KR20210105724A - Reinforcement learning method, device, and program for identifying causal effect in logged data - Google Patents

Abstract

Provided is a reinforcement learning method for identifying causal effect comprising: a step of acquiring recorded data related to patients; a step of sampling a subject included in another non-treated group from the recorded data to estimate causality of treatment and a result for any target subject; a step of comparing a result of the target subject and a result of the sampled subject to generate causal compensation; and a step of performing reinforcement learning using the causal compensation.

Description

Reinforcement learning method, device, and program for identifying causal effect in logged data}

Embodiments of the present invention relate to a reinforcement learning method, apparatus and program for identifying causal or causal influence in recorded data.

As big data becomes available in the medical field and healthcare field, attention is focused on the development of techniques to identify individual levels of causal or causal effects from observational data. For example, attempts may be made to develop techniques for predicting causal effects and establishing decision-making models regarding what actions (eg, treatment) to take.

For the development of these technologies, for example, a randomized clinical trial (RCT) may be used to discover a causal relationship between treatment and outcome, but clinical trials involve ethical issues, high costs, and effectiveness issues. . Therefore, a new analysis method is required for inferring causality.

On the other hand, since the conventional data driven decision model learns only one potential outcome from one individual, there is a problem that the trained model may be biased. . For example, such a model may have a problem of deriving results with bias depending on the individual.

Also, in reinforcement learning, an agent creates a sequence of actions that maximizes a reward, and since the reward can be determined artificially, the learning model can be artificially induced. Therefore, the learning model according to the prior art may not have objectivity and may be difficult to use scientifically.

The present invention has been devised to improve the above problems, and an object of the present invention is to provide a reinforcement learning method, apparatus and program for identifying causality in recorded data. However, these problems are exemplary, and the scope of the present invention is not limited thereto.

A reinforcement learning method for identifying causality according to an embodiment of the present invention includes: acquiring recorded data about patients; sampling, from the recorded data, subjects included in the non-treated relative group to estimate causality of treatment and outcome for any subject subject; generating a causal reward by comparing the result of the target entity with the result of the sampled entity; It may include; performing reinforcement learning using the causal reward.

According to an embodiment, in the generating of the causal reward, the difference between the result of the target entity and the result of the sampled entity is determined by determining a factual result of the target entity and a counterfactual of the target entity. The method may include transforming a difference between the result of the target entity and the result of the sampled entity to correspond to the difference in the result.

에, 상기 i번째 샘플링된 개체와 상기 대상 개체의 공변량 거리(covariates distance)에 기초한 가중치를 곱하여 상기 인과보상으로 생성하는 단계를 포함할 수 있다. According to an embodiment, the generating of the causal reward may include a difference in results between the i-th sampled entity and the target entity.

and generating the causal compensation by multiplying the i-th sampled entity and a weight based on a covariates distance of the target entity.

로 사용하는 단계를 더 포함할 수 있다. According to an embodiment, the generating of the causal reward comprises a difference between the result of the i-th sampled entity and the result of multiplying the result of the target entity by the reciprocal of a propensity score, the result difference

It may further include the step of using as

According to an embodiment, performing the reinforcement learning may include applying the causal reward to a Deep Q Neural Network (DQN).

According to one embodiment, the step of obtaining recorded data about the patients comprises: each patient's age, sex, primary site of tumor, tumor size, histologic subtype, SEER stage, surgical treatment, and obtaining one or more of parameters indicative of radiation therapy.

Reinforcement learning apparatus for identifying causality according to an embodiment of the present invention includes: a data acquisition unit for acquiring recorded data about patients; a sampling unit configured to sample, from the recorded data, subjects included in the relative group that did not receive the treatment, in order to estimate the causality of treatment and results for a given subject; a causal compensation generating unit for generating a causal compensation by comparing the result of the target entity with the result of the sampled entity; and a reinforcement learning unit that performs reinforcement learning using the causal reward.

