Dates of completed IT Health Checks for the Food Standards Agency.

Physical Health Checks for People with Severe Mental Illness , Q2 2024-25

Abstract Prognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems. A major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of "no trouble found" events that have become a significant warranty-cost issue. Bios Aleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research. Anton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.

Determinants of individuals’ intention to undergo health checks without adjusted for sociodemographic data.

The data is presented by the ACT Government for the purpose of disseminating information for the benefit of the public. The ACT Government has taken great care to ensure the information in this report is as correct and accurate as possible. While the information is considered to be true and correct at the date of publication, changes in circumstances after the time of publication may impact on the accuracy of the information. Differences in statistical methods and calculations, data updates and guidelines may result in the information contained in this report varying from previously published information.

Dates of completed IT Health Checks for the Food Standards Agency.

The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.

Health checks contain a buyer behaviour log (containing customer and product purchase details), Product performance records and monthly dashboard.

About the Dataset This dataset comprises anonymized health records of 60,000 patients, collected from multiple reputed medical institutions as part of a broader Internet of Medical Things (IoMT) data initiative. Each record includes vital physiological parameters essential for monitoring cardiovascular and respiratory health. The dataset is designed to support research and development in healthcare analytics, remote patient monitoring, and IoMT-based diagnostic systems. Key Points Type: Synthetic healthcare monitoring dataset simulating IoMT-based patient data.

Purpose: Designed to mimic real-world vital sign measurements, AI predictions, and alert generation.

Completeness: No missing values; all records are complete and clean.

Format: CSV file with mixed numeric and categorical data types.

Dataset Features Patient Number – Unique identifier for each patient record.

Heart Rate (bpm) – Beats per minute reading.

SpO₂ Level (%)– Blood oxygen saturation percentage.

Systolic Blood Pressure (mmHg)– Systolic blood pressure value.

Diastolic Blood Pressure (mmHg) – Diastolic blood pressure value.

Body Temperature (°C) – Body temperature in Celsius.

Fall Detection– Indicates whether a fall was detected (Yes/No).

Predicted Disease – AI-predicted medical condition.

Data Accuracy (%) – Model’s prediction confidence.

Heart Rate Alert – Status: NORMAL / ABNORMAL.

SpO₂ Level Alert – Status: NORMAL / ABNORMAL.

Blood Pressure Alert – Status: NORMAL / ABNORMAL.

Temperature Alert – Status: NORMAL / ABNORMAL.

Total Records 60,000 records

13 attributes (6 numerical, 7 categorical)

Data Source Origin: Synthetic data generated for research and educational purposes.

Provenance: Simulates readings from IoT-enabled health monitoring devices (e.g., wearable sensors, medical monitors).

Note: Not based on real patients; avoids privacy concerns while preserving realistic patterns.

Application Domain Internet of Medical Things (IoMT) and AI-driven healthcare systems.

Possible uses:

Chronic disease monitoring (e.g., diabetes, hypertension, asthma).

Predictive modeling for early diagnosis.

Alert-based anomaly detection in vitals.

Simulation for IoT and healthcare research.

Testing real-time health monitoring dashboards.

This dataset includes the number of blood sugar and blood pressure screenings, cholesterol, community resource referrals, and health presentations performed by Austin Public Health's Health Equity Unit. The dataset is broken down by race/ethnicity and gender.

Generic health data commonly collected during regular health checks. It provides a suitable and adjustable framework for extensive variety of uses, such as analysis, testing, simulation and algorithm development.

The Community Health Assessment (CHA) is a systematic assessment of population health in Philadelphia, highlighting key public health challenges and assets and informing local public health programs, policies, and partnerships. The CHA includes indicators reflecting health behaviors, health conditions, health care factors, and social and environmental determinants of health. The Philadelphia Department of Public Health publishes an annual report of the analyses, linked to under the 'Related' tab. Additionally, they have released an online, interactive version of the CHA, known as the Community Health Explorer, to make the data more accessible to a broader audience.

The National Animal Health Monitoring System (NAHMS) Program Unit conducts national studies on the health, management, and productivity of United States domestic livestock and poultry populations.

