Explore our synthetic healthcare dataset designed for machine learning, data science, and healthcare analytics.

Heterogenous Big dataset is presented in this proposed work: electrocardiogram (ECG) signal, blood pressure signal, oxygen saturation (SpO2) signal, and the text input. This work is an extension version for our relevant formulating of dataset that presented in [1] and a trustworthy and relevant medical dataset library (PhysioNet [2]) was used to acquire these signals. The dataset includes medical features from heterogenous sources (sensory data and non-sensory). Firstly, ECG sensor’s signals which contains QRS width, ST elevation, peak numbers, and cycle interval. Secondly: SpO2 level from SpO2 sensor’s signals. Third, blood pressure sensors’ signals which contain high (systolic) and low (diastolic) values and finally text input which consider non-sensory data. The text inputs were formulated based on doctors diagnosing procedures for heart chronic diseases. Python software environment was used, and the simulated big data is presented along with analyses.

Deep Learning in Healthcare Market Outlook

The global deep learning in healthcare market size was valued at approximately $2.8 billion in 2023 and is projected to reach around $13.7 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 19.4% during the forecast period. The rapid integration of artificial intelligence (AI) and machine learning technologies in healthcare systems, alongside advancements in computational power and data availability, are significant growth drivers for the market.

One of the primary growth factors for the deep learning in healthcare market is the increasing demand for efficient and accurate diagnostic tools. Deep learning algorithms have demonstrated superior performance in interpreting medical images, detecting anomalies, and predicting outcomes compared to traditional methods. This has led to widespread adoption in medical imaging, significantly enhancing diagnostic precision and reducing the burden on healthcare professionals. The ever-increasing volume of healthcare data, coupled with the need for quick and accurate decision-making, further propels the market forward. By leveraging large datasets, deep learning can achieve a level of precision and speed unattainable by human capabilities alone.

Another significant driver is the growing emphasis on personalized medicine. Deep learning enables the analysis of complex biological data, aiding in the development of personalized treatment plans tailored to individual patient profiles. This shift towards precision medicine is transforming patient care, allowing for more effective treatment protocols and better patient outcomes. The pharmaceutical industry, in particular, is investing heavily in deep learning technologies to expedite drug discovery and development processes, thereby reducing time-to-market and costs associated with bringing new drugs to consumers.

The adoption of electronic health records (EHRs) and the integration of AI in healthcare administration are also crucial growth factors. Deep learning algorithms can process vast amounts of patient data stored in EHRs to identify patterns and predict disease outbreaks, optimize resource allocation, and enhance patient management. The demand for streamlined operations and improved patient care is driving healthcare providers to incorporate these advanced technologies. Furthermore, the ongoing advancements in computational power and the availability of high-quality healthcare datasets are crucial enablers for the application of deep learning technologies in various healthcare domains.

Computer Vision in Healthcare is revolutionizing the way medical professionals approach diagnostics and treatment planning. By leveraging advanced image processing algorithms, computer vision can analyze medical images with remarkable accuracy, identifying patterns and anomalies that might be missed by the human eye. This technology is not only enhancing the precision of medical imaging but also enabling the development of automated systems that assist radiologists in interpreting complex datasets. The integration of computer vision in healthcare is streamlining workflows, reducing diagnostic errors, and ultimately improving patient outcomes. As the technology continues to evolve, its applications are expanding beyond imaging to include areas such as surgery, pathology, and patient monitoring, offering a comprehensive toolset for modern healthcare delivery.

On the regional front, North America holds the largest share of the deep learning in healthcare market, driven by substantial investments in AI technology, well-established healthcare infrastructure, and supportive government initiatives. The region's focus on technological innovation and its robust research ecosystem are key factors contributing to market growth. Moreover, the presence of leading AI and healthcare companies in North America accelerates the adoption of deep learning technologies. Europe and Asia Pacific are also witnessing significant growth, with the latter expected to exhibit the highest CAGR during the forecast period due to increasing healthcare digitization and rising investments in AI-driven healthcare solutions.

Component Analysis

The deep learning in healthcare market is segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market owing to continuous advancements in AI algorithms and the development of sophisticated software solutions tailored for healthcar