A non-transitory computer-readable storage medium for identifying causality in accordance with an embodiment of the present invention comprises an operation for obtaining recorded data about patients. and sampling, from the recorded data, subjects included in the non-treated relative group to estimate causality of treatment and outcome for any subject subject; One or more programs may be stored for executing an operation of generating a causal reward by comparing the results of the entity and an operation of performing reinforcement learning using the causal reward.

Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims, and detailed description of the invention.

According to an embodiment of the present invention made as described above, it is possible to estimate a counterfactual result from the recorded data, and it is possible to generate a reinforcement learning model capable of identifying or predicting causality from the recorded data.

Therefore, even without conducting a randomized clinical trial (RCT), it is possible to identify and predict the causality of treatment outcomes for individual patients through reinforcement learning.

In addition, the bias can be eliminated by performing reinforcement learning using a causal reward using the difference between the results of the subject and the pseudo-counterfactual object, and the performance of reinforcement learning can be greatly improved.

Of course, the scope of the present invention is not limited by these effects.

1 shows a reinforcement learning method for identifying causality according to an embodiment of the present invention.
2 schematically shows the configuration of a reinforcement learning apparatus for identifying causality according to an embodiment of the present invention.

Since the present invention can apply various transformations and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. Effects and features of the present invention, and a method of achieving them, will become apparent with reference to the embodiments described below in detail in conjunction with the drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various forms.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, and when described with reference to the drawings, the same or corresponding components are given the same reference numerals, and the overlapping description thereof will be omitted. .

In the following examples, the singular expression includes the plural expression unless the context clearly dictates otherwise.

In the following embodiments, terms such as include or have means that the features or components described in the specification are present, and the possibility that one or more other features or components may be added is not excluded in advance.

In the following embodiments, when components, units, blocks, etc. are connected, other components, units, and blocks are interposed between the components, units, and blocks as well as when the components, units, and blocks are directly connected. It also includes cases where it is indirectly connected.

In the following examples, reinforcement learning is learning which action is optimal to take in a current state. Whenever an action is taken, a reward is given from the external environment, and learning can proceed in the direction of maximizing this reward.

In other words, reinforcement learning is an algorithm in which an agent recognizes the current state in an environment and finds an action or action sequence (policy) that maximizes a reward among selectable actions. . Agents can decide actions based on policies. A policy can be a rule of probability for taking a specific action in a specific situation, and can be learned through learning. The goal of reinforcement learning may be to maximize the cumulative reward.

According to the reinforcement learning method, apparatus and program for identifying causality according to an embodiment of the present invention, causal effect of treatment T on outcome Y of individual X from observational data , CE) can be estimated.

The variable X represents an individual characteristic, and may represent, for example, individual patients. X is the observed baseline characteristics or baseline information of patients given as covariates. Outcome Y represents the outcome of a treated or untreated individual (ie, patient), and may represent, for example, survival or survival rate. The result Y can be represented, for example, in terms of time and survival at that time. However, the present invention is not limited thereto.

According to the method, apparatus and program according to an embodiment of the present invention, for the outcome Y of the subject X treated (T=t), the effect of the treatment T on the outcome Y, that is, the causality (CE) of the treatment T and the outcome Y Reinforcement learning to identify (identify) can be performed. In addition, if the reinforcement learning is used, an individual causal effect (ICE) calculated for the individual X can be used to predict an individual's treatment effect and support decision-making on the individual's treatment.

To estimate the causal effect (CE) of treatment T on outcome Y for subject X from batch data or observation data with a logged policy t, from actual data a factual outcome representing the actual outcome ( factual outcome) E[Y|X, T=t] and a counterfactual outcome E[Y|X, T≠t] representing a hypothetical expected outcome for the same subject are defined.

In fact, the result E[Y|X, T=t] represents the outcome Y if subject X is treated (T = t), and the reflex chamber outcome E[Y|X, T≠t] is that subject X is treated The result Y in the case of not receiving (T ≠ t) is denoted. The result Y may be expressed as, for example, survival period or survival rate. On the other hand, if the action with respect to treatment T is binary, it can be expressed as T=0 or T=1, for example, T=1 for the treated case and T= for the non-treated case. can be expressed as 0.

