Dataset name: asppl_dataset_v2.csv

Version: 2.0

Dataset period: 06/07/2018 - 01/14/2022

Dataset Characteristics: Multivalued

Number of Instances: 8118

Number of Attributes: 9

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Occupational Classification (CBO) (Brasil, 2022b);

• National Registry of Health Establishments (CNES) (Brasil, 2022c);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).

Table 1: Description of AVASUS dataset features.

Attributes	Description	datatype	Value
gender	Gender of the course participant.	Categorical.	Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)
course_progress	Percentage of completion of the course.	Numerical.	Range from 0 to 100.
course_evaluation	A score given to the course by the participant.	Numerical.	0, 1, 2, 3, 4, 5 or NaN.
evaluation_commentary	Comment made by the participant about the course.	Categorical.	Free text or NaN.
region	Brazilian region in which the participant resides.	Categorical.	Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).
CNES	The CNES code refers to the health establishment where the participant works.	Numerical.	CNES Code or NaN.
health_care_level	Identification of the health care network level for which the course participant works.	Categorical.	“ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”, “ALTA COMPLEXIDADE”, and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")
year_enrollment	Year in which the course participant registered.	Numerical.	Year (YYYY).
CBO	Participant occupation.	Categorical.	Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)

Dataset name: prison_syphilis_and_population_brazil.csv

Dataset period: 2017 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 13

Missing Values: No

Source:

• National Penitentiary Department (DEPEN) (Brasil, 2022d);

Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.

Table 2: Description of DEPEN dataset Features.

Attributes	Description	datatype	Value
Region	Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.	Categorical.	Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.
syphilis_2017	Number of syphilis cases in the prison system in 2017.	Numerical.	Number of syphilis cases.
syphilis_rate_2017	Normalized rate of syphilis cases in 2017.	Numerical.	Syphilis case rate.
syphilis_2018	Number of syphilis cases in the prison system in 2018.	Numerical.	Number of syphilis cases.
syphilis_rate_2018	Normalized rate of syphilis cases in 2018.	Numerical.	Syphilis case rate.
syphilis_2019	Number of syphilis cases in the prison system in 2019.	Numerical.	Number of syphilis cases.
syphilis_rate_2019	Normalized rate of syphilis cases in 2019.	Numerical.	Syphilis case rate.
syphilis_2020	Number of syphilis cases in the prison system in 2020.	Numerical.	Number of syphilis cases.
syphilis_rate_2020	Normalized rate of syphilis cases in 2020.	Numerical.	Syphilis case rate.
pop_2017	Prison population in 2017.	Numerical.	Population number.
pop_2018	Prison population in 2018.	Numerical.	Population number.
pop_2019	Prison population in 2019.	Numerical.	Population number.
pop_2020	Prison population in 2020.	Numerical.	Population number.

Dataset name: students_cumulative_sum.csv

Dataset period: 2018 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 7

Missing Values: No

Source:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.

Table 3: Description of Students dataset Features.

The Updated Systematic Review reviews the January 2010 to August 2013 health IT literature to examine the effects of health IT across three aspects of care: efficiency, quality, and safety. This report updates previous systematic reviews of the health IT literature, focusing specifically on identifying and summarizing the evidence related to the use of health IT as outlined in the Meaningful Use regulations. The review examined the literature to determine the article authors' findings related to the effects or associations of a meaningful use functionality on an aspect of care. Each article's findings was scored as positive (defined as: health IT improved key aspect of care but none worse off), mixed-positive (defined as: positive effects of health IT outweight negative effects), neutral (defined as: health IT not associated with change in outcome), or negative (defined as: negative effects of health IT on outcome). The full review data: article, related meaningful use functionality, aspect of care, and author sentiment are provided in this dataset.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

Clinical Data Analytics Market Outlook

The clinical data analytics market has garnered significant attention in recent years, and as of 2023, it is valued at approximately USD 7.5 billion. The market is projected to reach an impressive USD 19.8 billion by 2032, growing at a robust CAGR of 11.2% from 2024 to 2032. This rapid expansion can be attributed to the increasing demand for data-driven decision-making in healthcare, driven by the necessity to enhance patient outcomes and streamline healthcare operations. The integration of advanced analytics in clinical processes allows healthcare providers to transform data into actionable insights, thereby improving quality of care and reducing costs.

