse as a first stage. It was ensured that the data warehouse would conform to the subject-oriented, integrated, time-variant and non-volatile conditions of the data warehouse approach presented in  [18].
Data Warehouse A data warehouse schema is composed of fact and dimension tables. The fact table is connected to a number of dimension tables with many-to-one relationships. The term "fact" represents a business measure. A fact table includes two kinds of columns, that is, two or more foreign keys that connect primary keys and numeric attributes (known as measures) on which calculations can be made. Two of the most popular and well-known schemas for implementing a multi-dimensional model are the star schema and the snowflake schema  [19]. The star schema adopts the relational model for the representation of multi-dimensional data consisting of a fact table and a number of nonnormalized dimension tables. Figure  2  presents an example of a star schema for medical records. Each tuple of the fact table stores data from a patient with one diagnosis having a specific therapy type applied on one day.
Other possible granularities for the time dimension include week, month, and year. The granularity decision influences the degree of detail for the recorded data and the size of the database. Each dimension consists of one or more hierarchies and represents a semantically related concept within the modeled domain. Dimensional attributes are descriptive, textual values for describing the dimensional value. Dimension tables have fewer rows than fact tables. Each dimension is defined  by a single primary key that serves as the basis for the referential integrity with any given fact table to which it is joined  [19]. A snowflake schema is obtained from a star schema, and it is made up of a fact table and many dimensions related to that fact. In this type of schema, dimension data are grouped into multiple (totally or partially) normalized tables instead of one large non-normalized table.
An example of a patient admission snowflake schema is shown in Figure  3. With snowflake modeling, the number of the tables and the required joins increase. A fact constellation schema consists of a collection of several fact tables. They share dimension tables, which are called conformed dimensions. It can be considered as an extension of a star schema. Although it is more complicated than the star and snowflake schemas, it is generally utilized. For designing the lung and ovarian cancer diseases data warehouse, the fact constellation schema model was adopted as a multi-dimensional data modelling approach because it can be used as a collection of numerous star schemas.
PLCO Data The NCI, the main institution for cancer research in the United States, provided the data used for this research. The NCI manages the National Cancer Program, which conducts and supports research, training, health information dissemination, and other programs with respect to the cause, diagnosis, prevention, and treatment of cancer, rehabilitation from cancer, and the continuing care of cancer patients and the families of cancer patients  [7]. The prostate, lung, colorectal, and ovarian (PLCO) dataset was created through a large-scale, randomized study to determine whether certain screening tests will reduce the number of deaths from these cancers  [7]. Within the scope of this research, data from 3,594 lung and 3,914 ovarian cancer cases were studied. These data included demographic information about the patient, past medical history, general health status, smoking history, treatment modalities, and cancer characteristics.
Clinical Data Warehousing Stages Data integration is very important for healthcare organizations because these data must be provided as input to clinical decision support systems. Data from different operational systems are collected in a common repository and made  available for operations such as querying and analysis using the data warehouse approach. The fact constellation schema can support consistent, integrated, and flexible sources of data, so it was adopted in this research after thoughtful consideration of the available types of cancer data provided by the NCI. To build the lung and ovarian cancer clinical data warehouse, preprocessing operations were applied to data. Data processing was the most important and time-consuming part of the process of designing the data warehouse, which included various processes, such as filtering, cleaning and transforming the data, to ensure better quality and accurate results.
In this study, the data warehouse was developed with Microsoft SQL Server 2012. First, the data were extracted from the source system. SQL server integration services (SSIS) were used for the extract-transform-load (ETL) process. After the elimination of wrong and inconsistent data, the data were transformed and loaded into the data warehouse. The data are stored in a fact constellation schema model using a multi-dimensional modeling approach to perform OLAP operations. The multi-dimensional data model is based on concepts such as cube, dimension, and hierarchy. The fact constellation schema model used is easy to understand and appropriate for query performance. As seen in Figure  4 , the fact table contains keys representing each of the dimension tables. The lung and ovarian cancer data warehouse model created using the Microsoft SQL Server has 14 dimension tables and 2 fact tables that contain the following: (1) complications, diagnoses, treatments, X-ray results, and X-ray anomalies for each cancer type, (2) patient information, diet history, dietary data and time dimensions shared by both fact tables, (3) one lung cancer fact table, and (4) one ovarian cancer fact table.