Here, the causal relationship between T and Y for the subject X can be inferred through the difference between the fact result and the semi-real result, that is, Y _T=1 (X) - Y _{T=0 (X).}

However, it is impossible to simultaneously observe the treated and untreated results for the same subject X. That is, for the treated subject X, the untreated reflex chamber results do not actually exist, and therefore cannot be observed.

Therefore, in order to estimate the causal relationship between treatment T and outcome Y for subject X, a new algorithm for estimating reflex chamber outcomes with logged data is required. Specifically, the recorded data for the untreated (T=0) counterpart patients, because the reflex chamber results for the treated subject X (i.e., the untreated results) cannot be obtained from the recorded data. A method of representing a pseudo-counterfactual through sampling from

According to the reinforcement learning method, apparatus and program for identifying causality according to an embodiment of the present invention, treatment in subject X through comparison with simulated-reflection results sampled from non-treated (T=0) relative patients A causal reward of T=1 may be estimated, and reinforcement learning may be performed using the causal reward. To perform reinforcement learning using causal reward, the agent searches the action space using the causal reward in the reinforcement learning system and learns the optimal policy to maximize the causal reward of individual X.

Meanwhile, according to an embodiment of the present invention, a group of individuals sampled to represent the pseudo-counterfactual may be referred to as, for example, a pseudo-counterfactual group X'. In addition, the simulated - 'can be expressed as (X _i' the sampled object to indicate that the half true X _i ∈ X '). For example, X _i ' may be the i-th sampled entity. Since X _i ' is an individual sampled from recorded data regarding untreated relative patients, it may also be referred to as a 'comparative population'.

In summary, in order to identify a causal effect (CE), an estimate of the treated and untreated outcomes of subject X is required. According to the present invention, subject X sampled from untreated relative patients _{By comparing the result of i} ' and the result of the treated individual X, the simulated-causal reward can be estimated, and reinforcement learning can be performed to approximate actual causality using the simulated-causal reward.

1 shows a reinforcement learning method for identifying causality according to an embodiment of the present invention. Identifying causal, causal effect, or causal effect (CE) may mean identifying the causal effect of an action (e.g. treatment) on an individual outcome. . Also, 'identifying' may include estimating, approximating, or predicting.

In a reinforcement learning method for identifying causality according to an embodiment of the present invention, obtaining recorded data about patients (S10), in order to estimate the causality of treatment and results for an arbitrary subject. , from the recorded data, sampling an individual included in the counterpart that has not received the treatment (S20), comparing the result of the subject with the result of the sampled individual to generate a causal compensation It may include a step (S30) and a step (S40) of performing reinforcement learning using the causal reward.

Referring to FIG. 1 , first, a step S10 of obtaining logged data about patients is performed. The recorded data about the patients may include the values of _{X i} , T , Y _{i for each patient i.}

According to an embodiment of the present invention, X _i representing baseline information of patients may be collected from the recorded data.

X _i is the age, sex, primary site of the tumor (eg, axial or distal) of the subject (eg patient) i, tumor size (eg above or below the reference size), and histologic subtype (eg, spindle cell type, biphasic type, or not otherwise specified [NOS]), SEER stage (eg, localized, regional, or distant), surgical treatment, or radiation therapy. The present invention is not limited thereto, and X _i may include various other variables. X _i can use all available clinical information depending on the characteristics of the patient population being observed. For example, X _i can be selected in various ways depending on which treatment T causality is to be estimated. For example, X _i may include variables representing blood pressure, diabetes, smoking, body mass index, HDL index, LDL index, and family history of individual i. For example, X _i may include image data of the entity i. X _i is the image data, and is data of a medical image such as an X-ray image, a computed tomography (CT) image, a positron emission tomography (PET) image, an ultrasound image, or magnetic resonance imaging (MRI), or a predetermined process to the data. may contain a single value. X _i may be in the form of, for example, a vector including a plurality of variables as described above.

According to an embodiment of the present invention, it is possible to collect treatment T of each individual i from the recorded data. For example, treatment T may be chemotherapy, but this is only an example and the present invention may be applied to various treatments.