BackgroundThe National Health Service (NHS) Health Check programme was introduced in 2009 in England to systematically assess all adults in midlife for cardiovascular disease risk factors. However, its current benefit and impact on health inequalities are unknown. It is also unclear whether feasible changes in how it is delivered could result in increased benefits. It is one of the first such programmes in the world. We sought to estimate the health benefits and effect on inequalities of the current NHS Health Check programme and the impact of making feasible changes to its implementation.Methods and findingsWe developed a microsimulation model to estimate the health benefits (incident ischaemic heart disease, stroke, dementia, and lung cancer) of the NHS Health Check programme in England. We simulated a population of adults in England aged 40–45 years and followed until age 100 years, using data from the Health Survey of England (2009–2012) and the English Longitudinal Study of Aging (1998–2012), to simulate changes in risk factors for simulated individuals over time. We used recent programme data to describe uptake of NHS Health Checks and of 4 associated interventions (statin medication, antihypertensive medication, smoking cessation, and weight management). Estimates of treatment efficacy and adherence were based on trial data. We estimated the benefits of the current NHS Health Check programme compared to a healthcare system without systematic health checks. This counterfactual scenario models the detection and treatment of risk factors that occur within ‘routine’ primary care. We also explored the impact of making feasible changes to implementation of the programme concerning eligibility, uptake of NHS Health Checks, and uptake of treatments offered through the programme. We estimate that the NHS Health Check programme prevents 390 (95% credible interval 290 to 500) premature deaths before 80 years of age and results in an additional 1,370 (95% credible interval 1,100 to 1,690) people being free of disease (ischaemic heart disease, stroke, dementia, and lung cancer) at age 80 years per million people aged 40–45 years at baseline. Over the life of the cohort (i.e., followed from 40–45 years to 100 years), the changes result in an additional 10,000 (95% credible interval 8,200 to 13,000) quality-adjusted life years (QALYs) and an additional 9,000 (6,900 to 11,300) years of life. This equates to approximately 300 fewer premature deaths and 1,000 more people living free of these diseases each year in England. We estimate that the current programme is increasing QALYs by 3.8 days (95% credible interval 3.0–4.7) per head of population and increasing survival by 3.3 days (2.5–4.1) per head of population over the 60 years of follow-up. The current programme has a greater absolute impact on health for those living in the most deprived areas compared to those living in the least deprived areas (4.4 [2.7–6.5] days of additional quality-adjusted life per head of population versus 2.8 [1.7–4.0] days; 5.1 [3.4–7.1] additional days lived per head of population versus 3.3 [2.1–4.5] days). Making feasible changes to the delivery of the existing programme could result in a sizable increase in the benefit. For example, a strategy that combines extending eligibility to those with preexisting hypertension, extending the upper age of eligibility to 79 years, increasing uptake of health checks by 30%, and increasing treatment rates 2.5-fold amongst eligible patients (i.e., ‘maximum potential’ scenario) results in at least a 3-fold increase in benefits compared to the current programme (1,360 premature deaths versus 390; 5,100 people free of 1 of the 4 diseases versus 1,370; 37,000 additional QALYs versus 10,000; 33,000 additional years of life versus 9,000). Ensuring those who are assessed and eligible for statins receive statins is a particularly important strategy to increase benefits. Estimates of overall benefit are based on current incidence and management, and future declines in disease incidence or improvements in treatment could alter the actual benefits observed in the long run. We have focused on the cardiovascular element of the NHS Health Check programme. Some important noncardiovascular health outcomes (e.g., chronic obstructive pulmonary disease [COPD] prevention from smoking cessation and cancer prevention from weight loss) and other parts of the programme (e.g., brief interventions to reduce harmful alcohol consumption) have not been modelled.ConclusionsOur model indicates that the current NHS Health Check programme is contributing to improvements in health and reducing health inequalities. Feasible changes in the organisation of the programme could result in more than a 3-fold increase in health benefits.

This dataset contains the predicted prices of the asset HEALTHCHECKS github.com/healthchecks/HEALTHCHECKS over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Number of NHS Health Checks offered and uptake each quarter, for the year to date and over five years April 2013-March 2018 Source agency: Public Health England Designation: Official Statistics not designated as National Statistics Language: English Alternative title: NHS Health Check quarterly data returns

This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.

Health Checkup Result

Health Checkup Result of 19,000,000 people in South Korea, 2002~2020

• GitHub Link
• Source Link

In Korea, everyone is compulsorily required to join the National Health Insurance. The National Health Insurance Service (NHIS), which manages national health insurance, provides basic health checkups to subscribers every year.

This dataset is the result of a random sampling of 1 million people per year from 2002 to 2021 among those who underwent basic health checkups provided by the NHIS.

Missing values are those that have not been selectively tested by individuals.

This dataset consists of a total of 19 csv files for each year, and each csv file contains only the health checkup results for that year. There are differences in the features of the dataset by year.

1. Difference in the number of columns

2. There are features that have been excluded or added by year.

3. Difference in AREA_CODE

4. After 2012, a new area, 'SEAJONG' was named and a new area code, 36, was added.
   

5. Categorization differences in AGE_GROUP

6. There is a difference in age categorization criteria between 2002 and 2013 and the dataset after 2014.

A description of each column is as follows.

Feature Description