At a later stage, the SQL server analysis services (SSAS) were used to build cubes using the lung and ovarian clinical data warehouse as the data source. A single cube was built using all dimensions in the data warehouse. This is to allow analysis of the diet history factor with respect to each category of complications, abnormalities, location, age, sex, and time. Building cubes enables users to use OLAP tools for interactive analysis of multi-dimensional data at various granularity levels. Data cubes store summarized data that precomputed measures such as count() and sum() retrieve faster in terms of query processing times.
OLAP Queries The established clinical data warehouse was designed to respond to large and complex queries. It is intended to help medical analysts and physicians in making decisions through diverse perspectives. Several sample OLAP queries are presented below for the lung and ovarian cancer clinical data warehouse. This query counted the number of patients grouped and ordered by the daily smoked cigarettes who received a biopsy and had nodules. The query is very important to prove the danger of smoking. Query 2. What is the distribution of patients with complication Pnömotoraks, collection of air in the pleural cavity, and those treated with chemotherapy according to age? This query counted number of patients grouped by their age that had Pnömotoraks complication and were treated with chemotherapy.
The result table helps doctors to evaluate specific age groups that have both conditions. This query combined data from both cancer types, lung and ovarian. Therefore, both fact tables from the fact constellation schema were used, Fact_Lung and Fact_Ovarian. The query is very important for statistical medical data analysis. This query was used to extract patients' personal data related to both types of cancers and specific treatment. In spite of advanced query complexity, Query 4 is easily expressed using the fact constellation schema by performing four join operations.
In the design of the data warehouse, the fact constellation schema model, a multi-dimensional data modeling approach, was used. This model succeeded in responding to complex queries, and the analysis of data was facilitated by using OLAP cubes and viewing multi-level data details. Indisputably, the fact constellation schema is the most challenging data warehouse design architecture. For the first time, real medical data were used combining two different cancer types using fact constellation modelling to extract semantically useful information to improve the decisionmaking process for cancer patients. The present approach also provides the ability to access data in the data warehouse using complex queries because dimensional attributes are shared by a number of fact tables, unlike the star and snowflake schemas.
The fact constellation schema is capable of dealing with complex systems because the relationships between fact and dimensional tables are more easily understood. The medical field requires advanced data analytical processing by studying real critical medical data and this work addresses this need. We plan to integrate prostate and colorectal cancer data into the already built clinical data warehouse and then to develop a decision support system for adapting more data analysis and mining tools. Hence, we can analyze cancer diseases, find correlations among attributes and cost of treatment for these diseases, death rates for specific types of cancer, and the impact of particular drugs on each disease. We expect that the final implementation of the decision support system will greatly assist cancer informatics research.
3 A meta-analysis of population-based observational studies from the United States and Europe concluded that about 30% of AD cases may be attributable to seven potentially modifiable risk factors for AD-diabetes, midlife hypertension, midlife obesity, physical inactivity, depression, smoking, and low educational attainment. 14 is study and others have concluded that targeting these risk factors may represent a powerful strategy for risk reduction of AD. Given the complex, multifactorial, and heterogeneous nature of late-onset AD and dementia, interventions targeting several risk factors and mechanisms simultaneously may be required to achieve optimal preventive effects. 15 e Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER, ClinicalTrials. gov: NCT01041989) represents the first large, long-term randomized controlled trial (RCT) demonstrating that a multidomain lifestyle intervention can improve cognitive function in older adults from the general population who are at elevated risk of developing dementia.
HARMONIZATION OF TRIALS, DATA SHARING, AND ALIGNMENT WW-FINGERS promotes the prospective harmonization of methods and outcomes across studies and responsible sharing of data. This will enable joint analyses and comparisons across studies and will generate robust evidence to inform dementia prevention strategies. The power of WW-FINGERS can only be realized through data sharing. In the AD field, open and responsible data sharing has fueled tremendous progress in the Alzheimer's Disease Neuroimaging Initiative (ADNI), as well as in four prevention studies that came together under the umbrella of the Collaboration for Alzheimer's Prevention (CAP). 38 Recommendations to promote responsible data sharing were also adopted by a European task force through a consensus-building process. 39 vision for data sharing in WW-FINGERS has been adapted from the Institute of Medicine (IOM) report on sharing clinical trial data.
40 ese principles include (1) establishing a culture of sharing with incentives for sharing data; (2) building a global platform for sharing data; (3) establishing best practices for data sharing; (4) providing adequate financial support for the sharing of data and allocating the costs among all stakeholders; and (5) minimizing the risks and reducing disincentives for data sharing. Integrating data from different RCTs and cohort studies into a shared database requires that data are harmonized using existing and accepted data standards, templates, and common data elements. Common data elements enable linkage of data across multiple studies, increase the consistency and quality of data, facilitate comparisons, drive efficiencies, and promote interoperability. In WW-FINGERS, harmonization will also be promoted in study design, enrollment criteria, selection of interventions and outcomes, and monitoring of intervention adherence.