According to an embodiment of the present invention, the result Y _i of each entity i may be collected from the recorded data. Y _i may represent the outcome regarding the survival of individual i, and may include time and event (ie, death or survival). For example, Y _i may be expressed as a variable representing time and whether or not survival occurs at that time.

Next, in order to estimate the treatment T for any subject and the causality of the outcome, the relative subject (T≠t) to represent the simulated-reflection facts for the subject (T=t, X). , X _i ') is sampled (S20) is performed. The relative population (T≠t, X _i ') is sampled from the untreated relative patient, among the recorded data. Conversely, if an individual in fact data did not receive treatment (T ≠ t, X), the simulated-reflex chamber counterpart _{could be an individual who received treatment (T = t, X i} ').

Next, using the comparison of the result of the target entity with the result of the sampled entity, generating a causal reward ( S30 ) is performed. Causal compensation according to the present invention may refer to discounted causal reweard.

X') 간에 성향점수 재가중(propensity score reweighting) 또는 공변량 조정(covariate adjustment)을 통하여 상기 개체들 간의 결과를 비교하는 공변성 매칭(covariance matching)이 수행될 수 있다. 이하에서, 인과보상을 생성하는 동작에 관한 상세한 설명이 서술된다. According to an embodiment, in the comparison of the results of the subject subject (T=t, X) and the sampled subject (T≠t, X _i '∈X'), similar subjects (X) having similar covariates

X'), covariance matching may be performed to compare results between the individuals through propensity score reweighting or covariate adjustment. Hereinafter, a detailed description of the operation of generating a causal compensation is described.

은, 대상 개체 X와 매치된 상대군 개체(T≠t, X_i'∈X')의 결과의 차이

로 나타내어질 수 있다. Individual causal influence of the target object (T=t, X)

is the difference between the result of the target entity X and the matched counterpart entity (T≠t, X _{i '∈X')}

can be expressed as

Specifically, the individual causal influence (ICE) can ideally be inferred from Equation 1 below. E may represent an expected value.

However, since Y(T≠t)|X, the result of not receiving treatment (T≠t) of the subject X, is a result of a reflex chamber that cannot be actually observed, as shown in Equation 2 below, the simulated-reflection chamber group X' It can be estimated using an entity X _{i ' sampled from .} X _i ' is the relative population, sampled from recorded data for relative patients who did not receive treatment (T≠t).

는 X와 X_i'의 결과의 차이를 나타낼 수 있으며, E는 기대값을 나타낼 수 있다. 한편, 수학식 2에서

를 산출함에 있어서, X_i'와 일대일 매칭을 하는 경우, 숨겨진 교란변수(hidden confounder)에 의해 바이어스(bias)가 증가할 수 있다. 또한 일대일 매칭을 하는 경우, 매칭되지 않은 샘플들 사이의 중요한 정보가 무시될 수 있는 문제가 발생할 수 있다

may represent the difference between the results of X and X _i ', and E may represent the expected value. On the other hand, in Equation 2

In calculating , _{if one-to-one matching is performed with X i} ', a bias may increase due to a hidden confounder. Also, in case of one-to-one matching, there may be a problem in that important information between unmatched samples may be ignored.

Therefore, in the method, apparatus and program according to an embodiment of the present invention, in the sampling step ( S20 ), without considering covariates, N randomly selected relative group subjects (T≠t, X _i ') can be selected.

에 가중치(weight)를 크게 하고 샘플링된 개체 X_i'가 사실 개체 X와 유사하지 않을수록 결과 차이

에 가중치(weight)를 작게 하는 계산을 포함할 수 있다. 본 발명에서 가중치는 개체의 유사도가 작을 수록 (즉, 비교 대상 환자간의 거리가 멀 수록) 작은 가중치를 가지게 되는 일종의 할인된 가중치(discounted weight)를 지칭할 수 있다(discounted factor:

). On the other hand, since the randomly sampled relative subject X _i ' is not the same individual as the factual individual (i.e., the individual representing the treated patient), it corresponds to a simulated-reflex chamber and is used to reflect the actual reflex chamber. transformation is needed The above variant is the _{difference in outcome if the sampled entity X i} ' is in fact similar to entity X.