The burgeoning healthcare sector's reliance on data analytics is a significant growth driver of the clinical data analytics market. Healthcare organizations are increasingly adopting analytics to manage the massive volume of data generated from various sources, including electronic health records (EHRs), clinical trials, and patient monitoring systems. The ability to harness this data effectively aids in developing personalized treatment plans, predicting disease outbreaks, and optimizing resource allocation. Moreover, government initiatives to promote the adoption of health information technologies and improve patient care quality further bolster the market's growth prospects. As a result, healthcare providers are investing heavily in analytics tools to stay competitive and compliant with regulations.

Another pivotal factor contributing to the market's growth is the emphasis on precision medicine, which necessitates advanced analytics to tailor medical treatment to individual characteristics. Precision health initiatives require analyzing vast datasets to identify patterns and correlations that inform personalized healthcare strategies. This approach is increasingly being recognized for its potential to enhance treatment efficiency and reduce adverse effects. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) technologies into clinical data analytics systems empowers healthcare professionals with predictive insights and automated decision support, further driving market expansion. The synergy between precision medicine and data analytics is transforming healthcare delivery by enabling more precise diagnostics and therapies.

The proliferation of cloud-based solutions is also a critical element propelling the clinical data analytics market. Cloud technology offers scalability, flexibility, and cost-effectiveness, allowing healthcare organizations to store and analyze large datasets efficiently. The shift towards cloud-based analytics solutions is particularly beneficial for small and medium-sized enterprises (SMEs) that may not have the resources for extensive on-premises infrastructure. Furthermore, the COVID-19 pandemic underscored the importance of real-time data access and collaboration, leading to accelerated adoption of cloud-based platforms. As healthcare providers continue to embrace digital transformation, the demand for cloud-based analytics solutions is expected to rise, contributing to market growth.

Big Data Analytics in Healthcare is revolutionizing the way healthcare providers manage and utilize vast amounts of data. By leveraging big data, healthcare organizations can gain deeper insights into patient care, operational efficiencies, and clinical outcomes. The ability to analyze large datasets allows for more accurate predictions and personalized treatment plans, ultimately enhancing patient care. Big data analytics also plays a crucial role in identifying trends and patterns that can lead to early detection of diseases and better resource management. As healthcare systems continue to generate massive volumes of data, the integration of big data analytics becomes essential for driving innovation and improving overall healthcare delivery.

Regionally, North America leads the clinical data analytics market, driven by the high adoption rate of advanced healthcare technologies and favorable government initiatives. The United States, in particular, has witnessed substantial investments in healthcare IT infrastructure and a strong focus on data-driven healthcare systems. Europe follows closely, with countries like Germany, the UK, and France promoting the digitization of healthcare services. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, fueled by the increasing penetration of healthcare IT solutions in emerging ec

According to Cognitive Market Research, the global Clinical Risk Grouping Solution market will be USD 715.8 million in 2023 and expand at a compound annual growth rate (CAGR) of 15.20% from 2023 to 2030.

North America held the major market of more than 40% of the global revenue with a market size of USD 286.32 million in 2023 and will grow at a compound annual growth rate (CAGR) of 13.4% from 2023 to 2030
Europe accounted for a share of over 30% of the global market size of USD 214.74 million
Asia Pacific held the market of more than 23% of the global revenue with a market size of USD 164.63 million in 2023 and will grow at a compound annual growth rate (CAGR) of 17.2% from 2023 to 2030
Latin America market of more than 5% of the global revenue with a market size of USD 35.79 million in 2023 and will grow at a compound annual growth rate (CAGR) of 14.6% from 2023 to 2030
Middle East and Africa held the major market of more than 2% of the global revenue with a market size of USD 14.32 million in 2023 and will grow at a compound annual growth rate (CAGR) of 14.9% from 2023 to 2030.

Rising Healthcare Costs to Provide Viable Market Output

The adoption of Clinical Risk Grouping Solutions is mostly driven by the need to save healthcare expenses. These creative fixes are essential to healthcare systems' resource allocation optimization. These technologies assist in identifying high-risk patients by applying predictive modeling and advanced analytics, which enables healthcare professionals to put focused preventive care measures in place. By preventing health conditions from worsening and lowering the need for costly procedures, this proactive strategy improves patient outcomes and helps save money. As healthcare organizations worldwide look for more efficient and cost-effective models, Clinical Risk Grouping Solutions are in high demand due to their ability to enhance care management and advance a value-based approach to healthcare.

