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JP-2026075280-A - Risk estimation device, risk estimation method, and program

JP2026075280AJP 2026075280 AJP2026075280 AJP 2026075280AJP-2026075280-A

Abstract

[Challenge] To estimate risk with high accuracy. [Solution] In the risk estimation device, the acquisition means acquires data from multiple different modalities. The encoder converts the data from each modality into data representing a probability distribution in a latent space. The predictor predicts the risk corresponding to each modality based on the probability distribution. The calculation means integrates the risks corresponding to each modality using weights corresponding to each modality to calculate the estimation result. By using the risk estimation device to estimate disease risk, it is possible to support decision-making regarding the lifestyle habits of the subject. [Selection Diagram] Figure 5

Inventors

  • 黄 晨暉
  • 我田 健介
  • 二瓶 史行

Assignees

  • 日本電気株式会社

Dates

Publication Date
20260508
Application Date
20241022

Claims (10)

  1. A means of acquiring data from multiple different modalities, An encoder that converts data from each modality into data representing the probability distribution in the latent space, A predictor that predicts the risk corresponding to each modality based on the aforementioned probability distribution, A computational means for integrating the risks corresponding to each modality using the weights corresponding to each modality to calculate the estimation result, A risk estimation device equipped with the following features.
  2. The risk estimation device according to claim 1, comprising an optimization means for optimizing the weights corresponding to each modality based on the similarity between the probability distribution corresponding to each modality and a predetermined reference distribution.
  3. The risk estimation device according to claim 2, wherein the optimization means sets the weight to a larger value as the similarity between the probability distribution and the reference distribution increases, and sets the weight to a smaller value as the similarity between the probability distribution and the reference distribution decreases.
  4. The data showing the aforementioned probability distribution includes the mean and standard deviation. The risk estimation device according to claim 2, wherein the similarity is represented by the KL divergence between the probability distribution and the reference distribution.
  5. The risk estimation apparatus according to claim 1, further comprising weight correction means for correcting the weights corresponding to each modality based on the probability distribution corresponding to each modality.
  6. The risk estimation apparatus according to claim 5, wherein the weight correction means calculates a correction coefficient for correcting the weights corresponding to each modality based on the similarity between the probability distribution of each modality and the reference distribution.
  7. The risk estimation device according to claim 6, wherein the weight correction means sets the correction coefficient to 0 when the similarity is greater than a predetermined threshold, and sets the correction coefficient to 1 when the similarity is less than or equal to the predetermined threshold.
  8. The risk estimation device according to claim 1, wherein the predictor predicts the disease risk of a subject based on data from multiple modalities related to the subject's health, using a pre-trained machine learning model.
  9. A risk estimation method performed by a computer, By obtaining data from multiple different modalities, The data for each modality is transformed into data that represents the probability distribution in the latent space. Based on the aforementioned probability distribution, the risk corresponding to each modality is predicted. A risk estimation method that calculates an estimation result by integrating the risks associated with each modality using the weights corresponding to each modality.
  10. By obtaining data from multiple different modalities, The data for each modality is transformed into data that represents the probability distribution in the latent space. Based on the aforementioned probability distribution, the risk corresponding to each modality is predicted. A program that causes a computer to perform a process of integrating the risks associated with each modality using the corresponding weights for each modality and calculating an estimation result.

Description

This disclosure relates to risk estimation. Techniques for estimating disease risk using machine learning models are known. For example, Patent Document 1 describes a multimodal machine learning model that predicts the progression of dementia using multiple types of input data. In Patent Document 1, the prediction results based on multiple input data are integrated according to the prediction interval, which is the interval from a baseline point in time to a future point in time where the prediction is made, to generate the final prediction result. International Publication WO2023/276976 This document shows the overall configuration of the risk estimation device related to this disclosure.This is a block diagram showing the hardware configuration of the risk estimation device.This is a block diagram showing the functional configuration of the learning device for the risk estimation model.This is a flowchart of the learning process.This is a block diagram showing the functional configuration of a risk estimation device.This is a flowchart of the risk estimation process.This is a block diagram showing other functional configurations of the risk estimation device.This is a block diagram showing other functional configurations of the risk estimation device.This is a flowchart for other risk estimation processes. Preferred embodiments of this disclosure will be described below with reference to the drawings. <First Embodiment> [Overall structure] Figure 1 shows the overall configuration of the risk estimation device according to this disclosure. The risk estimation device 100 estimates the disease risk of a subject based on data related to the subject's health. Specifically, the risk estimation device 100 receives multimodal data, that is, data from multiple different modalities. A modality refers to a method or means for representing information, and multimodal data refers to data in different data formats, such as text, images, audio, and sensor data. In this embodiment, the multimodal data includes various data obtained from health checkups, such as the subject's height, weight, gender, blood pressure, BMI (Body Mass Index), body fat percentage, triglyceride levels, smoking status and amount, and alcohol consumption status and amount. As shown in Figure 1, the risk estimation device 100 receives multiple data sets from different modalities (in this example, data sets D1 to D3). The risk estimation device 100 predicts disease risk based on the data from each input modality and integrates the prediction results from each modality to output the final estimation result. In this process, the risk estimation device 100 converts the data from each modality into a probability distribution in latent space and integrates the prediction results from each modality according to the similarity between the obtained probability distribution and a predetermined reference distribution. This allows the risk estimation device 100 to integrate the prediction results from each modality in an appropriate proportion according to the characteristics of the data from each modality, enabling highly accurate estimation of disease risk. The risk estimation device 100 can be suitably applied to the medical or healthcare fields. For example, the risk estimation device 100 can be used to estimate the risk of lifestyle-related diseases based on data obtained from regular health checkups. [Hardware Configuration] Figure 2 is a block diagram showing the hardware configuration of the risk estimation device 100. As shown in the figure, the risk estimation device 100 comprises a processor 11, an interface (IF) 12, a ROM (Read Only Memory) 13, a RAM (Random Access Memory) 14, a database (DB) 15, and a storage medium 16. Each component is connected to the others, for example, via a bus 18. The processor 11 is a computer such as a CPU (Central Processing Unit), and controls the entire risk estimation device 100 by executing a pre-prepared program. Specifically, the processor 11 can be a CPU, GPU (Graphics Processing Unit), DSP (Digital Signal Processor), MPU (Micro Processing Unit), FPU (Floating Point Number Processing Unit), PPU (Physics Processing Unit), TPU (Tensor Processing Unit), quantum processor, microcontroller, or a combination thereof. Furthermore, the processor 11 loads the program stored in the ROM 13 and storage medium 16 into the RAM 14 and executes each process coded in the program. The processor 11 functions as part or all of the risk estimation device 100. Specifically, the processor 11 performs the learning process and risk estimation process described later. IF12 transmits and receives data with external devices. Specifically, during the learning phase, the risk estimation device 100 receives multimodal data from multiple individuals as learning data through IF12. Furthermore, during the estimation phase, i.e., when estimating risk, the risk estimation device 100 receives multimodal data from the subject through IF12 and outputs