Abstract: Dual machine translators are trained by generating a translated medical image by operation of an illustrative model on an original medical record, generating information based on whether the translated medical image is natural in a modality of medical imaging, producing a back-translated medical record by operation of an interpretive model on the translated medical image, calculating a reward by comparing the back-translated medical record to the original medical record, updating parameters of the illustrative model in response to the information and the reward, and updating parameters of the interpretive model in response to the reward.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate a transformation of a model of an entity by a model of a plurality of entities are provided. According to an embodiment, a computer-implemented method can comprise identifying a plurality of parameters of a model of a plurality of entities; generating a model of an entity based on collected data of an operation of the entity, wherein the model of the entity comprises a subset of the plurality of parameters; and transforming the model of the entity based the model of the plurality of entities such that a first result from the model of the plurality of entities and a second result from the model of the entity have a relationship that satisfies a defined criterion, given same values used for the subset of the plurality of parameters.

Abstract: Mechanisms are provided for training a hybrid machine learning (ML) computer model to simulate a biophysical system of a patient and predict patient classifications based on results of simulating the biophysical system. A mechanistic model is executed to generate a training dataset. A surrogate ML model is trained to replicate logic of the mechanistic computer model and generate patient feature outputs based on surrogate ML model input parameters. A transformation ML model is trained to transform patient feature outputs of the surrogate ML model into a distribution of patient features. A generative ML model is trained to encode samples from a uniform distribution of input patient data into mechanistic model parameter inputs that are coherent to the target distribution of patient features and are input to the surrogate ML model. Input patient data for a patient is processed through the ML models to predict a patient classification for the patient.

Abstract: Techniques regarding inferring parameters of one or more mechanistic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a machine learning component that can identify a causal relationship in a mechanistic model via a machine learning architecture that employs a parameter space of the mechanistic model as a latent space of a variational autoencoder.

Abstract: Techniques regarding inferring parameters of one or more mechanistic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a machine learning component that can identify a causal relationship in a mechanistic model via a machine learning architecture that employs a parameter space of the mechanistic model as a learned distribution sampled within a generative adversarial network.

Abstract: Dual machine translators are trained by generating a translated medical image by operation of an illustrative model on an original medical record, generating information based on whether the translated medical image is natural in a modality of medical imaging, producing a back-translated medical record by operation of an interpretive model on the translated medical image, calculating a reward by comparing the back-translated medical record to the original medical record, updating parameters of the illustrative model in response to the information and the reward, and updating parameters of the interpretive model in response to the reward.

Abstract: Mechanisms are provided for training a hybrid machine learning (ML) computer model to simulate a biophysical system of a patient and predict patient classifications based on results of simulating the biophysical system. A mechanistic model is executed to generate a training dataset. A surrogate ML model is trained to replicate logic of the mechanistic computer model and generate patient feature outputs based on surrogate ML model input parameters. A transformation ML model is trained to transform patient feature outputs of the surrogate ML model into a distribution of patient features. A generative ML model is trained to encode samples from a uniform distribution of input patient data into mechanistic model parameter inputs that are coherent to the target distribution of patient features and are input to the surrogate ML model. Input patient data for a patient is processed through the ML models to predict a patient classification for the patient.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate a transformation of a model of an entity by a model of a plurality of entities are provided. According to an embodiment, a computer-implemented method can comprise identifying a plurality of parameters of a model of a plurality of entities; generating a model of an entity based on collected data of an operation of the entity, wherein the model of the entity comprises a subset of the plurality of parameters; and transforming the model of the entity based the model of the plurality of entities such that a first result from the model of the plurality of entities and a second result from the model of the entity have a relationship that satisfies a defined criterion, given same values used for the subset of the plurality of parameters.