Big Data Adoption to Propel Market Growth

Advances in big data analytics have fundamentally altered the healthcare sector by enabling the analysis of massive patient data sets, resulting in more accurate risk assessments and targeted therapies. These technology advancements are utilized by Clinical Risk Grouping Solutions to sort through extensive patient data, spot trends, and forecast possible health hazards. Healthcare workers are better equipped to anticipate patient requirements, improve care plans, and improve overall health outcomes when analyzing and interpreting large datasets. Big data analytics and healthcare work well together, essential for advancing precision medicine, raising the standard of patient care, and eventually changing the healthcare system to be more individualized and efficient.

Market Restraints of the Clinical Risk Grouping Solution Market

Data Quality and Integration to Restrict Market Growth

Clinical risk grouping solutions faces significant issues related to data integration and quality. The dependability of risk assessments may be jeopardized by inadequate or inaccurate data from many sources, which could affect patient outcomes. To yield significant insights, it is crucial to guarantee the precision and coherence of integrated data for these solutions. Ensuring the accuracy of risk assessments and sustaining the efficacy of Clinical Risk Grouping Solutions in facilitating focused healthcare interventions require strong data governance protocols, uniform formats, and frequent quality assurances.

Impact of the COVID-19 on Clinical Risk Grouping Solution Market

The COVID-19 pandemic has brought attention to the significance of risk stratification in healthcare, which has substantially impacted the market for clinical risk grouping solutions. The crisis has made it clear that sophisticated analytics tools are required to properly evaluate and manage patient risk, particularly in light of the increased attention paid to vulnerable populations. Clinical risk grouping solutions are now crucial for determining who is more likely to experience catastrophic consequences, allocating resources optimally, and enhancing care coordination. In the context of pandemic-induced healthcare complications, the market is growing due to the rapid adoption of these solutions by healthcare systems looking for more robust and proactive ways to manage ongoing and emerging health concerns. What is Clinical Risk Group...

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Healthcare Fraud Detection Market Size 2024-2028

The healthcare fraud detection market size is forecast to increase by USD 914.3 million at a CAGR of 11% between 2023 and 2028.

In the healthcare industry, the market is experiencing significant growth due to several key factors. The increasing number of patients seeking health insurance and the complexity of insurance claims are driving the need for advanced solutions. Statistical methods, machine learning, and artificial intelligence are being employed to enhance payment integrity and detect fraudulent activities in real time. These technologies enable on-premises and cloud-based solutions to analyze large volumes of data and identify patterns that may indicate fraud. The emergence of social media and its impact on the healthcare industry also necessitates the use of advanced analytics to ensure accurate claim processing and prevent fraud. However, challenges persist, including the time-consuming deployment and need for frequent upgrades of fraud detection systems. To address these challenges, healthcare providers and insurance companies are investing in advanced analytics solutions to streamline operations, improve efficiency, and maintain payment integrity.

What will be the Size of the Market During the Forecast Period?

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Healthcare fraud continues to pose a significant challenge for the healthcare industry, resulting in substantial financial losses. According to estimates, healthcare fraud costs the US economy approximately USD 68 billion annually. This figure includes fraudulent claims, billing schemes, identity theft, prescription fraud, and other fraudulent healthcare activities. Fraudulent claims arise when providers or patients submit false or exaggerated claims to insurance companies for medical services. Billing schemes involve overcharging for services or supplies, while identity theft occurs when an individual uses someone else's personal information to obtain healthcare services or prescription medications. Prescription fraud includes the unlawful distribution of prescription drugs, often for financial gain.



Furthermore, healthcare fraud offenders employ various tactics to evade detection, making it essential for healthcare organizations to implement strong fraud detection and prevention measures. Advanced analytics solutions, such as data analysis techniques and statistical methods, have emerged as effective tools in the fight against healthcare fraud. Machine learning and artificial intelligence (AI) are increasingly being used in healthcare fraud detection. These technologies enable descriptive analytics, which involves analyzing historical data to identify patterns and trends. Predictive analytics uses this information to anticipate future fraudulent activities, while prescriptive analytics recommends actions to prevent fraud. Data science plays a crucial role in healthcare fraud detection, as it involves extracting insights from complex data sets. Data analytics, including fraud detection solutions, can be delivered through on-premise or cloud-based solutions. On-premise solutions offer greater control over data security, while cloud-based solutions provide flexibility and scalability. Insurance claims review is a critical component of healthcare fraud detection.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Descriptive analytics
  Predictive analytics
  Prescriptive analytics


Geography

  North America

    Canada
    US


  Asia

    China
    India


  Europe

    Germany


  Rest of World (ROW)

By Type Insights

The descriptive analytics segment is estimated to witness significant growth during the forecast period.

