MediSyn Inc

MediSyn IncMediSyn IncMediSyn Inc
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MediSyn Inc

MediSyn IncMediSyn IncMediSyn Inc
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  • Technology
  • About

Generate realistic digital twins for your patient records

Generate realistic digital twins for your patient recordsGenerate realistic digital twins for your patient recordsGenerate realistic digital twins for your patient records

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

Generate realistic digital twins for your patient records

Generate realistic digital twins for your patient recordsGenerate realistic digital twins for your patient recordsGenerate realistic digital twins for your patient records

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.

Find out more

Our differentiators

  1. We can generate complete synthetic EHR datasets based on your entire EHR database (with over 20K feature variables and 100 visits per patient). Note that most existing solutions in the market can only produce a handful of variables for much smaller datasets. Their generation process is done on a small subset of your data, not your entire database. We utilize all your data in the model training and model generation process. 
  2. We can generate target datasets for specific conditions.
  3. Our resulting synthetic data can be arbitrarily large and is not limited by the size of the real EHR data. 
  4. Our synthetic data are of high fidelity to support various statistical analyses and machine learning models. 
  5. We are integrated with OMOP and FHIR.

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

Difficulty in receiving sensitive data

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)

Data usage agreement (DUA)

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)

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 networks

Research networks

Research networks

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

Synthetic data

Research networks

Research networks

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? 

Contact Us

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We love our customers and will respond to all customers' email within 24 hours. 

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