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: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include replicating, with patient parameters, a set of patient data of a patient and conditioning said patient parameters with at least one measure from said patient. The operations may include parameterizing a pharmacokinetic model with said patient parameters and sampling said patient parameters with a constrained optimization generative adversarial network. The operations may include calculating dosage data of a pharmaceutical with said patient parameters with said constrained optimization generative adversarial network and communicating said dosage data to a user.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include training a cr-GAN model with a first data set of a first pharmaceutical and a second data set of a second pharmaceutical. The operations may include conditioning the cr-GAN model with at least one conditional variable and generating, with the cr-GAN model, patient parameters. The operations may include replicating a set of patient data of a patient with the patient parameters and calculating dosage data with the patient parameters based on a therapeutic target. The operations may include displaying the dosage data to a user.

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: A computer-implemented method for transaction authorization is disclosed. The computer-implemented method includes receiving a transaction request from a user to access a resource. The computer-implemented method further includes determining historical biometric data for the user. The computer-implemented method further includes determining current biometric data for the user at a time the transaction request is received. The computer-implemented method further includes determining whether the historical biometric data for the user matches the current biometric data for the user at the time the transaction request is received. The computer-implemented method further includes responsive to determining that the historical biometric data for the user matches the current biometric data for the user at the time the transaction request is received, authorizing the transaction request to access the resource.

Abstract: A method, computer system, and a computer program product for model inversion is provided. The present invention may include training a generator of a generative adversarial network to sample a distribution of input parameters of a mechanistic model. The present invention may include generating a distribution of parameters for the mechanistic model. The present invention may include simulating the mechanistic model with the distribution 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: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor and configure the at least one processor to implement a medical record to illustrative medical image translation engine. The medical record to illustrative medical image translation engine receives a medical record batch from storage for a patient and generates one or more predicted prognosis records based on the medical record batch using a neural network. The medical record to illustrative medical image translation engine converts the one or more predicted prognosis records to illustrative medical images using a first agent. The medical record to illustrative medical image translation engine generates a presentation of disease progression using the illustrative medical images and outputs the presentation to a user.

Abstract: Systems and methods that facilitate determining interaction between medications and the brain using a brain measure and a brain model. Hidden nervous system states are difficult to predict, diagnose, and treat with therapeutic medications. A Dual Neural Machine Translation (d-NMT) algorithmic system that utilizes sets of parameters for a relapsing-remitting MS model based on patient medical records and adjusts a method of parameterization to produce a model that can match patients' medical records and medical images. These parameters are can be used by a therapeutic determining model to recommend therapies, doses, and time courses accurately.

Abstract: Embodiments of the present invention are directed to a systems and methods for registration of pulse wave signal and determining arterial pressure. A non-limiting example of the system includes a strain gauge sensor. A non-limiting example of the method includes receiving, to a processor, a first pressure pulse signal from a first strain gauge sensor. The method also includes receiving, to the processor, a second pressure pulse signal from a second strain gauge sensor. The method also includes determining a pulse transit time between the first strain gauge sensor and the second strain gauge sensor based at least in part upon the first pressure pulse signal and the second pressure pulse signal. The method also includes determining an arterial pressure based at least in part upon the pulse transit time.

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 and methods that facilitate determining interaction between medications and the brain using a brain measure and a brain model. Hidden nervous system states are difficult to predict, diagnose, and treat with therapeutic medications. A Dual Neural Machine Translation (d-NMT) algorithmic system that utilizes sets of parameters for a relapsing-remitting MS model based on patient medical records and adjusts a method of parameterization to produce a model that can match patients' medical records and medical images. These parameters are can be used by a therapeutic determining model to recommend therapies, doses, and time courses accurately.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor and configure the at least one processor to implement a medical record to illustrative medical image translation engine. The medical record to illustrative medical image translation engine receives a medical record batch from storage for a patient and generates one or more predicted prognosis records based on the medical record batch using a neural network. The medical record to illustrative medical image translation engine converts the one or more predicted prognosis records to illustrative medical images using a first agent. The medical record to illustrative medical image translation engine generates a presentation of disease progression using the illustrative medical images and outputs the presentation to a user.

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: Embodiments of the present invention are directed to a systems and methods for registration of pulse wave signal and determining arterial pressure. A non-limiting example of the system includes a strain gauge sensor. A non-limiting example of the method includes receiving, to a processor, a first pressure pulse signal from a first strain gauge sensor. The method also includes receiving, to the processor, a second pressure pulse signal from a second strain gauge sensor. The method also includes determining a pulse transit time between the first strain gauge sensor and the second strain gauge sensor based at least in part upon the first pressure pulse signal and the second pressure pulse signal. The method also includes determining an arterial pressure based at least in part upon the pulse transit time.