Descriptive analytics serves as the foundation for advanced analytics such as predictive and prescriptive analytics. By integrating basic descriptive analytics with additional data sources, meaningful insights are generated. Descriptive analytics is a fundamental analytics technique widely used by healthcare organizations. Each business unit employs descriptive analytics to monitor operational efficiency and identify trends. Financial statements, presentations, and dashboards showcase the outcomes of descriptive analytics. This form of analytics examines past data to understand the changes that have occurred. Insurance claims review, pharmacy billing fraud, and payment integrity are some areas where descriptive analytics plays a crucial role in maintaining healthcare spending.

Furthermore, machine learning and artificial intelligence technologies can enhance the capabilities of descriptive analytics, leading to improved fraud detection. On-premis

Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p < 0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating the least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be healthcare lie-detection methods to enhance optimal diagnosis and treatment.

In 2023, the number of data compromises in the United States stood at 3,205 cases. Meanwhile, over 353 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2022, healthcare, financial services, and manufacturing were the three industry sectors that recorded most data breaches. The number of healthcare data breaches in the United States has gradually increased within the past few years. In the financial sector, data compromises increased almost twice between 2020 and 2022, while manufacturing saw an increase of more than three times in data compromise incidents. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

BackgroundHealthcare is facing a growing threat of cyberattacks. Myriad data sources illustrate the same trends that healthcare is one of the industries with the highest risk of cyber infiltration and is seeing a surge in security incidents within just a few years. The circumstances thus begged the question: are US hospitals prepared for the risks that accompany clinical medicine in cyberspace?ObjectiveThe study aimed to identify the major topics and concerns present in today's hospital cybersecurity field, intended for non-cyber professionals working in hospital settings.MethodsVia structured literature searches of the National Institutes of Health's PubMed and Tel Aviv University's DaTa databases, 35 journal articles were identified to form the core of the study. Databases were chosen for accessibility and academic rigor. Eighty-seven additional sources were examined to supplement the findings.ResultsThe review revealed a basic landscape of hospital cybersecurity, including primary reasons hospitals are frequent targets, top attack methods, and consequences hospitals face following attacks. Cyber technologies common in healthcare and their risks were examined, including medical devices, telemedicine software, and electronic data. By infiltrating any of these components of clinical care, attackers can access mounds of information and manipulate, steal, ransom, or otherwise compromise the records, or can use the access to catapult themselves to deeper parts of a hospital's network. Issues that can increase healthcare cyber risks, like interoperability and constant accessibility, were also identified. Finally, strategies that hospitals tend to employ to combat these risks, including technical, financial, and regulatory, were explored and found to be weak. There exist serious vulnerabilities within hospitals' technologies that many hospitals presently fail to address. The COVID-19 pandemic was used to further illustrate this issue.ConclusionsComparison of the risks, strategies, and gaps revealed that many US hospitals are unprepared for cyberattacks. Efforts are largely misdirected, with external—often governmental—efforts negligible. Policy changes, e.g., training employees in cyber protocols, adding advanced technical protections, and collaborating with several experts, are necessary. Overall, hospitals must recognize that, in cyber incidents, the real victims are the patients. They are at risk physically and digitally when medical devices or treatments are compromised.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

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The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

Dataset name: asppl-dataset.csv

Version: 1.0

Dataset period: 06/07/2018- 05/25/2021

Dataset Characteristics: Multivalued

Number of Instances: 4861

Number of Attributes: 33

Missing Values: Yes

Area(s): Health and education

Sources:

Primary: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];

Secondary:

Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];

National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and

Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].

Description: The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.

Oral diseases affect nearly 3.5 billion people, with the majority residing in low- and middle-income countries. Due to limited healthcare resources, many individuals are unable to access proper oral healthcare services. Image-based machine learning technology is one of the most promising approaches to improving oral healthcare services and reducing patient costs. Openly accessible datasets play a crucial role in facilitating the development of machine learning techniques. However, existing dental datasets have limitations such as a scarcity of Cone Beam Computed Tomography (CBCT) data, lack of matched multi-modal data, and insufficient complexity and diversity of the data. This project addresses these challenges by providing a dataset that includes 329 CBCT images from 169 patients, multi-modal data with matching modalities, and images representing various oral health conditions.

