Patients Table:

• PatientID: Unique identifier for each patient.
• firstname: First name of the patient.
• lastname: Last name of the patient.
• email: Email address of the patient.

This table stores information about individual patients, including their names and contact details.

Doctors Table:

• DoctorID: Unique identifier for each doctor.
• DoctorName: Full name of the doctor.
• Specialization: Area of medical specialization.
• DoctorContact: Contact details of the doctor.

This table contains details about healthcare providers, including their names, specializations, and contact information.

Appointments Table:

• AppointmentID: Unique identifier for each appointment.
• Date: Date of the appointment.
• Time: Time of the appointment.
• PatientID: Foreign key referencing the Patients table, indicating the patient for the appointment.
• DoctorID: Foreign key referencing the Doctors table, indicating the doctor for the appointment.

This table records scheduled appointments, linking patients to doctors.

MedicalProcedure Table:

• ProcedureID: Unique identifier for each medical procedure.
• ProcedureName: Name or description of the medical procedure.
• AppointmentID: Foreign key referencing the Appointments table, indicating the appointment associated with the procedure.

This table stores details about medical procedures associated with specific appointments.

Billing Table:

• InvoiceID: Unique identifier for each billing transaction.
• PatientID: Foreign key referencing the Patients table, indicating the patient for the billing transaction.
• Items: Description of items or services billed.
• Amount: Amount charged for the billing transaction.

This table maintains records of billing transactions, associating them with specific patients.

demo Table:

• ID: Primary key, serves as a unique identifier for each record.
• Name: Name of the entity.
• Hint: Additional information or hint about the entity.

This table appears to be a demonstration or testing table, possibly unrelated to the healthcare management system.

This dataset schema is designed to capture comprehensive information about patients, doctors, appointments, medical procedures, and billing transactions in a healthcare management system. Adjustments can be made based on specific requirements, and additional attributes can be included as needed.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

Problem Statement

👉 Download the case studies here

Hospitals and healthcare providers faced challenges in ensuring continuous monitoring of patient vitals, especially for high-risk patients. Traditional monitoring methods often lacked real-time data processing and timely alerts, leading to delayed responses and increased hospital readmissions. The healthcare provider needed a solution to monitor patient health continuously and deliver actionable insights for improved care.

Challenge

Implementing an advanced patient monitoring system involved overcoming several challenges:

Collecting and analyzing real-time data from multiple IoT-enabled medical devices.

Ensuring accurate health insights while minimizing false alarms.

Integrating the system seamlessly with hospital workflows and electronic health records (EHR).

Solution Provided

A comprehensive patient monitoring system was developed using IoT-enabled medical devices and AI-based monitoring systems. The solution was designed to:

Continuously collect patient vital data such as heart rate, blood pressure, oxygen levels, and temperature.

Analyze data in real-time to detect anomalies and provide early warnings for potential health issues.

Send alerts to healthcare professionals and caregivers for timely interventions.

Development Steps

Data Collection

Deployed IoT-enabled devices such as wearable monitors, smart sensors, and bedside equipment to collect patient data continuously.

Preprocessing

Cleaned and standardized data streams to ensure accurate analysis and integration with hospital systems.

AI Model Development

Built machine learning models to analyze vital trends and detect abnormalities in real-time

Validation

Tested the system in controlled environments to ensure accuracy and reliability in detecting health issues.

Deployment

Implemented the solution in hospitals and care facilities, integrating it with EHR systems and alert mechanisms for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world data and healthcare provider feedback.

Results

Enhanced Patient Care

Real-time monitoring and proactive alerts enabled healthcare professionals to provide timely interventions, improving patient outcomes.

Early Detection of Health Issues

The system detected potential health complications early, reducing the severity of conditions and preventing critical events.

Reduced Hospital Readmissions

Continuous monitoring helped manage patient health effectively, leading to a significant decrease in readmission rates.

Improved Operational Efficiency

Automation and real-time insights reduced the burden on healthcare staff, allowing them to focus on critical cases.

Scalable Solution

The system adapted seamlessly to various healthcare settings, including hospitals, clinics, and home care environments.

Data on healthcare facility locations in Kenya. The dataset was provided by the Government of Kenya.