The larger the weight on , the less the sampled object X _i ' is in fact similar to the object X, the greater the difference in results.

It may include a calculation for reducing the weight in . In the present invention, the weight may refer to a kind of discounted weight that has a smaller weight as the degree of similarity of an individual decreases (that is, as the distance between patients to be compared increases) (discounted factor:

).

가 대상 개체의 사실 결과와 반사실 결과의 차이에 상응하도록, 상기 결과 차이

를 변형할 수 있다.That is, the difference between the result of the target object X and the sampled object X _{i '}

corresponds to the difference between the true result and the semi-real result of the subject, the result difference

can be transformed.

에 따라 가중치

를 줄 수 있다. Specifically, the method, apparatus and program according to an embodiment of the present invention, the covariance difference between _{X and X i '}

weighted according to

can give

에 가중치

를 적용한 기대값

을 이용하여 인과관계 또는 인과성을 근사하여 나타낼 수 있다. Referring to Equation 3, the difference between the results of _{X and X i '}

weighted on

expected value applied

can be used to approximate causality or causality.

를 나타냄에 있어서, 각각의 결과(Y, Y_i)를 성향점수(

)의 역수(inverse propensity score, IPS)로 재가중(reweight)하여 상기 결과 차이를 나타낼 수도 있다. In addition, according to another embodiment of the present invention, the difference in results between the _{target object X and the sampled object X i '∈X'}

In representing each result (Y, Y _i ), the propensity score (

) may be reweighted with an inverse propensity score (IPS) to indicate the difference in the result.

는, 치료를 배정 받을 확률 P(T = t|X)로, 환자에 대해서 측정된 공변량들(X)이 주어졌을 때, 환자가 치료를 받을 조건부확률로 정의될 수 있다. propensity score

can be defined as the probability P(T = t|X) of receiving treatment, the conditional probability that a patient will receive treatment, given the covariates (X) measured for the patient.

Equation 5 shows the definition of the propensity score, and Equation 6 shows the difference between the reweighted result of the inverse of the propensity score for the _{target entity X and the sampled entity X i '.}

는, 대상 개체 X와 샘플링된 개체 X_i'의 공변량 거리(covariates difference)

에 의해 할인될(discounted) 수 있다. According to an embodiment of the present invention, the difference between the results of Equation 6

is the covariates difference between the target object X and the sampled object X _{i '}

may be discounted by

Equation 7 is an expression representing the covariate distance.

만큼 거듭제곱 하여 나타낸다. 여기서 T는 annealing temperature로, 샘플링을 반복할 때마다 상기 가중치를 점진적으로 감소시키도록 한다. 즉, 시간이 지남에 따라 점진적으로 "온도"를 낮춰서 탐험 비율(또는 탐험된 값의 반영 비율)을 낮추도록 한다. 할인율(discount factor) γ는

를 만족하며, 작은 값일수록 즉각적인 보상에 강조를 두게 한다. On the other hand, the weight (discounted weight) according to an embodiment of the present invention, the discount rate γ

It is expressed by multiplying it by Here, T is the annealing temperature, and the weight is gradually decreased each time sampling is repeated. That is, gradually lowering the “temperature” over time to lower the rate of exploration (or the rate of reflection of values explored). The discount factor γ is

is satisfied, and the smaller the value, the more emphasis is placed on immediate reward.

와 같이 나타낼 수 있다. The difference as a result of applying a discounted weight to the i-th sampling is,

can be expressed as

은 수학식 8과 같이 정규화될 수 있다. In addition, the sum of the discounted result differences

can be normalized as in Equation (8).

Equation 8 represents the discounted result difference, which may be used as a reward in the reinforcement learning system according to an embodiment of the present invention.

In various embodiments of the present invention, a reward used for reinforcement learning to identify causality may be referred to as a _{(discounted) individual causal reward R ICE.} R _ICE may be equal to Equation (8) according to an embodiment. However, the present invention is not limited thereto, and causal compensation according to various embodiments of the present invention may be expressed as Equations 2, 3, 4, and 6. In common, causal compensation can be generated by comparing the results of the target entity with the results of the sampled entity.