Members of the WW-FINGERS Network recognize the need to establish rigorous standards and harmonize these elements before the studies begin data collection. Prospective harmonization of the WW-FINGERS trials is being detailed in the WW-FINGERS Harmonization Protocol, which is currently under development. Among the key points, the Protocol specifies that in all trials the main principles of the intervention are based on the experiences from the FINGER study. It is recommended that all the basic components of the multidomain intervention available in the FINGER study should be implemented; that is, dietary counseling, physical exercise, cognitive training, and vascular and metabolic risk monitoring. At the same time, local and cultural adaptations of the content and delivery method of the intervention are encouraged.
Given cultural differences in the WW-FINGERS Network, assessment of these domains may involve administration of different cognitive tests. Another approach might be to administer a test that assesses everyday functional memory (as opposed to rote memory) in ways that are more culturally relevant to individuals under study. It should be emphasized that the WW-FINGERS is not a single study with multiple centers around the world, with unified strict criteria on harmonized outcomes and interventions. Instead, the aim is to test the adaptation of the FINGER study in different populations regarding feasibility and effectiveness. Thus, the studies in the WW-FINGERS are separate studies with their own research questions with pooled data analyses enabled through prospective harmonization, which increases power and permits subgroup analyses.
Indeed, one of the key goals in the WW-FINGERS is to incorporate diversity, and inclusion of historically underrepresented groups (eg, sex, race, ethnicity, education, socioeconomic status) is highly encouraged. Pooled data from the WW- Regarding the need for a data sharing platform, the WW-FINGERS Network will build upon prior work, as well as search for innovative ICT-solutions (ie, Information and communications technology), generated by the rapid expansion of data sharing technologies and tools. For instance, U. S. POINTER will use the Global Alzheimer's Association Interactive Network (GAAIN, gaain. org) as a platform for global data sharing, as GAAIN can link diverse data sets. ICT solutions can help overcome the challenges of having data cross country borders, harmonizing data sets, and allowing for aggregate interrogation in a secure framework.
As the WW-FINGERS Network expands and trials are launched, data-sharing tools will be assessed as it will be important for the various initiatives to align so that trial data are analyzed, interpreted, and validated in a consistent manner and that privacy and confidentiality are maintained. From the European perspective, given the current changes in regulation regarding data protection (ie, European General Data Protection Regulation, GDPR),  41  sharing individual level data with countries outside of the European Union is difficult, even with proper pseudonymization. 42 Therefore, instead of sharing individual level data across the borders, the aim within the WW-FINGERS Network is to create a federated database platform enabling pooled data analyses remotely. 43 Overall, WW-FINGERS aims to optimize harnessing of data, while accounting for global variations and increasing requirements for data protection and sharing.
many, France) in the Multimodal Prevention Trial for Alzheimer's Disease (MIND-ADmini, ClinicalTrials. gov: NCT03249688). MIND-ADmini is an ongoing pilot trial evaluating the feasibility of the FINGER multidomain lifestyle intervention in prodromal AD defined according to the International Working Group (IWG)-1 criteria. 29 Participants also have modifiable vascular or lifestyle-related risk factors. Conducting a feasibility trial in people who are already in the early stages of AD and have some cognitive impairment is important because of the potential need for additional support with healthy lifestyle maintenance. The duration of the trial is 6 months, with an optional 6-month extension. Participants are randomized into three groups: multidomain lifestyle intervention based on the FINGER model; multidomain lifestyle intervention plus the medical food Fortasyn Connect; or standard care.
SoUth Korean study to PrEvent cognitive impaiRment and protect BRAIN health through lifestyle intervention in at-risk elderly people (SUPERBRAIN, ClinicalTrials. gov: NCT03980392) is a multicenter feasibility study enrolling seniors (60 to 79 years) with at least one modifiable risk factor for dementia. The study includes two intervention arms-facility-based intervention and home-based intervention-as well as a group receiving regular health advice. The 24-weekmultidomain intervention has been derived by the FINGER model and includes five components: monitoring and management of metabolic and vascular risk factors; cognitive training and social activity; physical exercise; nutritional guidance; and motivational enhancement. The trial aims to assess the feasibility of the multidomain intervention, and includes as secondary outcomes disability, depressive symptoms, quality of life, vascular risk factors, physical performance, nutritional assessment, and a motivation questionnaire.