INTERNATIONAL BUSINESS MACHINES CORPORATION (20240296922). USING A GAN FOR ELECTRONIC HEALTH RECORD EXTRAPOLATION simplified abstract

USING A GAN FOR ELECTRONIC HEALTH RECORD EXTRAPOLATION

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

VADIM Ratner of Haifa (IL)

Yoel Shoshan of Haifa (IL)

USING A GAN FOR ELECTRONIC HEALTH RECORD EXTRAPOLATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240296922 titled 'USING A GAN FOR ELECTRONIC HEALTH RECORD EXTRAPOLATION

Simplified Explanation

This patent application describes a method using a generative adversarial network (GAN) for extrapolating electronic health records (EHRs) to predict patient trajectories for a disease.

• The method involves obtaining EHRs for multiple patients.
• A generative component of the GAN generates artificial patient trajectories for a disease, which are validated as real by a discriminative component of the GAN.
• The artificial patient trajectories are used to train the GAN iteratively.
• The trained GAN can then be used to predict hypothetical patient trajectories or latent diagnoses for the disease based on a new patient's EHR.

Key Features and Innovation

• Utilizes a generative adversarial network (GAN) for EHR extrapolation.
• Generates artificial patient trajectories for a disease.
• Trains the GAN iteratively to improve prediction accuracy.
• Predicts hypothetical patient trajectories or latent diagnoses for a disease based on new patient data.

Potential Applications

This technology could be applied in healthcare settings to assist in predicting patient trajectories for various diseases, aiding in treatment planning and personalized medicine.

Problems Solved

• Improves the accuracy of predicting patient trajectories for diseases.
• Enhances the efficiency of utilizing electronic health records for predictive analytics.

Benefits

• Enables more accurate predictions of patient trajectories.
• Facilitates personalized treatment planning based on patient data.
• Enhances the overall efficiency of healthcare analytics.

Commercial Applications

Predictive analytics in healthcare, personalized medicine, disease management software development.

Prior Art

There may be prior research on using GANs for predictive analytics in healthcare and disease trajectory prediction.

Frequently Updated Research

Stay updated on advancements in GAN technology for healthcare predictive analytics and disease trajectory prediction.

Questions about GANs in Healthcare

How can GANs improve predictive analytics in healthcare?

Generative adversarial networks (GANs) can enhance predictive analytics by generating realistic patient trajectories based on electronic health records, improving the accuracy of disease predictions.

What are the potential challenges in implementing GANs for healthcare predictive analytics?

Challenges in implementing GANs for healthcare predictive analytics may include data privacy concerns, model interpretability, and the need for large, high-quality datasets.

Original Abstract Submitted

a method of using a generative adversarial network (gan) for ehr extrapolation is provided. the method includes obtaining ehrs for a plurality of patients. a generative component of the gan is used to generate artificial patient trajectories for a disease that are marked as real by a discriminative component of the gan based on the obtained ehrs. the artificial patient trajectories for the disease that are marked as real are used to iteratively train the gan. the trained gan is applied to a new patient ehr to predict at least one hypothetical patient trajectory or latent diagnosis for the disease.