18389695. MEDICAL SUPPORT DEVICE, OPERATION METHOD OF MEDICAL SUPPORT DEVICE, AND OPERATION PROGRAM OF MEDICAL SUPPORT DEVICE simplified abstract (FUJIFILM CORPORATION)
Contents
- 1 MEDICAL SUPPORT DEVICE, OPERATION METHOD OF MEDICAL SUPPORT DEVICE, AND OPERATION PROGRAM OF MEDICAL SUPPORT DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MEDICAL SUPPORT DEVICE, OPERATION METHOD OF MEDICAL SUPPORT DEVICE, AND OPERATION PROGRAM OF MEDICAL SUPPORT DEVICE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MEDICAL SUPPORT DEVICE, OPERATION METHOD OF MEDICAL SUPPORT DEVICE, AND OPERATION PROGRAM OF MEDICAL SUPPORT DEVICE
Organization Name
Inventor(s)
MEDICAL SUPPORT DEVICE, OPERATION METHOD OF MEDICAL SUPPORT DEVICE, AND OPERATION PROGRAM OF MEDICAL SUPPORT DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18389695 titled 'MEDICAL SUPPORT DEVICE, OPERATION METHOD OF MEDICAL SUPPORT DEVICE, AND OPERATION PROGRAM OF MEDICAL SUPPORT DEVICE
Simplified Explanation
The patent application describes a medical support device that utilizes machine learning to predict the progression of a disease in a subject candidate for a clinical trial of a drug. The device acquires input data related to the disease and the clinical trial period, inputs this data into a machine learning model trained using supervised training data, and outputs a prediction result regarding the disease of the subject candidate in the clinical trial period.
- Processor connected to memory:
- The device includes a processor connected to or built into memory. - The processor acquires target input data related to a disease of a subject candidate for a clinical trial of a drug.
- Machine learning model:
- The processor inputs the target input data and the clinical trial period to a machine learning model trained using supervised training data. - The machine learning model outputs a prediction result regarding the disease of the subject candidate in the clinical trial period.
- Selection reference information:
- The device outputs selection reference information for determining whether or not to select the subject candidate as a subject for the clinical trial, based on the prediction result.
Potential Applications
This technology can be applied in the healthcare industry for predicting disease progression in clinical trial candidates, aiding in the selection process for trial participants.
Problems Solved
This technology helps in efficiently identifying suitable candidates for clinical trials based on the predicted progression of their disease, potentially improving the success rate of drug trials.
Benefits
- Improved selection process for clinical trial participants - Enhanced prediction of disease progression in subjects
Potential Commercial Applications
Predictive medical support devices can be utilized by pharmaceutical companies, research institutions, and healthcare providers to streamline the clinical trial participant selection process and improve trial outcomes.
Possible Prior Art
One potential prior art could be the use of machine learning models in healthcare for predicting disease progression or treatment outcomes.
Unanswered Questions
How does the device handle privacy and data security concerns?
The article does not address the specific measures taken by the device to ensure the privacy and security of the input data related to the disease of the subject candidate.
What is the accuracy rate of the machine learning model in predicting disease progression?
The article does not provide information on the accuracy rate of the machine learning model in predicting the progression of the disease in the subject candidate for the clinical trial.
Original Abstract Submitted
A medical support device includes: a processor; and a memory connected to or built into the processor, and the processor acquires target input data which is input data related to a disease of a subject candidate for a clinical trial of a drug, and a clinical trial period, inputs the target input data and the clinical trial period to a machine learning model trained using supervised training data including accumulated input data related to a disease at two or more points in time and a time interval of the input data, and causes the machine learning model to output a prediction result regarding the disease of the subject candidate in the clinical trial period, and outputs selection reference information for determining whether or not to select the subject candidate as a subject for the clinical trial, according to the prediction result.