In the manner described above, the step S30 of generating a discounted individual causal reward may be performed.

Causal compensation may be generated in various ways in various embodiments, as described above. In common, causal compensation can be generated by comparing the results of the target entity with the results of the sampled entity.

According to an embodiment, the reinforcement learning method for identifying causality may generate and use a difference between a result of randomly sampled subjects from a counterpart group and a result of a target entity as a causal reward. For example, it can be used as a causal compensation by comparing the survival outcomes of untreated patients (ie, relative patients) with those of treated patients.

According to another embodiment, the reinforcement learning method for identifying causality may generate and use a discounted reward as a causal reward according to the variance of a covariate between a sampled relative and a target entity.

According to another embodiment, in the reinforcement learning method for identifying causality, a compensation obtained by weighting the result difference between a sampled relative group object and a target object as an inverse propensity score (IPS) is generated as a causal reward. and can be used. For example, the difference in the result of a value obtained by multiplying the result of the sampled individual and the target object by the reciprocal of the propensity score may be used as a causal compensation.

According to another embodiment, the reinforcement learning method for identifying causality may generate and use a discounted reward as a causal reward according to cross entropy between the sampled relative group and the target entity.

Finally, a step (S40) of performing reinforcement learning using the generated causal reward may be performed. The step of performing reinforcement learning (S40) includes applying the generated causal reward to Q-learning.

Specifically, the agent may learn an optimal policy for maximizing the expected value of the infinite or finite sum of the causal reward. Agents can also learn the expected values of individual causal rewards.

The Bellman equation represents the regression of the expected reward in a Markov decision process (MDP). In the reinforcement learning method for identifying causality according to an embodiment of the present invention, in the step S40 of performing reinforcement learning _{, R ICE} (X) may be applied as a reward for the action t in the _{state X t.} The Bellman equation and the optimal Bellman equation can be expressed as in Equation 9 below.

In the Q-learning setting, the optimal Bellman equation can be expressed as Equation 10 below.

및 Q(X_t, T)의 최대값을 근사할 수 있다(off-policy 방식). In Equation (10), T' represents the behavior of the next state. In Q-learning, the agent _{searches both Q(X t} , T=1) and Q(X _t , T=0) to

and the maximum value of Q(X _t , T) (off-policy method).

를 측정함으로써

를 추정할 수 있다. In order to estimate the individual causal effect of treatment T, the reinforcement learning method according to the present invention is as follows:

by measuring

can be estimated.

After applying the causal compensation R _ICE generated as described above to the DQN (Deep Q Neural Network) algorithm, it was confirmed that the performance of reinforcement learning was greatly improved.

Meanwhile, the step S40 of performing reinforcement learning is not limited thereto, and may be applied to various algorithms other than DQN. _{Causal compensation R ICE} (X) according to an embodiment of the present invention can be used when determining compensation for medical data in various types of reinforcement learning algorithms.

2 schematically shows the configuration of a reinforcement learning apparatus for identifying causality according to an embodiment of the present invention.

The reinforcement learning apparatus 10 for identifying causality shown in FIG. 2 shows only the components related to the present embodiment in order to prevent the features of the present embodiment from being blurred. Accordingly, it can be understood by those of ordinary skill in the art related to the present embodiment that other general-purpose components may be further included in addition to the components shown in FIG. 2 .

The reinforcement learning apparatus 10 for identifying causality according to an embodiment of the present invention may correspond to at least one processor or include at least one processor. Accordingly, the reinforcement learning apparatus 10 for identifying causality may be driven in a form included in other hardware devices such as a microprocessor or a general-purpose computer system.