The global Artificial Intelligence (AI) Training Dataset market is experiencing robust growth, driven by the increasing adoption of AI across diverse sectors. The market's expansion is fueled by the burgeoning need for high-quality data to train sophisticated AI algorithms capable of powering applications like smart campuses, autonomous vehicles, and personalized healthcare solutions. The demand for diverse dataset types, including image classification, voice recognition, natural language processing, and object detection datasets, is a key factor contributing to market growth. While the exact market size in 2025 is unavailable, considering a conservative estimate of a $10 billion market in 2025 based on the growth trend and reported market sizes of related industries, and a projected CAGR (Compound Annual Growth Rate) of 25%, the market is poised for significant expansion in the coming years. Key players in this space are leveraging technological advancements and strategic partnerships to enhance data quality and expand their service offerings. Furthermore, the increasing availability of cloud-based data annotation and processing tools is further streamlining operations and making AI training datasets more accessible to businesses of all sizes. Growth is expected to be particularly strong in regions with burgeoning technological advancements and substantial digital infrastructure, such as North America and Asia Pacific. However, challenges such as data privacy concerns, the high cost of data annotation, and the scarcity of skilled professionals capable of handling complex datasets remain obstacles to broader market penetration. The ongoing evolution of AI technologies and the expanding applications of AI across multiple sectors will continue to shape the demand for AI training datasets, pushing this market toward higher growth trajectories in the coming years. The diversity of applications—from smart homes and medical diagnoses to advanced robotics and autonomous driving—creates significant opportunities for companies specializing in this market. Maintaining data quality, security, and ethical considerations will be crucial for future market leadership.

This repository contains two healthcare datasets in Hindi and Punjabi, translated from English. The datasets cover medical diagnoses, disease names, and related healthcare information. The data has been carefully cleaned and formatted to ensure accuracy and usability for various applications, including machine learning, NLP, and healthcare analysis.

The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

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1) Data Introduction • The Healthcare Dataset is a synthetic dataset designed to mimic real-world healthcare data for data science, machine learning, and data analysis purposes. It includes patient information, medical conditions, admission details, and healthcare services provided. This dataset is ideal for developing and testing healthcare predictive models, practicing data manipulation techniques, and creating data visualizations.

2) Data Utilization (1) Healthcare data has characteristics that: • It includes detailed patient information such as age, gender, blood type, medical condition, and admission details. This information can be used to analyze healthcare trends, patient demographics, and the effectiveness of medical treatments. (2) Healthcare data can be used to: • Predictive Modeling: Helps in developing models to predict patient outcomes, treatment success rates, and disease progression. • Healthcare Analytics: Assists in analyzing patient data to identify patterns, improve patient care, and optimize resource allocation. • Educational Purposes: Supports learning and teaching data science concepts in a healthcare context, providing realistic data for experimentation and practice.

This dataset is based on train and test dataset from this competition: https://www.kaggle.com/competitions/widsdatathon2024-challenge1 .

What did I change? 1. I dropped 2 columns that contained to little data.
2. using Machine Learning I imputed "payer_type", "patient_race" and "bmi". 3. using "patient_zip3" I filled missing values in "patient_state" , "Region" and "Division" 4. using SinmpleImputer I imputed few missing numeric data in "Ozone", "PM2.5" and other columns 5. I created some new features, based on demographic features, that may be a bit more informative. 6. I tokenized the 'breast_cancer_diagnosis_desc' column

If you're interested how I did that check those notebooks: https://www.kaggle.com/code/anopsy/ml-for-missing-values for "bmi" and new features check this: https://www.kaggle.com/code/anopsy/fe-and-xgb-on-clean-data

According to the description of the original dataset, it's a "39k record dataset (split into training and test sets) representing patients and their characteristics (age, race, BMI, zip code), their diagnosis and treatment information (breast cancer diagnosis code, metastatic cancer diagnosis code, metastatic cancer treatments, … etc.), their geo (zip-code level) demographic data (income, education, rent, race, poverty, …etc), as well as toxic air quality data (Ozone, PM25 and NO2)."

• Numerous language-centric research on healthcare is conducted day by day. To address shortcomings of Arabic natural language generation models, we introduce a large Arabic Healthcare Dataset (AHD) of textual data. For this motivation, we named our dataset ‘AHD’.

• The largest Arabic Healthcare Dataset (AHD) as we know was collected from altibbi website.

• The AHD consists of more than 808k Question and Answer into 90 variety categories. The AHD contains one file, and the file description will be discussed here. One file is the actual data which is in Arabic language.

  • AHD.xlsx file contains dataset in excel format, which includes the question, answer, and category in Arabic.

  • AHD_english.xlsx file contains dataset in excel format, which includes the question, answer, and category translated to English.

• Distribution of Question and Answer per category.xlsex shows the distribution of the data set by category.

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This dataset is a collection of articles indexed in the Web of Science database, used for a bibliometric article on the topic Data Collection and Analysis Systems Using Machine Learning in Internet of Things. The main idea is to identify articles related to the theme through bibliometric techniques and perform analyses using tools such as VOSviewer and CiteNetExplorer to support the state of the art.

