Machine learning model generation apparatus, machine learning model generation method, and non-transitory computer readable medium

Abstract

A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

What is claimed is: 
     
         1 . A machine learning model generation apparatus comprising:
 at least one memory; and   at least one processor configured to be constituted in such a way as to execute an instruction stored in the at least one memory, wherein   the at least one processor executes:   movement processing of moving a patient, among a plurality of patients included in a target patient group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target patient group to a source patient group, the next timing being a timing next to a target timing;   processing of generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one patient included in the target patient group after the movement processing and at least one patient included in the source patient group after the movement processing; and   processing of generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one patient included in the target patient group after the movement processing, and   the observation data include at least a state and treatment of a patient at a specific time until the last timing,   the target timing machine learning model outputs treatment at the target timing by using at least a state at the target timing as an input.   
     
     
         2 . The machine learning model generation apparatus according to  claim 1 , wherein the at least one processor moves, after discarding a patient included in the source patient group, a patient having an output error of the next timing machine learning model with respect to observation data at the next timing being larger than a predetermined amount, to a source patient group. 
     
     
         3 . The machine learning model generation apparatus according to  claim 1 , wherein
 the plurality of weak learners includes at least a first weak learner and a second weak learner, and   the at least one processor   generates the first weak learner by using observation data being weighted by a weight being set for each patient,   increases a weight for a patient, among patients included in the target patient group after the movement processing, having an output error of the first weak learner with respect to observation data at the target timing being larger than a predetermined amount,   reduces a weight for a patient, among patients included in the source patient group after the movement processing, having an output error of the first weak learner with respect to observation data at the target timing being larger than a predetermined amount, and   generates the second weak learner by using observation data being weighted by a weight being updated for each patient.   
     
     
         4 . The machine learning model generation apparatus according to  claim 3 , wherein the at least one processor
 increases a weight according to the classification error being evaluated for the first weak learner, for a patient, among patients included in the target patient group after the movement processing, having an output error of the first weak learner with respect to observation data at the target timing being larger than a predetermined amount, and   reduces a weight according to a predetermined coefficient, for a patient, among patients included in the source patient group after the movement processing, having an output error of the first weak learner with respect to observation data at the target timing being larger than a predetermined amount.   
     
     
         5 . The machine learning model generation apparatus according to  claim 3 , wherein the at least one processor determines, for each patient included in the source patient group after the movement processing, at least one of an initial value of a weight of observation data of the patient, and a reduction amount of a weight when the weight of the patient is updated, based on an output error of the next timing machine learning model with respect to observation data at the next timing of the patient. 
     
     
         6 . The machine learning model generation apparatus according to  claim 5 , wherein the at least one processor
 reduces, for each patient included in the source patient group after the movement processing, an initial value of a weight of observation data of the patient, as an output error of the next timing machine learning model with respect to observation data at the next timing of the patient is larger, or   increases, for each patient included in the source patient group after the movement processing, a reduction amount of a weight when the weight of the patient is updated, as an output error of the next timing machine learning model with respect to observation data at the next timing of the patient is larger.   
     
     
         7 . The machine learning model generation apparatus according to  claim 1 , wherein the observation data include an amount of an effect acquired by treatment at a specific time in a patient having a state at the specific time until the last timing. 
     
     
         8 . The machine learning model generation apparatus according to  claim 7 , wherein the at least one processor uses, when the plurality of weak learners is generated, information acquired by subtracting an amount according to an output error of the next timing machine learning model from an amount of an effect included in observation data at the next timing, for each patient included in the source patient group after the movement processing, as an effect at the next timing of the patient. 
     
     
         9 . The machine learning model generation apparatus according to  claim 8 , wherein the at least one processor increases an amount of reduction as a patient has a larger output error of the next timing machine learning model at the next timing when the amount of the effect is reduced. 
     
     
         10 . The machine learning model generation apparatus according to  claim 1 , wherein the at least one processor uses cost-sensitive learning when each of the plurality of weak learners is generated. 
     
     
         11 . The machine learning model generation apparatus according to  claim 1 , wherein the at least one processor
 calculates, for each of the plurality of generated weak learners, reliability of the weak learner, based on at least the classification error being evaluated, for the weak learner, by using observation data at the target timing of at least one patient included in the target patient group after the movement processing, and   generates the target timing machine learning model by combining each of the plurality of generated weak learners being weighted by associated reliability.   
     
     
         12 . The machine learning model generation apparatus according to  claim 1 , wherein the target timing learning model outputs treatment by a healthcare worker at the target timing, the treatment having been optimized in order to maximize a treatment effect of a patient. 
     
     
         13 . A machine learning system including the machine learning generation apparatus according to  claim 1 , further comprising an estimation apparatus comprising:
 at least one estimation apparatus memory; and   at least one estimation apparatus processor configured to be constituted in such a way as to execute an instruction stored in the at least one estimation apparatus memory, wherein   the at least one estimation apparatus processor executes   processing of estimating the treatment at the target timing based on at least the state of the target timing by inputting the measurement data including at least the state at the target timing into the target timing machine learning model and receiving the treatment at the target timing from the target timing machine learning model.   
     
     
         14 . A machine learning system including the machine learning generation apparatus according to  claim 1 , further comprising an estimation apparatus comprising:
 at least one estimation apparatus memory; and   at least one estimation apparatus processor configured to be constituted in such a way as to execute an instruction stored in the at least one estimation apparatus memory, wherein   the at least one estimation apparatus processor executes   processing of estimating treatment at each timing from a first timing to the last timing by setting the target timing to the each timing, inputting the measurement data including at least the state at the target timing into the target timing machine learning model and receiving the treatment at the target timing from the target timing machine learning model, thereby estimating treatment plan including estimated treatment form the first timing to the last timing.   
     
     
         15 . A machine learning model generation method comprising:
 executing movement processing of moving a patient, among a plurality of patients included in a target patient group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target patient group to a source patient group, the next timing being a timing next to a target timing;   generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one patient included in the target patient group after the movement processing and at least one patient included in the source patient group after the movement processing; and   generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one patient included in the target patient group after the movement processing, wherein   the observation data include at least a state and treatment of a patient at a specific time until the last timing, and   the target timing machine learning model outputs treatment at the target timing by using at least a state at the target timing as an input.   
     
     
         16 . A non-transitory computer readable medium storing a program for causing a computer to execute:
 movement processing of moving a patient, among a plurality of patients included in a target patient group, having an output error of a next timing machine learning model of a next timing with respect to observation data at the next timing being larger than a predetermined amount, from the target patient group to a source patient group, the next timing being a timing next to a target timing;   processing of generating a plurality of weak learners by using at least observation data from the target timing to a last timing of at least one patient included in the target patient group after the movement processing and at least one patient included in the source patient group after the movement processing; and   processing of generating a target timing machine learning model of the target timing, based on at least each of the plurality of generated weak learners, and a classification error being evaluated, for each of the plurality of generated weak learners, by using observation data at the target timing of the at least one patient included in the target patient group after the movement processing, wherein   the observation data include at least a state and treatment of a patient at a specific time until the last timing, and   the target timing machine learning model outputs treatment at the target timing by using at least a state at the target timing as an input.