Problem Statement

👉 Download the case studies here

Healthcare providers often rely on generalized treatment protocols that may not address the unique needs of individual patients. This approach led to variability in treatment outcomes, reduced efficacy, and limited patient satisfaction. A leading hospital sought a solution to develop personalized treatment plans tailored to each patient’s medical history, genetic profile, and current health status.

Challenge

Implementing a personalized healthcare treatment system involved overcoming the following challenges:

Integrating diverse patient data, including medical history, lab results, genetic information, and lifestyle factors.

Developing predictive models capable of identifying optimal treatment plans for individual patients.

Ensuring compliance with privacy regulations and maintaining data security throughout the process.

Solution Provided

An advanced healthcare treatment recommendation system was developed using machine learning models and predictive analytics. The solution was designed to:

Analyze patient data to identify patterns and predict treatment outcomes.

Recommend individualized treatment plans optimized for efficacy and patient preferences.

Continuously learn and adapt to improve recommendations based on new medical insights and patient feedback.

Development Steps

Data Collection

Aggregated data from electronic health records (EHR), genetic testing reports, and patient-provided health information.

Preprocessing

Standardized and anonymized data to ensure accuracy, consistency, and compliance with healthcare privacy regulations.

Model Development

Trained machine learning models to identify correlations between patient characteristics and treatment outcomes. Developed predictive algorithms to recommend personalized treatment plans for conditions like chronic diseases, cancer, and rare disorders.

Validation

Tested the system on historical patient data to evaluate its accuracy in predicting successful treatment outcomes.

Deployment

Integrated the solution into the hospital’s clinical decision support systems, enabling healthcare providers to access personalized treatment recommendations during consultations.

Continuous Monitoring & Improvement

Established a feedback mechanism to refine models using real-world treatment outcomes and patient satisfaction data.

Results

Improved Patient Outcomes

The system delivered personalized treatment recommendations that significantly improved recovery rates and health outcomes.

Increased Treatment Efficacy

Optimized treatment plans reduced trial-and-error approaches, leading to more effective interventions and fewer side effects.

Personalized Healthcare Experiences

Patients reported higher satisfaction levels due to treatment plans tailored to their individual needs and preferences.

Enhanced Decision-Making

Healthcare providers benefited from data-driven insights, enabling more informed and confident decisions.

Scalable and Future-Ready Solution

The system scaled seamlessly to support diverse medical specialties and adapted to incorporate emerging medical research.

New Medical Data Set

## Overview

New Medical Data Set is a dataset for object detection tasks - it contains Medical Dataset annotations for 1,620 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data set is related to the inputs and outputs of hospitals in Kerman province. In this data set, there are 3 inputs and 2 outputs for 4 years.

Medical Imaging (CT-Xray) Colorization New Dataset 🩺💻🖼️ This dataset provides a collection of medical imaging data, including both CT (Computed Tomography) and X-ray images, with an added focus on colorization techniques. The goal of this dataset is to facilitate the enhancement of diagnostic processes by applying various colorization techniques to grayscale medical images, allowing researchers and machine learning models to explore the effects of color in radiology.

Key Features: CT and X-ray Images 🏥: Contains both CT scans and X-ray images, widely used in medical diagnostics. Colorized Medical Images 🌈: Each image has been colorized using advanced methods to improve visual interpretation and analysis, including details that might not be immediately obvious in grayscale images. New Dataset 📊: This dataset is newly created to provide high-quality colorized medical imaging, ideal for training AI models in medical image analysis and enhancing diagnostic accuracy. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F4bfb7257cf09b0a118808b289c6c3ed4%2Fmotion_image.gif?generation=1742292037458801&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F20c64287d3b580a36bf8f948f82dbb6b%2Fmotion_image2.gif?generation=1742292060396551&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2Fdb91cac64f5a6a9100ac117fc8a55ee5%2Fmotion_image4.gif?generation=1742292150147491&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15408835%2F8624a8cab05645e3a5f02a2c1e3e9e3f%2Fmotion_image3.gif?generation=1742292165846162&alt=media" alt="">