This report is one in a series of five syntheses of Primary Health Care Transition Fund (PHCTF) initiative results addressing the following topics: Chronic Disease Prevention and Management, Collaborative Care, Evaluation and Evidence and Information Management and Technology. The fifth report is an overall analysis on the role and impact of the PHCTF in primary health care renewal entitled Laying the Groundwork for Culture Change: The Legacy of the Primary Health Care Transition Fund.

Contains data from World Health Organization's data portal covering the following categories:
Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, Neglected tropical diseases, TOBACCO, Nutrition, World Health Statistics, Health financing, Substance use and mental health, Injuries and violence, HIV/AIDS and other STIs, Public health and environment, Urban health, Child mortality, Noncommunicable diseases, Noncommunicable diseases CCS, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, Health inequality monitor, Health Equity Monitor, Child malnutrition, Organ transplants, Electromagnetic fields, International Health Regulations (2005) monitoring framework, Postnatal care, Insecticide resistance, Substance abuse, Lead paint, Neglected Tropical Diseases, ORALHEALTH, Universal Health Coverage, Adolescent mortality, Maternal, newborn, child and adolescent healthand ageing, Education, UNICEF indicators, Financial Protection, Maternal and reproductive health, Research and Development, Country policy, SDG targets, Global Observatory for eHealth (GOe), Antimicrobial Surveillance, RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, FINANCIAL PROTECTION - IMPOVERISHMENT, Dementia, Health Expenditure, AMR GLASS, Noncommunicable diseases and mental health, Risk factors: Radon, Health workforce, International Health Regulations, AMR GASP, ICD, SEXUAL AND REPRODUCTIVE HEALTH, Family Planning, Immunization, Air pollution, Nutrition Landscape Information System, NLIS, Care Seek, Assistive Technology, Health policy, Hemoglobin level, Foodborne Diseases, Hazards impact on health, WASH, AMC GLASS, Electrification of health care facilities, Medical Devices, Prison data, Climate Change Survey, Child Mortality

For links to individual indicator metadata, see resource descriptions.

Contains data from World Health Organization's data portal covering the following categories:
Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, Neglected tropical diseases, TOBACCO, Nutrition, World Health Statistics, Health financing, Substance use and mental health, Injuries and violence, HIV/AIDS and other STIs, Public health and environment, Urban health, Child mortality, Noncommunicable diseases, Noncommunicable diseases CCS, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, Health inequality monitor, Health Equity Monitor, Child malnutrition, Organ transplants, Electromagnetic fields, International Health Regulations (2005) monitoring framework, Postnatal care, Insecticide resistance, Substance abuse, Lead paint, Neglected Tropical Diseases, ORALHEALTH, Universal Health Coverage, Adolescent mortality, Maternal, newborn, child and adolescent healthand ageing, Education, UNICEF indicators, Financial Protection, Maternal and reproductive health, Research and Development, Country policy, SDG targets, Global Observatory for eHealth (GOe), Antimicrobial Surveillance, RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, Dementia, Health Expenditure, AMR GLASS, Noncommunicable diseases and mental health, Risk factors: Radon, Health workforce, International Health Regulations, AMR GASP, ICD, SEXUAL AND REPRODUCTIVE HEALTH, Family Planning, Immunization, Air pollution, Nutrition Landscape Information System, NLIS, Care Seek, Assistive Technology, Health policy, Hemoglobin level, Foodborne Diseases, Hazards impact on health, WASH, AMC GLASS, Electrification of health care facilities, Medical Devices, Prison data, Climate Change Survey, Child Mortality

For links to individual indicator metadata, see resource descriptions.

This statistic shows a ranking of the estimated current healthcare spending per capita in 2020 in Africa, differentiated by country. The spending refers to the average current spending of both governments and consumers per inhabitant.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The current healthcare spending in Ghana was forecast to continuously increase between 2024 and 2029 by in total 1.1 billion U.S. dollars (+33.4 percent). After the fifth consecutive increasing year, the spending is estimated to reach 4.2 billion U.S. dollars and therefore a new peak in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. The spending refers to current spending of both governments and consumers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending in countries like Senegal and Ivory Coast.