Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

Use this layer to join non-spatial data: https://ph-lacounty.hub.arcgis.com/datasets/3e38574c3d31477d908c8028fb864ca4/aboutFor more information about the Community Health Profiles data initiative, please see the initiative homepage.

The SWAN Public Use Datasets provide access to longitudinal data describing the physical, biological, psychological, and social changes that occur during the menopausal transition. Data collected from 3,302 SWAN participants from Baseline through the 10th Annual Follow-Up visit are currently available to the public. Registered users are able to download datasets in a variety of formats, search variables and view recent publications.

This statistic shows the estimated number of mHealth app downloads worldwide from 2013 to 2017, in billions of downloads. It is estimated that in 2017 there will be *** billion mobile health app downloads.

This product presents comparable time-series data for a range of health indicators from a number of sources including the Canadian Community Health Survey, Vital Statistics, and Canadian Cancer Registry.

This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioural attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

The Health Information National Trends Survey (HINTS) is a biennial, cross-sectional survey of a nationally-representative sample of American adults that is used to assess the impact of the health information environment. The survey provides updates on changing patterns, needs, and information opportunities in health; Identifies changing communications trends and practices; Assesses cancer information access and usage; Provides information about how cancer risks are perceived; and Offers a testbed to researchers to test new theories in health communication.

Source:

Oresti Banos, Department of Computer Architecture and Computer Technology, University of Granada Rafael Garcia, Department of Computer Architecture and Computer Technology, University of Granada Alejandro Saez, Department of Computer Architecture and Computer Technology, University of Granada

Email to whom correspondence should be addressed: oresti '@' ugr.es (oresti.bl '@' gmail.com)

Data Set Information:

The MHEALTH (Mobile HEALTH) dataset comprises body motion and vital signs recordings for ten volunteers of the diverse profile while performing several physical activities. Sensors placed on the subject's chest, right wrist, and left ankle are used to measure the motion experienced by diverse body parts, namely, acceleration, rate of turn, and magnetic field orientation. The sensor positioned on the chest also provides 2-lead ECG measurements, which can be potentially used for basic heart monitoring, checking for various arrhythmias, or looking at the effects of exercise on the ECG.

DATASET SUMMARY:

• Activities: 12
• Sensor devices: 3
• Subjects: 10

EXPERIMENTAL SETUP

The collected dataset comprises body motion and vital signs recordings for ten volunteers of the diverse profile while performing 12 physical activities (Table 1). Shimmer2 [BUR10] wearable sensors were used for the recordings. The sensors were respectively placed on the subject's chest, right wrist, and left ankle and attached by using elastic straps (as shown in the figure in the attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn, and the magnetic field orientation, thus better capturing the body dynamics. The sensor positioned on the chest also provides 2-lead ECG measurements which are not used for the development of the recognition model but rather collected for future work purposes. This information can be used, for example, for basic heart monitoring, checking for various arrhythmias, or looking at the effects of exercise on the ECG. All sensing modalities are recorded at a sampling rate of 50 Hz, which is considered sufficient for capturing human activity. Each session was recorded using a video camera. This dataset is found to generalize to common activities of daily living, given the diversity of body parts involved in each one (e.g., the frontal elevation of arms vs. knees bending), the intensity of the actions (e.g., cycling vs. sitting and relaxing) and their execution speed or dynamicity (e.g., running vs. standing still). The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.

ACTIVITY SET

The activity set is listed in the following: L1: Standing still (1 min) L2: Sitting and relaxing (1 min) L3: Lying down (1 min) L4: Walking (1 min) L5: Climbing stairs (1 min) L6: Waist bends forward (20x) L7: Frontal elevation of arms (20x) L8: Knees bending (crouching) (20x) L9: Cycling (1 min) L10: Jogging (1 min) L11: Running (1 min) L12: Jump front & back (20x) NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).

A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the section “Citation Requests†.

Attribute Information:

The data collected for each subject is stored in a different log file: 'mHealth_subject.log'. Each file contains the samples (by rows) recorded for all sensors (by columns). The labels used to identify the activities are similar to the abovementioned (e.g., the label for walking is '4').