Methods Used for Colorization: Basic Color Map Application 🎨: Applying standard color maps to highlight structures in CT and X-ray images. Adaptive Histogram Equalization (CLAHE) 🔍: Adaptive enhancement to improve contrast and highlight important features, especially in medical contexts. Contrast Stretching 📈: Adjusting image intensity to enhance visual details and improve diagnostic quality. Gaussian Blur 🌀: Applied to reduce noise, offering a smoother image for better processing. Edge Detection (Canny) ✨: Detecting edges and contours, useful for identifying specific features in medical scans. Random Color Palettes 🎨: Using randomized color schemes for unique visual representations. Gamma Correction 🌟: Adjusting image brightness to reveal more information hidden in the shadows. LUT (Lookup Table) Color Mapping 💡: Applying predefined color lookups for visually appealing representations. Alpha Blending 🔶: Blending colorized regions based on certain thresholds to highlight structures or anomalies. 3D Rendering 🔺: For creating 3D-like visualizations from 2D scans. Heatmap Visualization 🔥: Highlighting areas of interest, such as anomalies or tumors, using heatmap color gradients. Interactive Segmentation 🖱️: Interactive visualizations that help in segmenting regions of interest in medical images. Applications 🏥💡 This dataset has numerous applications, particularly in the field of medical image analysis, AI development, and diagnostic improvement. Some of the major applications include:

Medical Diagnostics Enhancement 🔍:

Colorization can aid radiologists in interpreting CT and X-ray images by making abnormalities more visible. Helps in visualizing tumors, fractures, or other anomalies, especially in cases where grayscale images are hard to interpret. AI and Machine Learning for Healthcare 🤖:

Used for training deep learning models in image segmentation, detection, and classification of diseases (e.g., cancer detection). AI models can be trained on these colorized images to improve accuracy in diagnostic tools, leading to early disease detection. Medical Image Enhancement 🖼️:

Enables improved contrast, better detail visibility, and highlighting of specific anatomical regions using color. Colorization may improve the accuracy of radiological assessments by allowing professionals to more easily spot abnormalities and changes over time. Data Augmentation for Model Training 📚:

The colorized images can serve as an additional data source for training AI models, increasing model robustness through synthetic data generation. Various colorization methods (like heatmaps and random palettes) can be used to augment image variations, improving model performance under different conditions. Visualizing Anomalies for Anomaly Detection 🔥:

Heatmap visualization helps detect subtle and hidden anomalies by coloring the areas of interest with intensity, enabling faster identification of potential issues. Edge detection and segmentation techniques enhance the ability to detect the edges and boundaries of tumors, fractures, and other critical features. 3D Image Rendering for Detailed Analysis 🧠:

3D rend...

AI Medical Dataset

  Introduction

The AI Medical General Dataset is an experimental dataset designed to build a general chatbot with a strong foundation in medical knowledge. This dataset provides a large corpus of medical data, consisting of approximately 27 million rows, specifically adapted for training Large Language Models (LLMs) in the medical domain.

  Data Sources

Our dataset is comprised of three primary sources:

Source Number of Words… See the full description on the dataset page: https://huggingface.co/datasets/ruslanmv/ai-medical-dataset.

Healthcare

## Overview

Healthcare is a dataset for object detection tasks - it contains Pills annotations for 922 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

United-MedSyn Dataset

  Description

The United-MedSyn dataset is a specialized medical speech dataset designed to evaluate and improve Automatic Speech Recognition (ASR) systems within the healthcare domain. It comprises English medical speech recordings, with a particular focus on medical terminology and clinical conversations. The dataset is well-suited for various ASR tasks, including speech recognition, transcription, and classification, facilitating the… See the full description on the dataset page: https://huggingface.co/datasets/united-we-care/United-Syn-Med.

The heart attack datasets were collected at Zheen hospital in Erbil, Iraq, from January 2019 to May 2019. The attributes of this dataset are: age, gender, heart rate, systolic blood pressure, diastolic blood pressure, blood sugar, ck-mb and troponin with negative or positive output. According to the provided information, the medical dataset classifies either heart attack or none. The gender column in the data is normalized: the male is set to 1 and the female to 0. The glucose column is set to 1 if it is > 120; otherwise, 0. As for the output, positive is set to 1 and negative to 0.

