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.

According to a survey conducted among healthcare providers in the United States in April 2021, ** percent of respondents reported that in their hospital or health systems artificial intelligence (AI)/machine learning efforts were in the pilot stage and the rollout was to be decided, while a further ** percent said that it is in the early stage initiatives.

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

In 2021, the AI and machine learning medical device market was valued at around *** billion U.S. dollars globally. By 2032, the market was forecast to increase to a value of **** billion U.S. dollars.

Introduction

AI in Healthcare Statistics: Artificial Intelligence (AI) in healthcare is growing rapidly, helping doctors and healthcare providers improve patient care. AI uses machines and algorithms to analyse data, such as medical records or images, to help diagnose diseases and suggest treatments faster and more accurately. AI technologies like machine learning, natural language processing, and robotic surgery are driving this growth.

AI helps in areas like medical imaging, drug discovery, and personalised treatment, making healthcare more efficient. This technology is transforming healthcare by reducing costs, speeding up diagnoses, and improving the accuracy of treatments, all while supporting healthcare professionals in delivering better care.

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.

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.

The intelligent health prediction market is experiencing robust growth, driven by the increasing prevalence of chronic diseases, advancements in artificial intelligence (AI) and machine learning (ML), and the rising adoption of wearable health technologies. The market, estimated at $15 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $60 billion by 2033. This expansion is fueled by several key factors. Firstly, the ability of AI-powered predictive models to analyze large datasets – including genomics, medical images, and wearables data – to identify individuals at high risk of developing specific conditions allows for proactive interventions, improving patient outcomes and reducing healthcare costs. Secondly, the growing emphasis on preventative healthcare and personalized medicine is driving demand for these solutions. Companies are leveraging AI to develop tailored prevention and treatment strategies, leading to improved health management and reduced hospitalizations. Finally, increased investments in research and development, coupled with the increasing accessibility of advanced computing capabilities, are further accelerating market growth. However, challenges remain. Data privacy and security concerns surrounding the use of sensitive patient data are significant hurdles. Regulatory compliance and the establishment of ethical guidelines for AI-driven health predictions are crucial for building trust and ensuring responsible innovation. Furthermore, the integration of these technologies into existing healthcare infrastructure can be complex and expensive, requiring significant investment in software, hardware, and training. Despite these challenges, the long-term growth prospects for the intelligent health prediction market remain strong, fueled by continuous technological advancements and a growing need for efficient and effective healthcare solutions. The market is segmented by technology (AI, ML, big data analytics), application (disease prediction, risk assessment, personalized medicine), and end-user (hospitals, clinics, research institutions). Key players like 23andMe, Verily Life Sciences, and others are driving innovation and competition within this rapidly evolving landscape.

The Artificial Intelligence (AI) in Healthcare market is experiencing explosive growth, projected to reach $2808.4 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 39.7% from 2025 to 2033. This surge is driven by several key factors. The increasing availability of large healthcare datasets, coupled with advancements in machine learning algorithms, fuels the development of sophisticated AI-powered diagnostic tools, personalized treatment plans, and predictive analytics. Furthermore, the rising prevalence of chronic diseases and the demand for improved efficiency and reduced healthcare costs are compelling healthcare providers and pharmaceutical companies to adopt AI solutions. Major players like Intel, Nvidia, Google, IBM, and Microsoft are heavily investing in AI healthcare technologies, fostering innovation and driving market expansion. The integration of AI is transforming various segments, including drug discovery, medical imaging analysis, patient monitoring, and administrative tasks, enhancing accuracy, speed, and overall effectiveness. The market's expansion is not limited to a few regions; global adoption is accelerating. Several challenges remain, including concerns around data privacy, regulatory hurdles, and the need for robust ethical guidelines to ensure responsible AI implementation in healthcare. However, the substantial benefits of improved patient outcomes, streamlined operations, and reduced healthcare expenditures far outweigh these challenges. The continued advancements in AI technologies, coupled with increased funding and collaborations, suggest that the AI in healthcare market will continue its trajectory of robust growth throughout the forecast period. The presence of established tech giants alongside innovative startups further indicates a dynamic and competitive landscape poised for continued expansion and innovation.

The global Healthcare AI market is experiencing rapid growth, driven by the increasing adoption of artificial intelligence in healthcare applications. While precise figures for market size and CAGR are unavailable in the provided data, we can reasonably infer significant expansion based on industry trends. Considering the involvement of major tech players like Apple, Google, IBM, and Microsoft, alongside specialized healthcare AI companies, the market is demonstrably substantial. The integration of AI in areas like diagnostics, drug discovery, personalized medicine, and administrative tasks is accelerating. This is fueled by the availability of large datasets, advancements in machine learning algorithms, and increasing investments in AI research and development. The market's growth is projected to continue at a significant rate for the forecast period (2025-2033). Let's assume, based on comparable market reports and the presence of major players, a 2025 market size of approximately $15 billion USD. A conservative estimate for CAGR, considering market maturity and potential challenges, would be around 20% for the period, leading to substantial market expansion within the next decade. This growth is, however, subject to factors such as regulatory hurdles, data privacy concerns, and the need for robust infrastructure to support AI deployment. The key segments within the Healthcare AI market include medical imaging analysis, disease prediction and diagnostics, drug discovery and development, personalized medicine, and administrative tasks like claims processing. Major market restraints include concerns over data security and privacy, the high cost of implementation and maintenance, the need for skilled professionals, and the ethical implications surrounding AI in healthcare. To overcome these challenges, collaborations between technology companies, healthcare providers, and regulatory bodies are crucial to ensure responsible AI deployment and maximize its benefits for patients and healthcare systems. The competitive landscape is dynamic, characterized by both established tech giants and emerging specialized companies vying for market share through innovation and strategic partnerships. The continued focus on improving healthcare accessibility, efficiency, and patient outcomes will undoubtedly be the key driver for further growth in the Healthcare AI sector.

