Synthesizing and Augmenting Patient Data for Predictive Insight

Produce high-resolution, high-fidelity synthetic patient records that revolutionize health data science

Synthesizing and Augmenting Patient Data for Predictive Insight

Produce high-resolution, high-fidelity synthetic patient records that revolutionize health data science

Our Solution

MediSyn platform can create an arbitrarily large digital twin dataset based on your patient data.

We train an accurate and privacy-preserving machine learning generator on your patient data.
MediSyn can accurately model all structured variables from real electronic health records (over 20,000 dimensions).
The resulting generator can produce large longitudinal, high-dimensional patient datasets of targeted conditions.
Our software is based on our high-impact publication at Nature Communications

Find out more

Our differentiators

Full data generation: Our platform offers a unique solution for generating complete synthetic electronic health records (EHR) datasets based on your entire EHR database, which contains over 20,000 feature variables and 100 visits per patient. Unlike most existing solutions in the market, our platform can produce a large number of variables for much larger datasets. We use all of your data in the model training and generation process, unlike other solutions that use only a small subset of your data.
Conditional generation: Our platform also allows you to generate target datasets for specific conditions. The resulting synthetic data can be arbitrarily large and is not limited by the size of the real EHR data. Furthermore, our synthetic data is of high fidelity and high privacy, which supports various statistical analyses and machine learning models. (See our Nature Comm paper for evaluation)
OMOP and FHIR integration: We are proud to be integrated with OMOP and FHIR, two widely used standards in the healthcare industry.

Problems of sharing patient data

Security, privacy and legal concerns

Difficulty in receiving sensitive data

Security, privacy and legal concerns

Due to the concern about patient privacy, data security, and legal constraint (e.g., HIPAA), sharing real patient data is extremely difficult.

Relevant data are rare

Difficulty in receiving sensitive data

Security, privacy and legal concerns

We often look for specific types of patient data, such as patients with certain conditions, which can be insufficient for building most machine learning models.

Difficulty in receiving sensitive data

Many companies or institutions find difficulty in receiving raw patient records with protected health information (PHI) due to legal constraints.

Existing approaches for sharing patient data

Data usage agreement (DUA)

Complicated and time-consuming to set up; many projects are delayed due to the DUA process with real patient data.

Data deidentification

Data usage agreement (DUA)

De-id process can be imperfect and subject to reidentification risk; Some data (e.g., clinical notes) are hard to de-identify.

Research network

Require common data schema and cooperation/coordination across networks to conduct analysis. It is often infeasible to train models across institutions.

Synthetic data

Research network

Secure and safe to share as the data are synthetic but data are often unrealistic to support real-world clinical and machine learning applications. Can we generate realistic synthetic patient data?

Synthesizing and Augmenting Patient Data for Predictive Insight

Synthesizing and Augmenting Patient Data for Predictive Insight

Our Solution

Our differentiators

Problems of sharing patient data

Security, privacy and legal concerns

Difficulty in receiving sensitive data

Security, privacy and legal concerns

Relevant data are rare

Difficulty in receiving sensitive data

Security, privacy and legal concerns

Difficulty in receiving sensitive data

Difficulty in receiving sensitive data

Difficulty in receiving sensitive data

Existing approaches for sharing patient data

Data usage agreement (DUA)

Data usage agreement (DUA)

Data usage agreement (DUA)

Data deidentification

Data usage agreement (DUA)

Data usage agreement (DUA)

Research network

Research network

Research network

Synthetic data

Research network

Research network

Contact Us

Schedule a meeting with us

MediSyn inc

Email me for demo and solution presentation

Synthesizing and Augmenting Patient Data for Predictive Insight

Synthesizing and Augmenting Patient Data for Predictive Insight

Our Solution

Our differentiators

Problems of sharing patient data

Security, privacy and legal concerns

Difficulty in receiving sensitive data

Security, privacy and legal concerns

Relevant data are rare

Difficulty in receiving sensitive data

Security, privacy and legal concerns

Difficulty in receiving sensitive data

Difficulty in receiving sensitive data

Difficulty in receiving sensitive data

Existing approaches for sharing patient data

Data usage agreement (DUA)

Data usage agreement (DUA)

Data usage agreement (DUA)

Data deidentification

Data usage agreement (DUA)

Data usage agreement (DUA)

Research network

Research network

Research network

Synthetic data

Research network

Research network

Contact Us

Schedule a meeting with us

MediSyn inc

Email me for demo and solution presentation

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