The present invention may be represented in functional block configurations and various processing steps. These functional blocks may be implemented in any number of hardware and/or software configurations that perform specific functions. For example, the present invention provides integrated circuit configurations, such as memory, processing, logic, look-up tables, etc., capable of executing various functions by the control of one or more microprocessors or other control devices. can be hired Similar to how components of the present invention may be implemented as software programming or software elements, the present invention includes various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors. Further, the present invention may employ prior art techniques for electronic configuration, signal processing, and/or data processing, and the like. Terms such as “mechanism”, “element”, “means” and “configuration” may be used broadly, and the components of the present invention are not limited to mechanical and physical configurations. The term may include the meaning of a series of routines of software in association with a processor or the like.

Referring to FIG. 2 , the reinforcement learning apparatus 10 for identifying causality includes a data acquiring unit 11 , a sampling unit 12 , a causal compensation generating unit 13 , and a reinforcement learning unit 14 .

The data acquisition unit 11 may acquire logged data about the patients. _{The recorded data includes X i} representing the baseline information for each patient i , T representing the treatment option, and Y _i representing the outcome.

X _i may include one or more of variables indicative of patient i's age, sex, primary site of tumor, tumor size, histologic subtype, SEER stage, surgical treatment, and radiotherapy. X _i may be in the form of, for example, a vector containing one or more variables as described above.

_{The outcome Y i} of each individual represented by X _i may represent the outcome regarding the survival of the individual X _i , and may include time and event (eg, death or survival). For example, Y _i may be expressed as a variable representing time and whether or not survival occurs at that time.

와 같이 나타낼 수 있다. The recorded data is, for example,

can be expressed as

The sampling unit 12 is configured to estimate, from the recorded data, a subject (or a relative group) included in the counterpart not receiving the treatment, in order to estimate the causality of the treatment and the result for any subject X. object) can be sampled.

The relative group is an individual representing the relative group patient who has not experienced the treatment option (T≠t), and may be denoted by _{X i '.} A counterpart population is sampled to represent a pseudo-counterfactual for the subject entity (T=t, X).

According to one embodiment of the present invention, the sampling unit 12 _{does not consider the covariates of the relative group X i} ' (or does not compare with the covariates of the target individual X), in the set of recorded data. You can randomly select N opposing groups X _{i '}

와 상기 샘플링된 개체의 결과

를 비교하여, 강화학습에 사용될 인과보상을 생성할 수 있다. Causal compensation generating unit 13 is the result of the target object

and the result of the sampled object

By comparing , it is possible to generate a causal reward to be used in reinforcement learning.

와 대상 개체의 결과

와의 차이를 인과보상으로 생성 및 사용할 수 있다. 예를 들면, 치료 받지 않은 환자(즉, 상대군 환자)와 치료 받은 환자의 생존 결과를 비교하여 인과보상으로 사용할 수 있다. According to an embodiment, the causal compensation generating unit 13 is a result _{of the individual X i} ' randomly sampled from the relative group patients (T≠t, X').

and the result of the target object

The difference between and can be generated and used as a causal compensation. For example, it can be used as a causal compensation by comparing the survival outcomes of untreated patients (ie, relative patients) with those of treated patients.

에 따라 할인된(discounted) 보상을 인과보상으로 생성 및 사용할 수 있다. According to another embodiment, the causal compensation generating unit 13 is configured to generate a variance of the covariate between the _{sampled relative group entity X i ' and the target entity X.}

Accordingly, discounted rewards can be created and used as causal rewards.

)에, 상기 i번째 상대군 개체와 대상 개체의 공변량 거리(

)가 멀수록 작아지는 가중치(

)를 곱하여 인과보상으로 생성할 수 있다. 이 경우 인과보상은 수학식 8과 같을 수 있다. For example, the causal compensation generating unit 13 may generate a difference (

), the covariate distance (

) gets smaller as the distance increases (

) can be multiplied to generate causal compensation. In this case, the causal compensation may be equal to Equation (8).

)로 사용할 수 있다. 예를 들면 인과보상 생성부(13)는, 상대군 개체의 결과와 대상 개체의 결과에 각각 성향점수의 역수(IPS)를 곱한 결과의 차이를 이용하여 인과보상을 생성할 수 있다. According to another exemplary embodiment, the causal compensation generating unit 13 calculates the difference between the sampled relative group individual result and the target individual result multiplied by the inverse of the propensity score (IPS), respectively, between the relative group individual and the target object. The difference between the results of the object (

) can be used as For example, the causal reward generating unit 13 may generate a causal reward by using a difference between the result of the opponent group and the result of multiplying the result of the target entity by the reciprocal of the propensity score (IPS).

