Abstract: A computer-implemented method is provided for learning with incomplete data in which some of entries are missing. The method includes acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete set of covariates {tilde over (x)}. The method further includes obtaining, by a hardware processor, a predictive distribution p?(y|x) of an outcome y by using the incomplete set of covariates x and a parameter ?, the parameter ? being unknown. A learning of the parameter ? includes performing a maximization by maximizing a stochastically approximated conditional evidence lower bound.

Abstract: A computer-implemented process for training a prediction model for survival analysis includes the following operations. A batch of data is elected from a training dataset representing a plurality of individuals. A curve representing a survival rate of a group of individuals within the batch over a period of time is generated using a non-parametric statistical function and for the batch of data. Individual survival functions for each individual within the batch are estimated using the prediction model. An average survival function is generated from the individual survival functions. A calibration loss is generated using the curve representing the survival rate and the average survival function. Weight of a neural network including the prediction model are updated based upon a total loss including the calibration loss.

Abstract: A method, which trains a neural network to perform an analysis that satisfies average calibration, includes a processor manipulating a data set that includes an outcomes vector and a set of feature vectors, each of which corresponds to one of the outcomes in the outcomes vector. The processor repeatedly: selects a subset of the set of feature vectors; generates a distribution vector for a subset of the outcomes vector that corresponds to the subset of the set of feature vectors; produces a prediction vector by running the neural network on the subset of the set of feature vectors; calculates a Bregman divergence between the distribution vector and a scoring distribution vector of the prediction vector; and updates weights of the neural network based on the Bregman divergence.

Abstract: A computer-implemented method for updating hidden states in a recurrent neural network (RNN) to predict future data from multivariate time-series data with irregular time intervals is provided including inputting, for each of time steps at observations, observation data at a current time step in the multivariate time-series data to the RNN, for each of the time steps: subdividing a time interval between a previous time step and the current time step by a predetermined number, for each of subdivided time steps calculating a first element of the hidden state at a current subdivided time step using ODE-RNNs, and calculating a second element of the hidden state at the current subdivided time step using the last updated hidden state and a hidden state at the previous time step so that the last updated hidden state is decayed to be close to the hidden state at the previous time step.

Abstract: A computer-implemented method for reconstructing time series data including irregular time intervals and missing values to predict future data from the time series data using a Recurrent Neural Network (RNN) is provided including obtaining irregular time series data X={x1, . . . , xt, . . . , xT} and time interval data ?={?1, . . . , ?t, . . . , ?T}, where xt is a D-dimensional feature vector, T is a total number of observations, ?t is a D-dimensional time interval vector, and a d-th element ?td of ?t represents a time interval from a last observation, replacing missing values in xt with imputed values using an imputation to obtain {tilde over (x)}t, rescaling data of the time interval ?t to obtain rescaled time interval data ?(?t) by calculating ?(?t)=? log(e+ max(0,??t+b?))+b?, where W?, W?, b?, b? are network parameters of a neural network and e is Napier's constant, and multiplying {tilde over (x)}t by ?(?t) to obtain {circumflex over (x)}t as regular time series data for input of the RNN.

Abstract: A computer-implemented method for computing an objective function of discriminative inference with generative models with incomplete data in which some of entries are missing is provided including acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete features {tilde over (x)} and computing a predictive distribution p?(y|x) of an outcome y by using the incomplete set of covariates x and a parameter ?, the parameter ? being unknown. Learning of the parameter ? is performed by minimizing an objective function (?):=?ln p?(y|x)=ln p?({tilde over (x)}|m)?ln p?(y,x|m), and the objective function (?) is bounded with a difference between a marginal evidence upper bound MEUBO and a joint evidence lower bound JELBO, where ln p?({tilde over (x)}|m)?MEUBO and ln p?(y,{tilde over (x)}|m)?JELBO.

Abstract: A computer-implemented method is provided for predicting a medical event time. The method includes receiving an electronic health record (EHR) including a plurality of pairs of observation variables and corresponding timestamps. Each of the plurality of pairs include a respective observation variable and a respective corresponding timestamp. The method further includes converting the EHR into a K-dimensional vector representing a cumulative-stay time at a finite number of patient medical states, the patient medical states being determined by values of the observation variables. The method also includes processing, by a hardware processor, the K-dimensional vector using a medical event time prediction model to output a prediction of a medical event time. The medical event time prediction model has been configured through training to receive and process K-dimensional vectors converted from past EHRs to output predicted medical event times.

Abstract: A computer-implemented method, a computer program product, and a computer system for tracking progression of chronic conditions. A computer acquires trajectories of estimated glomerular filtration rates of respective patients. The computer determines a number of trajectory parts in each of the trajectories. The computer generates, in each of response vectors of the trajectories, subsets corresponding to respective ones of the trajectory parts. The computer replaces responses in the response vectors with the subsets. The computer determine, in the respective ones of the trajectory parts, numbers of patient groups. The computer calculates probabilities of the respective patients belonging to respective ones of the patient groups, based on the subsets. The computer clusters the respective patients into the respective ones of the patient groups, based on the probabilities. Information of clustering the patient groups is used to identify risk groups for renal functions.

Abstract: A computer-implemented method, a computer program product, and a computer system for using a time-window based attention long short-term memory (TW-LSTM) network to analyze sequential data with time irregularity. A computer splits elapsed time into a predetermined number of time windows. The computer calculates average values of previous cell states in respective ones of the time windows and sets the average values as aggregated cell states for the respective ones of the time windows. The computer generates attention weights for the respective ones of the time windows. The computer calculates a new previous cell state, based on the aggregated cell states and the attention weights for the respective ones of the time windows. The computer updates a current cell state, based on the new previous cell state.

Abstract: Methods and systems for classifying tabular data include clustering columns from one or more input tables into column groups. The column groups are processed using a neural network that has a set of input layers, each input layer accepting a respective one column group from the column groups as input, to generate a classification output. A classification task is performed on the one or more input tables using the classification output.

Abstract: Methods and systems for detecting seizures include generating two-dimensional frames that each include a first set of elements that store measurements from sensors and a second set of elements that store values calculated from said measurements. The two-dimensional frames are classified using a machine learning model. It is determined that a subject experienced a seizure during a measurement interval based on an output of the machine learning model. A corrective action is performed responsive to the determination that the subject experienced a seizure.