International Business Machines Corporation

James V Codella

Hung-Yang Chang

Subhro Das

Ching-Hua Chen

Marie Angelopoulos

Daby Mousse Sow

Noel C Codella

Pei-Yun S Hsueh

Vince Siu

Shahram Ebadollahi

Christopher Frank Codella

Michal Rosen-Zvi

Mohamed Ghalwash

Si Sun

Zijun Yao

Prithwish Chakraborty

Xinyu Hu

Stewart T Sill

An apparatus includes a substrate, an array of channels disposed in the substrate, wherein first ends of the channels are exposed to an outside the apparatus, a material disposed in the channel that promotes growth of neural tissue, a plurality of electrodes disposed at second ends of the channels, wherein each channel is aligned with a respective one of the electrodes, and a chip electrically connected to the electrodes.

Biological neuron to electronic computer interface

Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

System for generating specialized phenotypical embedding

An aspect includes querying, by a processor, a plurality of model data from a distributed data source based at least in part on one or more user characteristics. A plurality of sensor data is gathered associated with a condition of a user. A policy is generated including an end goal and one or more sub-goals based at least in part on the model data and the sensor data. The policy is iteratively adapted based at least in part on one or more detected changes in the sensor data collected over a period of time to adjust at least one of the one or more sub-goals. The policy and the one or more sub-goals are provided to the user.

Adaptive adjustment using sensor data and distributed data

A system provides an intervention for a user and comprises at least one processor. The system monitors behavior and context of a user to generate a behavior history. One or more models are utilized to determine an intervention for the user to induce a behavior modification, wherein the one or more models map interventions to user context and behavior and utilize the behavior history to determine an effective intervention for the user. The intervention is provided to the user and feedback is received in response to the intervention. The one or more models are updated based on the feedback. Embodiments of the present invention further include a method and computer program product for providing an intervention to a user in substantially the same manner described above.

Personalized intervention based on machine learning of behavior change states

Technical solutions are described that address correcting input time-series data provided for analysis and predictions. An example computer-implemented method includes receiving, by a processor, a time-series data input by a user. The computer-implemented method also includes computing, by the processor, a first plurality of predicted values based on the time-series data input by the user; computing, by the processor, a second plurality of predicted values by. The computer-implemented method also includes determining estimated time-series data based on the time-series data input by the user. The computer-implemented method also includes computing the second plurality of predicted values based on the estimated time-series data. The computer-implemented method also includes determining, by the processor, a defect in the time-series data input by the user based on a distribution of a plurality of differences between respective values from the first plurality of predicted values and the second plurality of predicted values.

Input data correction

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Danbury, CT, United States of America

James V Codella