According to another embodiment, the causal compensation generating unit 13 may generate and use a discounted compensation as a causal compensation according to cross entropy between the sampled relative group and the target entity.

The reinforcement learning unit 14 may perform reinforcement learning using the causal reward generated by the causal reward generating unit 13 . The reinforcement learning unit 14 may apply the generated causal reward to a Deep Q Neural Network (DQN). The reinforcement learning unit 14 may apply the generated causal reward to Q-learning. The reinforcement learning unit 14 may learn an optimal policy for maximizing an expected value of an infinite sum or a finite sum of the causal reward.

On the other hand, the reinforcement learning method for identifying causality according to an embodiment of the present invention shown in FIG. 1 can be written as a program that can be executed on a computer, and is a general-purpose method for operating the program using a computer-readable recording medium. It may be implemented in a digital computer. The computer-readable recording medium includes a storage medium such as a magnetic storage medium (eg, a ROM, a floppy disk, a hard disk, etc.) and an optically readable medium (eg, a CD-ROM, a DVD, etc.).

Although the present invention has been described with reference to one embodiment shown in the drawings, it will be understood that this is merely exemplary, and that those of ordinary skill in the art can make various modifications and variations therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (9)

obtaining recorded data about the patients;
sampling, from the recorded data, subjects included in the non-treated relative group to estimate causality of treatment and outcome for any subject subject;
generating a causal reward by comparing the result of the target entity with the result of the sampled entity;
Including; performing reinforcement learning using the causal reward
A reinforcement learning method to identify causality. According to claim 1,
The step of generating the causal compensation comprises:
such that a difference between a result of the subject subject and a result of the sampled subject corresponds to a difference between a factual result of the subject subject and a counterfactual result of the subject subject; comprising the steps of transforming the difference in the outcome of an object that has been
A reinforcement learning method to identify causality. According to claim 1,
The step of generating the causal compensation comprises:
The difference in results between the i-th sampled object and the target object

, multiplying the i-th sampled entity and a weight based on a covariates distance of the target entity to generate the causal compensation,
A reinforcement learning method to identify causality. 4. The method of claim 3,
The step of generating the causal compensation comprises:
The difference between the result of multiplying the result of the i-th sampled individual and the result of the target entity by the reciprocal of the propensity score, the result difference

Further comprising the step of using as
A reinforcement learning method to identify causality. According to claim 1,
The step of performing the reinforcement learning is,
Comprising the step of applying the causal compensation to DQN (Deep Q Neural Network),
A reinforcement learning method to identify causality. According to claim 1,
Obtaining recorded data about the patients comprises:
One or more of each patient's age, sex, tumor primary site, tumor size, histologic subtype, SEER stage, surgical and radiotherapy, medical imaging data, blood pressure, diabetes, smoking, and body mass index obtaining a variable indicative of clinical information comprising,
A reinforcement learning method to identify causality. According to claim 1,
The result of the sampled object is,
comprising the survival period of the sampled individual,
A reinforcement learning method to identify causality. a data acquisition unit for acquiring recorded data about patients;
a sampling unit configured to sample, from the recorded data, subjects included in the relative group that did not receive the treatment, in order to estimate causality of treatment and results for any target subject;
a causal compensation generating unit for generating a causal compensation by comparing the result of the target entity with the result of the sampled entity;
Containing, a reinforcement learning unit that performs reinforcement learning using the causal reward
A reinforcement learning device that identifies causality. A non-transitory computer-readable storage medium comprising:
obtaining recorded data pertaining to the patients;
sampling, from the recorded data, subjects included in the non-treated relative group to estimate causality of treatment and outcome for any subject subject;
generating a causal reward by comparing the result of the target entity with the result of the sampled entity;
A non-transitory computer-readable storage medium for storing one or more programs for executing an operation of performing reinforcement learning using the causal reward.

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