US-20260128179-A1 - STATE TRANSITION ESTIMATION DEVICE, STATE TRANSITION ESTIMATION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM

US20260128179A1US 20260128179 A1US20260128179 A1US 20260128179A1US-20260128179-A1

Abstract

A long-term state transition is estimated in consideration of variations among individuals. A state transition estimation device acquires a first feature vector of a first individual in a first age range, a second feature vector of a second individual in a second age range, and individual data of the first individual, generates a latent vector representing variation between the first individuals from the individual data, performs mapping to an estimated feature vector in a case where the first individual falls within a second age range by using the latent vector, updates a mapping parameter so that the mapping approaches optimal transport from a probability distribution of the first feature vector to a probability distribution of the second feature vector, and estimates a probability distribution of the estimated feature vector in a case where the individual in the first age range falls within the second age range.

Inventors

Kosuke Nishihara
Fumiyuki Nihey
Keisuke Suzuki
Mana HASHIMOTO
Yuki Kosaka
Kentaro Nakahara

Assignees

NEC CORPORATION

Dates

Publication Date: 20260507
Application Date: 20251015
Priority Date: 20241106

Claims (10)

1 . A state transition estimation device comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform: a data input process of acquiring a first feature vector indicating a state of each of a plurality of first individuals in a first age range, a second feature vector indicating a state of each of a plurality of second individuals in a second age range older than the first age range, and individual data different from the first feature vector of each of the plurality of first individuals; an individual variation generation process of generating a latent vector representing a variation between the first individuals from the individual data of the first individual; a data mapping process of performing mapping from the first feature vector of the first individual to an estimated feature vector in a case where the first individual falls within the second age range using the latent vector of the first individual; an update process of updating a mapping parameter applied to the data mapping process based on the first feature vectors and the estimated feature vectors of the plurality of first individuals and the second feature vectors of the plurality of second individuals in such a way that the mapping in the data mapping process approaches an optimal transport from a probability distribution of the first feature vector to a probability distribution of the second feature vector; and a probability distribution estimation process of estimating a probability distribution of the estimated feature vector in a case where an individual falls within the second age range from the first feature vector of the individual in the first age range using the data mapping process in which the mapping parameter is updated by the update process.
2 . The state transition estimation device according to claim 1 , wherein the data mapping process includes a process of outputting an average and a variance of the estimated feature vector obtained by the mapping using a machine learning model of a variational auto encoder (VAE) type.
3 . The state transition estimation device according to claim 2 , wherein the probability distribution estimation process includes a process of estimating a probability distribution of the estimated feature vector in a case where an individual falls within the second age range from the individual data of the individual in the first age range and the first feature vector using a machine learning model having the individual data as an input and a difference vector between the estimated feature vector obtained by the data mapping process and an average of the estimated feature vector as an output.
4 . The state transition estimation device according to claim 3 , wherein the probability distribution estimation process includes a process of estimating a first probability distribution of the estimated feature vector from the first feature vector of an individual in the first age range and estimating a second probability distribution of the estimated feature vector from the individual data of an individual in the first age range and the first feature vector, and the processor is configured to execute the instruction to perform display processing of displaying the first probability distribution and the second probability distribution.
5 . The state transition estimation device according to claim 1 , wherein at least some of the first individuals and at least some of the second individuals are relevant to each other, and the update process updates the mapping parameter applied to the data mapping process in such a way that the estimated feature vector mapped from the first feature vectors of the at least some of the first individuals and the second feature vector of the at least some of the second individuals approach each other in the data mapping process.
6 . The state transition estimation device according to claim 1 , wherein the individual data includes data that can change on a time axis, the probability distribution estimation process includes a process of re-estimating a probability distribution of the estimated feature vector in response to a change in the individual data, and the processor is configured to execute the instruction to perform display processing of displaying a change in a probability distribution of the estimated feature vector estimated in the probability distribution estimation process.
7 . The state transition estimation device according to claim 1 , wherein the first individual, the second individual, and the individual are persons, and the first feature vector, the second feature vector, and the estimated feature vector indicate a state related to health or a disease risk.
8 . The state transition estimation device according to claim 1 , wherein the processor is configured to execute the instruction to perform display processing of displaying the probability distribution estimated in the probability distribution estimation process as information for decision making on a life plan.
9 . A state transition estimation method comprising: a data input process of acquiring a first feature vector indicating a state of each of a plurality of first individuals in a first age range, a second feature vector indicating a state of each of a plurality of second individuals in a second age range older than the first age range, and individual data different from the first feature vector of each of the plurality of first individuals; an individual variation generation process of generating a latent vector representing a variation between the first individuals from the individual data of the first individual; a data mapping process of performing mapping from the first feature vector of the first individual to an estimated feature vector in a case where the first individual falls within the second age range using the latent vector of the first individual; an update process of updating a mapping parameter applied to the data mapping process based on the first feature vectors and the estimated feature vectors of the plurality of first individuals and the second feature vectors of the plurality of second individuals in such a way that the mapping in the data mapping process approaches an optimal transport from a probability distribution of the first feature vector to a probability distribution of the second feature vector; and a probability distribution estimation process of estimating a probability distribution of the estimated feature vector in a case where an individual falls within the second age range from the first feature vector of the individual in the first age range using the data mapping process in which the mapping parameter is updated by the update process.
10 . A non-transitory computer readable medium having stored therein a state transition estimation program causing a computer to execute: a data input process of acquiring a first feature vector indicating a state of each of a plurality of first individuals in a first age range, a second feature vector indicating a state of each of a plurality of second individuals in a second age range older than the first age range, and individual data different from the first feature vector of each of the plurality of first individuals; an individual variation generation process of generating a latent vector representing a variation between the first individuals from the individual data of the first individual; a data mapping process of performing mapping from the first feature vector of the first individual to an estimated feature vector in a case where the first individual falls within the second age range using the latent vector of the first individual; an update process of updating a mapping parameter applied to the data mapping process based on the first feature vectors and the estimated feature vectors of the plurality of first individuals and the second feature vectors of the plurality of second individuals in such a way that the mapping in the data mapping process approaches an optimal transport from a probability distribution of the first feature vector to a probability distribution of the second feature vector; and a probability distribution estimation process of estimating a probability distribution of the estimated feature vector in a case where an individual falls within the second age range from the first feature vector of the individual in the first age range using the data mapping process in which the mapping parameter is updated by the update process.