The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.

Abstract

👂💉 EHRSHOT is a dataset for benchmarking the few-shot performance of foundation models for clinical prediction tasks. EHRSHOT contains de-identified structured data (e.g., diagnosis and procedure codes, medications, lab values) from the electronic health records (EHRs) of 6,739 Stanford Medicine patients and includes 15 prediction tasks. Unlike MIMIC-III/IV and other popular EHR datasets, EHRSHOT is longitudinal and includes data beyond ICU and emergency department patients.

⚡️Quickstart 1. To recreate the original EHRSHOT paper, download the EHRSHOT_ASSETS.zip file from the "Files" tab 2. To work with OMOP CDM formatted data, download all the tables in the "Tables" tab

⚙️ Please see the "Methodology" section below for details on the dataset and downloadable files.

Methodology

1. 📖 Overview

EHRSHOT is a benchmark for evaluating models on few-shot learning for patient classification tasks. The dataset contains:

• **6,739 **patients
• 41.6 million clinical events
• 921,499 visits
• 15 prediction tasks

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2. 💽 Dataset

EHRSHOT is sourced from Stanford’s STARR-OMOP database.

• Data follows the OMOP CDM and is fully de-identified.
• Unlike most other EHR research datasets, EHRSHOT is not restricted to ED/ICU visits and instead includes longitudinal patient data for all hospital encounter types.
• EHRSHOT does not contain clinical notes or images.

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We provide two versions of the dataset:

• EHRSHOT-Original is the same exact dataset used in the original EHRSHOT paper.
• EHRSHOT-OMOP is a more complete version of the EHRSHOT dataset which includes all OMOP CDM tables and additional OMOP metadata.

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To access the raw data, please see the "Tables" and "Files"** **tabs above:

3. 💽 Data Files and Formats

We provide EHRSHOT in two file formats:

• OMOP CDM v5.4
• Medical Event Data Standard (MEDS)

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Within the "Tables" tab...

1. %3Cu%3EEHRSHOT-OMOP%3C/u%3E

* Dataset Version: EHRSHOT-OMOP

* Notes: Contains all OMOP CDM tables for the EHRSHOT patients. Note that this dataset is slightly different than the original EHRSHOT dataset, as these tables contain the full OMOP schema rather than a filtered subset.

Within the "Files" tab...

1. %3Cu%3EEHRSHOT_ASSETS.zip%3C/u%3E

* Dataset Version: EHRSHOT-Original

* Data Format: FEMR 0.1.16

* Notes: The original EHRSHOT dataset as detailed in the paper. Also includes model weights.

2. %3Cu%3EEHRSHOT_MEDS.zip%3C/u%3E

* Dataset Version: EHRSHOT-Original

* Data Format: MEDS 0.3.3

* Notes: The original EHRSHOT dataset as detailed in the paper. It does not include any models.

3. %3Cu%3EEHRSHOT_OMOP_MEDS.zip%3C/u%3E

* Dataset Version: EHRSHOT-OMOP

* Data Format: MEDS 0.3.3 + MEDS-ETL 0.3.8

* Notes: Converts the dataset from EHRSHOT-OMOP into MEDS format via the `meds_etl_omop`command from MEDS-ETL.

4. %3Cu%3EEHRSHOT_OMOP_MEDS_Reader.zip%3C/u%3E

* Dataset Version: EHRSHOT-OMOP

* Data Format: MEDS Reader 0.1.9 + MEDS 0.3.3 + MEDS-ETL 0.3.8

* Notes: Same data as EHRSHOT_OMOP_MEDS.zip, but converted into a MEDS-Reader database for faster reads.

4. 🤖 Model

We also release the full weights of **CLMBR-T-base, **a 141M parameter clinical foundation model pretrained on the structured EHR data of 2.57M patients. Please download from https://huggingface.co/StanfordShahLab/clmbr-t-base

**5. 🧑‍💻 Code **

Please see our Github repo to obtain code for loading the dataset and running a set of pretrained baseline models: https://github.com/som-shahlab/ehrshot-benchmark/

Usage

**NOTE: You must authenticate to Redivis using your formal affiliation's email address. If you use gmail or other personal email addresses, you will not be granted access. **

Access to the EHRSHOT dataset requires the following:

• Verified Affiliation with an **Academic, Government, **o

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

A 100-patient database that contains in total 100 virtual patients, 372 admissions, and 111,483 lab observations.