BackgroundResearch related to Artificial Intelligence (AI) in healthcare applications is evolving. It is essential to incorporate collaborative learning from published research to comprehend the challenges and accessibility of opportunities when integrating AI in healthcare systems. To investigate the role of AI, a qualitative and quantitative year in review study was conducted, encompassing the evaluation of literature published in 2024 to gain insight into the recent advancements of the field.MethodsTo find research articles about integrating new AI technologies into healthcare systems, a PubMed search using the terms “2024”, “artificial intelligence”, and “large language models” was conducted. The search was restricted to human subject research and used a deep-learning-based approach to assess the reliability of publications as of December 31, 2024 on January 1, 2025. In addition, for each publication, each mature article was manually annotated for the AI model type (e.g., LLM, DL, ML), healthcare specialty, and the data type used (image, text, tabular, or audio).Additionally,qualitative and quantitative analyses were performed to illuminate statistics and trends of combined published articles.ResultsOur PubMed search yielded 28,180 total articles; 1,693 were initially labeled mature, after which 1,551 articles were analyzed after exclusions. Similar to the prior years, we excluded systematic reviews in the final analysis and were excluded in this year's dataset.The most prevalent specialties within our PubMed search originated from imaging (407), head and neck (127), and General (122). Analysis of AI model types showed that the Large Language Model (LLM) was the most popular utilized in 479 publications, followed by AI General (448), and DL (372). Qualitative data was obtained on the data types, and it was revealed that the image data was predominant and used in 57.0% of the mature sources, followed by text (33.1%), followed by tabular (7.59%). The utilization of Large Language Models (LLMs) is the highest in publications associated with education at 18.6%, followed by General at 13.6%. These results indicate that LLMs are frequently applied in educational contexts and administrative tasks amongst the healthcare specialties for research.ConclusionHealthcare specialties, including imaging, head and neck, and general medicine, have taken over the realm of AI in healthcare. Other specialties that distinctive types of AI and LLMs could likely drive in the future include education, pathology, as well as surgery. It is essential to use a collaborative approach to investigate the multimodal models of AI in healthcare applications to provide a thorough encapsulation of AI in healthcare.Data Files DescriptionOne data file is provided, which illustrates the annotations of the mature sources used in our review. The first file is named Annotated_OnlyMature_Unique_2024_YIR_All_Publications - Annotated_OnlyMature_Unique_2024_YIR_All_Publications and includes ‘Title’, ‘DOI’, ‘Abstract’, ‘Author Address’, ‘Specialty’, ‘Model’, and 'Data Type’. The ‘Specialty’, ‘Model’, and ‘Data Type’ were predominantly analyzed by the BrainXAI research team to produce our meta-analysis of the mature sources of AI. This year we have excluded systematic reviews from the dataset compared to the 2023 year in review dataset, but can be provided on request.

Unlock valuable biomedical knowledge with our comprehensive PubMed Dataset, designed for researchers, analysts, and healthcare professionals to track medical advancements, explore drug discoveries, and analyze scientific literature.

Dataset Features

Scientific Articles & Abstracts: Access structured data from PubMed, including article titles, abstracts, authors, publication dates, and journal sources. Medical Research & Clinical Studies: Retrieve data on clinical trials, drug research, disease studies, and healthcare innovations. Keywords & MeSH Terms: Extract key medical subject headings (MeSH) and keywords to categorize and analyze research topics. Publication & Citation Data: Track citation counts, journal impact factors, and author affiliations for academic and industry research.

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Pharmaceutical Research & Drug Development: Analyze clinical trial data, drug efficacy studies, and emerging treatments. Medical & Healthcare Intelligence: Track disease outbreaks, healthcare trends, and advancements in medical technology. AI & Machine Learning Applications: Use structured biomedical data to train AI models for predictive analytics, medical diagnosis, and literature summarization. Academic & Scientific Research: Access a vast collection of peer-reviewed studies for literature reviews, meta-analyses, and academic publishing. Regulatory & Compliance Monitoring: Stay updated on medical regulations, FDA approvals, and healthcare policy changes.

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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.

The global healthcare data annotation tools market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) in healthcare. This surge is fueled by the need for accurate and high-quality annotated data to train sophisticated algorithms for applications like medical image analysis, diagnostic support, drug discovery, and personalized medicine. While precise market sizing data wasn't provided, considering the rapid expansion of AI in healthcare and the crucial role of data annotation, a reasonable estimate for the 2025 market size would be around $800 million, growing at a Compound Annual Growth Rate (CAGR) of approximately 25% during the forecast period (2025-2033). This projected CAGR reflects the increasing demand for AI-powered healthcare solutions and the consequential need for robust data annotation tools. Factors contributing to this growth include advancements in deep learning techniques, rising investments in AI healthcare startups, and the growing availability of large healthcare datasets. However, market expansion faces challenges. High costs associated with annotation, the need for specialized expertise to handle complex medical data, and concerns regarding data privacy and security are significant restraints. To overcome these challenges, the industry is witnessing a shift towards automation and semi-automated annotation tools, and cloud-based platforms that improve scalability and data security. Key segments within the market include tools for image annotation (medical images, pathology slides), text annotation (patient records, clinical notes), and audio annotation (patient voice recordings). Companies like Infosys, Shaip, and others are leading the charge in developing innovative solutions to meet the burgeoning demand. The continued growth trajectory is expected to lead to significant market expansion, exceeding $5 billion by 2033.