Description

INCORPORATION BY REFERENCE This application is based upon and claims the benefit of priority from Japanese patent application No. 2024-194452, filed on Nov. 6, 2024, the disclosure of which is incorporated herein in its entirety by reference. TECHNICAL FIELD The present disclosure relates to a state transition estimation device, a state transition estimation method, and a state transition estimation program. BACKGROUND ART The long-term transition prediction of the disease risk and the transition estimation of the health condition are effective for the future life plan design. By predicting the long-term transition of the health condition and the disease risk from the medical examination result and the daily health activity, the lifetime cost can be predicted, and for example, can be used for asset management support. In recent years, a large amount of cross-sectional data has been accumulated, and data analysis has become possible in the fields of healthcare and medical care. For example, Patent Literature 1 describes a technique for constructing a health model in which the health condition of a subject changes by age group. A technique for predicting a long-term transition is also useful in the fields of healthcare and medical care. Patent Literature 1: Japanese Unexamined Patent Application Publication No. 2016-038626Non-patent Literature 1: “Neural Optimal Transport” (Korotin, Alexander, Daniil Selikhanovych, and Evgeny Burnaev, The Eleventh International Conference on Learning Representations (Published as a conference paper at ICLR 2023)) SUMMARY However, it may be difficult to sufficiently acquire longitudinal data having temporal connection. The present inventor has studied estimating longitudinal data by estimating a transition between distributions based on cross-sectional data collected for each age group and age by using a technique such as optimal transport based on unique knowledge. As an example of the technology related to the optimal transport, for example, there is a technology in which machine learning and optimal transport described in the Non-patent Literature 1 are combined. According to the technology described in the Non-patent Literature 1, it is possible to give a distribution having a probabilistic spread as a transition destination to data of a certain point of transition source. As a result, for example, it is possible to stochastically indicate how the test value transitions as the age advances based on the test value of the medical examination of the subject in a certain age group. At first glance, this seems to be able to express a situation in which even if the test value is the same at a certain point in time, the test value does not necessarily transition to the same test value due to variation between individuals. However, in the technique described in the Non-patent Literature 1, since a distribution having a spread is generated by giving noise, it does not reflect actual variation between individuals. The present disclosure has been made in view of the above problems, and an example object thereof is to provide a technique for estimating a long-term state transition in consideration of variations among individuals. A state transition estimation device according to an example aspect of the present disclosure includes a data input unit that acquires a first feature vector indicating a state of each of a plurality of first individuals in a first age range, a second feature vector indicating a state of each of a plurality of second individuals in a second age range older than the first age range, and individual data different from the first feature vector of each of the plurality of first individuals, an individual variation generation unit that generates a latent vector representing a variation between the first individuals from the individual data of the first individual, a data mapping unit that performs mapping from the first feature vector of the first individual to an estimated feature vector in a case where the first individual falls within the second age range using the latent vector of the first individual, an update unit that updates a mapping parameter in the data mapping unit based on the first feature vectors and the estimated feature vectors of the plurality of first individuals and the second feature vectors of the plurality of second individuals in such a way that the mapping in the data mapping unit approaches an optimal transport from a probability distribution of the first feature vector to a probability distribution of the second feature vector, and a probability distribution estimation unit that estimates a probability distribution of the estimated feature vector in a case where an individual falls within the second age range from the first feature vector of the individual in the first age range using the data mapping unit in which the mapping parameter is updated by the update unit. A state transition estimation method according to an example aspect of