International Business Machines Corporation

Kohei Miyaguchi

Akira Koseki

Takayuki Katsuki

Hiroshi Kajino

Toshiya Iwamori

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.

Diabetes complication prediction by health record monitoring

A computer-implemented method is provided for policy evaluation. In the method, the utility of the given decision-making policy is estimated based on a dataset of state-action-reward-state tuples, a set of candidate bootstrapping estimators of the fitted Q-evaluation (FQE) algorithm, and a criterion function. The method automatically selects the best bootstrapping estimator from the candidates based on the criterion function and, when the criterion function is appropriately designed, it produces a good policy-value estimate such that the estimation error is small (below a threshold).

Model aggregation for fitted Q-evaluation

A computer-implemented method for implementing a computer system task involving streaming data by removing biased gradients from memory includes generating a parameter sequence including a plurality of parameters corresponding to respective iteration counts. Generating the parameter sequence includes obtaining a first parameter value corresponding to a given iteration count by updating memory corresponding to the given iteration count based on a second parameter value corresponding to a prior iteration count, adapting a size of the updated memory to remove biased gradients, and obtaining the first parameter value by performing a step of a gradient descent method based on the adaptation and the second parameter value. The method further includes learning a time-series model based on the parameter sequence, and implementing a computer system task using the time-series model.

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Kanagawa, Japan

Kohei Miyaguchi