The "Healthcare Documentation Database" is a concise yet comprehensive collection of medical transcriptions spanning various specialties and patient encounters. Each entry includes a brief description of the medical encounter, categorized by specialty and accompanied by a unique sample name for easy reference. The transcriptions capture essential details such as patient history, symptoms, diagnoses, and treatments, providing valuable insights for healthcare professionals and researchers. This dataset serves as a valuable resource for analyzing trends, patterns, and outcomes across different medical disciplines, facilitating evidence-based decision-making and research advancements in healthcare. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18544731%2Fb8dea5ab6b921b5affbb637fdd99de5c%2Fhealth_g1164501548.jpg?generation=1708926919496930&alt=media" alt="">

Context

The Heart Attack Risk Prediction Dataset serves as a valuable resource for delving into the intricate dynamics of heart health and its predictors. Heart attacks, or myocardial infarctions, continue to be a significant global health issue, necessitating a deeper comprehension of their precursors and potential mitigating factors. This dataset encapsulates a diverse range of attributes including age, cholesterol levels, blood pressure, smoking habits, exercise patterns, dietary preferences, and more, aiming to elucidate the complex interplay of these variables in determining the likelihood of a heart attack. By employing predictive analytics and machine learning on this dataset, researchers and healthcare professionals can work towards proactive strategies for heart disease prevention and management. The dataset stands as a testament to collective efforts to enhance our understanding of cardiovascular health and pave the way for a healthier future.

Content

This synthetic dataset provides a comprehensive array of features relevant to heart health and lifestyle choices, encompassing patient-specific details such as age, gender, cholesterol levels, blood pressure, heart rate, and indicators like diabetes, family history, smoking habits, obesity, and alcohol consumption. Additionally, lifestyle factors like exercise hours, dietary habits, stress levels, and sedentary hours are included. Medical aspects comprising previous heart problems, medication usage, and triglyceride levels are considered. Socioeconomic aspects such as income and geographical attributes like country, continent, and hemisphere are incorporated. The dataset, consisting of 8763 records from patients around the globe, culminates in a crucial binary classification feature denoting the presence or absence of a heart attack risk, providing a comprehensive resource for predictive analysis and research in cardiovascular health.

Dataset Glossary (Column-wise)

• Patient ID - Unique identifier for each patient
• Age - Age of the patient
• Sex - Gender of the patient (Male/Female)
• Cholesterol - Cholesterol levels of the patient
• Blood Pressure - Blood pressure of the patient (systolic/diastolic)
• Heart Rate - Heart rate of the patient
• Diabetes - Whether the patient has diabetes (Yes/No)
• Family History - Family history of heart-related problems (1: Yes, 0: No)
• Smoking - Smoking status of the patient (1: Smoker, 0: Non-smoker)
• Obesity - Obesity status of the patient (1: Obese, 0: Not obese)
• Alcohol Consumption - Level of alcohol consumption by the patient (None/Light/Moderate/Heavy)
• Exercise Hours Per Week - Number of exercise hours per week
• Diet - Dietary habits of the patient (Healthy/Average/Unhealthy)
• Previous Heart Problems - Previous heart problems of the patient (1: Yes, 0: No)
• Medication Use - Medication usage by the patient (1: Yes, 0: No)
• Stress Level - Stress level reported by the patient (1-10)
• Sedentary Hours Per Day - Hours of sedentary activity per day
• Income - Income level of the patient
• BMI - Body Mass Index (BMI) of the patient
• Triglycerides - Triglyceride levels of the patient
• Physical Activity Days Per Week - Days of physical activity per week
• Sleep Hours Per Day - Hours of sleep per day
• Country - Country of the patient
• Continent - Continent where the patient resides
• Hemisphere - Hemisphere where the patient resides
• Heart Attack Risk - Presence of heart attack risk (1: Yes, 0: No)

Structure of the Dataset

https://i.imgur.com/5cTusqA.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world scenarios. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation.

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