Abstract: Example implementations described herein are directed to the problem of dynamically extracting valid sets of clinical treatments and predictive features from electronic health records to construct models that can predict the effect of treatments and hence compare the effect of multiple, putative treatments. By utilizing a data pipelining technique for constructing machine learning models that only utilizes sets of valid treatment options instead of all possible options, the hardware and computational resources required for constructing the machine learning models can thereby be reduced, and the predicted treatment transition outcomes can be traced to valid treatments, thereby allowing the clinician to understand the effects from a clinical perspective.

Abstract: An analyzer calculates a first feature amount data group from an intermediate layer by inputting each training data of a training data group into a learning model which includes an input layer, one or more intermediate layers, and an output layer, and is learned based on the training data group assigned to the input layer and a correct answer data group assigned to the output layer. A second feature amount data is calculated from the intermediate layer by inputting prediction target data of the learning model. A search processing of searching specific first feature amount data similar to the second feature amount data is calculated by the second calculation processing, from the first feature amount data group, and an extraction processing of extracting, from the training data group, specific training data, which is a calculation source of the specific first feature amount data searched by the search processing.

Abstract: Example implementations described herein are directed to the problem of dynamically extracting valid sets of clinical treatments and predictive features from electronic health records to construct models that can predict the effect of treatments and hence compare the effect of multiple, putative treatments. By utilizing a data pipelining technique for constructing machine learning models that only utilizes sets of valid treatment options instead of all possible options, the hardware and computational resources required for constructing the machine learning models can thereby be reduced, and the predicted treatment transition outcomes can be traced to valid treatments, thereby allowing the clinician to understand the effects from a clinical perspective.