The meaning of each column is detailed next: Column 1: acceleration from the chest sensor (X-axis) Column 2: acceleration from the chest sensor (Y axis) Column 3: acceleration from the chest sensor (Z axis) Column 4: electrocardiogram signal (lead 1) Column 5: electrocardiogram signal (lead 2) Column 6: acceleration from the left-ankle sensor (X-axis) Column 7: acceleration from the left-ankle sensor (Y axis) Column 8: acceleration from the left-ankle sensor (Z axis) Column 9: gyro from the left-ankle sensor (X-axis) Column 10: gyro from the left-ankle sensor (Y axis) Column 11: gyro from the left-ankle sensor (Z axis) Column 13: magnetometer from the left-ankle sensor (X-axis) Column 13: magnetometer from the left-ankle sensor (Y axis) Column 14: magnetometer from the left-ankle sensor (Z axis) Column 15: acceleration from the right-lower-arm sensor (X-axis) Column 16: acceleration from the right-lower-arm sensor (Y axis) Column 17: acceleration from the right-lower-arm sensor (Z axis) Column 18: gyro from the right-lower-arm sensor (X-axis) Column 19: gyro from the right-lower-arm sensor (Y axis) Column 20: gyro fro...

Data downloads for health resources - organ donation and transplantation centers, shortage areas, health professions training programs, health center service delivery and look-alike sites, mental health, dental health, etc. Download metadata, Excel, and CSV files.

UK healthcare expenditure data by financing scheme, function and provider, and additional analyses produced to internationally standardised definitions.

Health and fitness apps recorded a download volume of over 3.6 billion in 2024. It represents an increase of over six percent compared to the previous examined year, when the health and wellness apps had around 3.4 billion downloads globally.

Home Health Agencies (HHA) provide at home skilled nursing, personal care and therapeutic services. Hospices provide palliative care and alleviate the physical, emotional, social and spiritual discomforts of an individual who is experiencing the last phases of life due to the existence of a terminal disease. In addition, hospices provide supportive care for the primary care giver and the family of the hospice patient. Home health agencies and hospices submit an annual utilization report to the Office at the end of each calendar year. The report includes information on services capacity, visits, utilization, patient characteristics, and capital/equipment expenditures, and gross revenues. The documentation, including report forms, is available for each reporting year.

CMS has released new information on Qualified Health Plan selections by county for the 37 states that use the HealthCare.gov platform (including the Federally-facilitated Marketplace, State Partnership Marketplaces and supported State-based Marketplaces) for the Marketplace open enrollment period from November 15, 2014 through February 15, 2015, including additional special enrollment period (SEP) activity reported through February 22, 2015. The data represent the number of unique individuals who have been determined eligible to enroll in a Qualified Health Plan and had selected a Marketplace plan by February 15, 2015 (including SEP activity through February 22).

The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined. NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI). While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

In June 2024, the Sweatcoin app was the most-downloaded health and fitness app in the Google Play Store worldwide. The app generated approximately 2.77 million downloads from Android users. Female health app Flo Period & Ovulation Tracker was the second-most popular app with over 2.5 million downloads from global Android users. Home Workout - No Equipment - which is published by the Leap Fitness Group ranked third with 1.93 million downloads from Android users in the last examined month.

The dataset contains state-wise National Family Health Survey (NFHS) compiled data on various family planning, childbirth, population, medical, health and other parameters which provide statistical indicators data on family profile and health status in India. There are 100+ indicators covered in the survey which broadly fall in the following categories: Health and Wellness, Maternal and Child Health, Family Planning and Reproductive Health, Disease Screening and Prevention, Social and Economic Factors, General Healthcare and Treatment

The different types of health data contained in the dataset include Anaemia among women and children, blood sugar levels and hypertension among men and women, tobacco and alcohol consumption among adults, delivery care and child feeding practices of women, quality of family planning services, screening of cancer among women, marriage and family, maternity care, nutritional status of women, child vaccinations and vitamin A supplementation, treatment of childhood diseases, etc.

Within these categories of health data, the dataset contains indicators data such as births attended by skilled health care professionals and caesarean section, number of children with under and heavy weight, stunted growth, their different vaccations status, male and female sterilization, consumption of iron folic acid among mothers, mother who had antenatal, postnatal, neonatal services, women who are obese and at the risk of weight to hip ratio, educational status among women and children, sanitation, birth and sex ratio, etc.

All of the data is compiled from the NFHS 4th and 5th survey reports. The The NFHS is a collaborative project of the International Institute for Population Sciences(IIPS), aimed at providing health data to strengthen India's health policies and programmes.