The AI in Healthcare Technology market is experiencing robust growth, driven by the increasing adoption of AI-powered solutions across various healthcare segments. The market's expansion is fueled by several factors, including the rising prevalence of chronic diseases, the need for improved diagnostic accuracy, the demand for personalized medicine, and the increasing availability of large healthcare datasets suitable for AI training. Technological advancements, such as the development of more sophisticated algorithms and the reduction in computational costs, are further accelerating market penetration. While data privacy concerns and regulatory hurdles present challenges, the potential for enhanced patient care and operational efficiencies is driving significant investment and innovation within the sector. Key players like Siemens Healthcare, GE Healthcare, and IBM Watson Health are leading the charge, developing and deploying AI solutions for medical imaging analysis, drug discovery, and precision medicine. The market is segmented by application (e.g., diagnostics, drug discovery, treatment planning) and by technology (e.g., machine learning, deep learning, natural language processing). We project a continued strong CAGR, reflecting the sustained momentum of this transformative technology in revolutionizing healthcare delivery. The forecast period (2025-2033) anticipates continued expansion, though the rate of growth may slightly moderate as the market matures. However, emerging applications of AI in areas like remote patient monitoring, predictive analytics for hospital resource allocation, and robotic surgery promise to sustain long-term growth. Competitive pressures will intensify as more companies enter the market, leading to product differentiation and a focus on developing specialized AI solutions tailored to specific healthcare needs. The successful integration of AI into existing healthcare infrastructure will be crucial for realizing the full potential of this technology. Factors such as interoperability challenges and the need for robust data security protocols will remain important considerations for market players and regulators alike. Despite these challenges, the long-term outlook for the AI in Healthcare Technology market remains highly positive, indicating significant opportunities for growth and innovation.

This dataset is a synthetic version inspired by the original "Stroke Prediction Dataset" on Kaggle. It contains anonymized, artificially generated data intended for research and model training on healthcare-related stroke prediction. The dataset generated using GPT-4o contains 50,000 records and 12 features. The target variable is stroke, a binary classification where 1 represents stroke occurrence and 0 represents no stroke. The dataset includes both numerical and categorical features, requiring preprocessing steps before analysis. A small portion of the entries includes intentionally introduced missing values to allow users to practice various data preprocessing techniques such as imputation, missing data analysis, and cleaning. The dataset is suitable for educational and research purposes, particularly in machine learning tasks related to classification, healthcare analytics, and data cleaning. No real-world patient information was used in creating this dataset.

The global Machine Learning in Medicine market is experiencing robust growth, projected to reach $[Estimated 2025 Market Size in Millions] in 2025 and expand at a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033. This significant expansion is fueled by several key drivers. The increasing availability of large, high-quality medical datasets, coupled with advancements in computing power and algorithm development, is enabling the creation of sophisticated machine learning models capable of enhancing diagnostic accuracy, accelerating drug discovery, and personalizing patient care. Furthermore, the rising prevalence of chronic diseases and the increasing demand for efficient and cost-effective healthcare solutions are bolstering the adoption of machine learning across various medical applications. Key trends within the market include the growing integration of AI-powered diagnostic tools, the rise of federated learning for protecting patient privacy while leveraging diverse datasets, and the expansion of machine learning applications into areas like personalized medicine and preventive healthcare. While data privacy and regulatory concerns pose challenges, the transformative potential of machine learning in improving healthcare outcomes is driving significant investment and innovation in this rapidly evolving market. The market segmentation reveals a strong focus on supervised learning techniques due to their effectiveness in tackling specific medical problems with labeled data. However, unsupervised learning and reinforcement learning are gaining traction, offering the potential for identifying novel patterns and optimizing treatment strategies, respectively. Application-wise, diagnosis and drug discovery currently lead the market, although other applications, including predictive modeling for risk assessment and personalized treatment plans, are showing considerable promise. Leading companies like Google, BioBeats, Jvion, and others are actively shaping the market landscape through their advanced technologies and strategic partnerships. Geographical distribution shows strong growth in North America and Europe, driven by advanced healthcare infrastructure and regulatory frameworks. However, emerging markets in Asia-Pacific are rapidly gaining ground due to increasing healthcare investment and a rising prevalence of diseases. The forecast period suggests continued expansion, particularly driven by the ongoing improvements in AI algorithms and the wider adoption across healthcare settings. We anticipate substantial growth across all segments driven by technological breakthroughs and a growing awareness of the clinical benefits.