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.

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.

Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records, and the treatment of chronic diseases.

MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.

The Process:

MedCamp employees / volunteers reach out to people and drive registrations.
During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.

Other things to note:

Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
For a few camps, there was hardware failure, so some information about date and time of registration is lost.
MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides  
information about several health issues through various awareness stalls.

Favorable outcome:

For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
You need to predict the chances (probability) of having a favourable outcome.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train
Test data is for all camps conducted on or after 1st April 2006.

Acknowledgements

Credits to AV

Inspiration

To share with the data science community to jump start their journey in Healthcare Analytics

The Artificial Intelligence (AI) Health Risk Management Platform market is experiencing robust growth, projected to reach $23.39 billion in 2025 and expanding at a Compound Annual Growth Rate (CAGR) of 19.8% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing prevalence of chronic diseases necessitates proactive risk management strategies, and AI offers powerful tools for predictive analytics, early detection, and personalized interventions. Furthermore, the rising adoption of electronic health records (EHRs) and the growing availability of large, anonymized health datasets fuel the development and application of AI-powered risk assessment models. The ability of AI to analyze complex patient data, identify high-risk individuals, and optimize resource allocation is proving invaluable for healthcare providers seeking to improve efficiency and reduce costs. Leading players like IBM, Health Catalyst, Verisk, Evolent, Optum, Ayasdi, Cleerly, and Health at Scale are driving innovation and market penetration through their advanced platforms and solutions. The market's growth also reflects a broader shift towards value-based care, where preventative measures and personalized risk management are prioritized. The market segmentation, while not explicitly provided, can be reasonably inferred. Key segments likely include solutions based on different AI techniques (e.g., machine learning, deep learning), deployment models (cloud-based vs. on-premise), and target user groups (hospitals, insurance companies, etc.). Geographic variations will also exist, with North America and Europe likely holding significant market share initially, followed by growth in Asia-Pacific and other regions as healthcare infrastructure improves and AI adoption increases. While regulatory hurdles and data privacy concerns present potential restraints, the overwhelming benefits of AI in improving patient outcomes and managing healthcare costs are expected to drive continued market expansion throughout the forecast period. The increasing investment in AI research and development further ensures the continuous improvement and sophistication of available platforms.

A 100,000-patient database that contains in total 100,000 virtual patients, 361,760 admissions, and 107,535,387 lab observations.

The global market for Machine Learning in Medical Imaging is experiencing robust growth, driven by the increasing adoption of AI-powered diagnostic tools and the rising prevalence of chronic diseases. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the ability of machine learning algorithms to analyze medical images (such as X-rays, CT scans, and MRIs) with greater speed and accuracy than human radiologists is significantly improving diagnostic capabilities and patient outcomes. Secondly, the increasing availability of large, high-quality medical image datasets is facilitating the development and training of more sophisticated machine learning models. Finally, regulatory approvals and increasing investments from both private and public sectors are further accelerating market penetration. The market is segmented by learning type (supervised, unsupervised, semi-supervised, reinforcement learning) and application (breast, lung, neurology, cardiovascular, liver, and others), with supervised learning currently dominating due to its proven effectiveness in image analysis. Major players like Zebra Medical Vision, Arterys, Aidoc, MaxQ AI, Google, Tencent, and Alibaba are actively shaping the landscape through innovation and strategic acquisitions. While data privacy and regulatory hurdles present some challenges, the overwhelming benefits in terms of improved healthcare efficiency and patient care are expected to propel sustained market expansion. The forecast period from 2025 to 2033 projects continued expansion, with a steady increase in market value driven by the ongoing technological advancements and broader adoption across various medical specialties. The Asia-Pacific region is expected to witness the fastest growth due to increasing healthcare infrastructure development and a large patient pool. North America will maintain a significant market share, reflecting the early adoption of these technologies and well-established healthcare systems. The continued development of specialized algorithms for specific disease types and the integration of machine learning into existing hospital workflows will further contribute to market growth. This overall positive trajectory suggests a bright future for Machine Learning in Medical Imaging, with significant potential to revolutionize healthcare diagnostics and treatment planning.

One out of two C-suite executives stated that Artificial Intelligence (AI) and Machine Learning (ML) were deployed in their healthcare organization, according to a survey conducted in the U.S. in 2019. In the surveyed upper market organizations, the implementation of AI and ML reached ** percent.

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.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

• 35694 rows
• 154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

• Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study
• Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities
• Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month
• Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.