There are 100+ indicators covered in the survey which broadly fall in the following categories: Health and Wellness, Maternal and Child Health, Family Planning and Reproductive Health, Disease Screening and Prevention, Social and Economic Factors, General Healthcare and Treatment

Users can customize how data on a number of health indicators are presented, and the resulting tables, charts, and maps can be downloaded. Entire datasets are also available to download. Background Global Health Facts is a Kaiser Family Foundation website that provides global health data on the following topics: HIV/ AIDS; TB; Malaria; Other conditions, diseases and risk indicators; Programs, funding and financing; Health workforce and capacity; Demography and population; Income and the Economy. User Functionality Raw data (by topic) can be downloaded or users can create customized reports, charts, graphs or tables to compare 2 or more countries on different health indicators. Specific profiles for just one country or for one health topic can also be generated. Users can view data as a table, chart or map. Rankings of countries are also available. Data Notes Data sources include UNAIDS, WHO, and the CIA and links to the specific source is provided. Annual data is updated as it comes available. The most recent data is from 2009 (However this varies by exposure), and the site does not specify when new data becomes available.

Health and fitness apps recorded around 521 million downloads in the United States in 2024. This represents a slight decrease in download volume compared to the previous examined year, when health apps registered approximately 532 million downloads from the region. The downloads of mobile apps in the health industry peaked in 2020, due to the unique circumstances brought by the global COVID-19 pandemic.

#Trending Datasets on Kaggle https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2Ffee4ca9db2f7052467377d6bb9ba4558%2FScreenshot%20(117).png?generation=1742392357908359&alt=media" alt=""> This study explores healthcare call data from April 2016 to February 2025 to understand patterns in healthcare service utilization during periods of emergency. Specifically, it examines the fluctuations in the total number of calls, including doctor consultations, health information requests, ambulance services, complaints, and inquiries about services. This analysis aims to evaluate the effectiveness of emergency response systems, focusing on how healthcare systems manage surges in demand during high-stress periods. By investigating spikes in call volumes, particularly during emergency periods, the study provides insights into the healthcare system's ability to manage such crises and the areas that require improvement. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F7722c3079e37a81d1b0ab3190809c0d9%2Fdownload.png?generation=1742263805106741&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2Ff019dcb1f849d238ce42d88c731b5870%2Fdownload%20(1).png?generation=1742263820704627&alt=media" alt="">

Healthcare systems face significant pressure during periods of emergency, such as natural disasters, public health crises, and other urgent health-related events. These emergencies lead to a surge in demand for medical services, placing a strain on healthcare providers. Call centers, as a vital component of healthcare systems, become critical hubs where individuals reach out for assistance. Analyzing the data of calls received during these emergency times provides valuable insights into how well healthcare systems respond to such crises.

This study analyzes healthcare call data spanning from April 2016 to February 2025, focusing on several key categories: Total Number of Calls, Total Number of Doctors Consultancy, Number of Total Health Information, Number of Total Ambulance Information, Number of Total Complaints, and Number of Calls to Know About The Service [1]. By understanding these call patterns, the study highlights the response effectiveness of healthcare systems and identifies areas for improvement, especially during emergency times when healthcare needs surge dramatically. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F14db04dec445002bea40263ebc8a4463%2FScreenshot%20(121).png?generation=1742361733208511&alt=media" alt=""> Dataset Description The dataset contains aggregated healthcare call data for each month from April 2016 to February 2025. The data is categorized into the following key columns: • Total Number of Calls: The total number of calls received across all service categories in a given month. • Total Number of Doctors Consultancy: The number of calls made by individuals seeking consultations with doctors. • Number of Total Health Information: The number of calls requesting general health information. • Number of Total Ambulance Information: The number of calls related to ambulance services. • Number of Total Complaints: The total number of complaints regarding the healthcare service. • Number of Calls To Know About The Service: The number of calls made by individuals seeking information about available healthcare services. These categories allow for a comprehensive analysis of how different aspects of healthcare services are impacted during periods of high demand, such as during emergencies. Methodology The analysis follows several steps to process and interpret the data: 1. Data Preprocessing: o Cleaning the dataset by ensuring there are no missing or irrelevant entries. o Converting the Year and Month columns to a Date Time format to facilitate chronological analysis. 2. Descriptive Analysis: o Calculating the total number of calls for each month to identify general trends. o Identifying peak periods of call volumes that might correspond to emergency periods. 3. Trend Analysis: o Visualizing the data through time-series plots to observe monthly trends in each of the categories. o Identifying any significant spikes in call volumes during specific months, which could indicate periods of emergency. 4. Evaluation of Response Effectiveness: o Analyzing the differences in call volumes and service delays during periods with higher demand. o Examining whether the healthcare system could meet the demands during emergency times, particularly in terms of response time and service availability. The data analysis revealed several notable trends: 4.1 Surge in Call Volumes During Emergency Periods Emergency periods showed marked increases in call volumes across all categories. A significant spi...

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description_LE_ Sex_Race_Ethnicity.pdf