International Business Machines Corporation

Institute of Bioengineering and Nanotechnology

Pei-Yun S Hsueh

Hariklia Deligianni

Amos Cahan

Theodore Gerard Van Kessel

Sreeram Ramakrishnan

Xinxin Zhu

James L Hedrick

Rudy J Wojtecki

Yi Yan Yang

Nathaniel H Park

Mareva B Fevre

Hung-Yang Chang

Xin Ding

Zhen Chang Liang

Ching-Hua Chen

Chia-Yu Chen

Jianying Hu

Jui-Hsin Lai

Yinglong Xia

Jimeng Sun

David Haim Gotz

Lih-Guong Jang

Yi-Chang Molly Wang

Nien-Chu Wu

Chandramouli Maduri

Vincent Y Deng

Joe Lu

Ning Duan

Kenneth James Barker

Yan Liu

Zhiguo Li

Sara Rosenthal

Subhro Das

James V Codella

Adam R Faulkner

Si Sun

Gema Almoguera

Xinyu Hu

A method, a computer program product, and a computer system predict medication adherence of a patient. The method includes identifying risk factors associated with medication adherence of the patient. The method includes determining a likely behaviour for medication adherence of the patient based on the identified risk factors and a temporal causal model. The temporal causal model is based on features of a patient cluster to which the patient belongs. The features are nodes in the temporal causal model. The likely behaviour is based on causality measures for each identified risk factor to the nodes. The method includes determining a current medication adherence value of the patient. The current medication adherence value is indicative of a ratio between an actual medication regiment and an expected medication regiment. The method includes determining a future medication adherence value of the patient based on the current medication adherence value and the causality measures.

Personalized medication non-adherence evaluation

Antibacterial coatings and methods of making the antibacterial coatings are described herein. A first branched polyethylenimine (BPEI) layer is formed and a first glyoxal layer is formed on a surface of the BPEI layer. The first BPEI layer and the first glyoxal layer are cured to form a crosslinked BPEI coating. The first BPEI layer can be modified with superhydrophobic moieties, superhydrophilic moieties, or negatively charged moieties to increase the antifouling characteristics of the coating. The first BPEI layer can be modified with contact-killing bactericidal moieties to increase the bactericidal characteristics of the coating.

Prevention of biofilm formation

A mechanism is provided for implementing a dynamic context-based collaborative medical concept interpreter for automatically generating and presenting summarized explanations of medical concepts. The dynamic context-based collaborative medical concept interpreter performs natural language processing on a real-time patient-provider communication to identify one or more medical concepts referred to in the communication. The dynamic context-based collaborative medical concept interpreter adjusts one or more previous explanations for the one or more medical concepts referred to in the communication using a set of contextual factors. The dynamic context-based collaborative medical concept interpreter generates an abstractive summary that summarizes ranked explanations of the one or more medical concepts based on an original language used in the one or more previous explanations. The dynamic context-based collaborative medical concept interpreter presents, in real time, the abstractive summaries of the one or more medical concepts to the patient and the provider in real-time patient-provider communication.

Dynamic context-based collaborative medical concept interpreter

A mechanism is provided in a data processing system to implement a personalized patient engagement engine. The personalized patient engagement engine develops a set of models for a plurality of behavioral phenotypes based on anonymized unstructured and structured patient-care management records for a plurality of patients over a period of time; matches a given patient to a behavioral phenotype; estimates a propensity of positive and/or negative behavioral responses of each of a plurality of targeted behaviors; dynamically updates personalized intervention effectiveness rankings in context for care manager and patient decision-making based on what has been shown to lead to positive responses for individuals with a similar behavioral profile; generates an intervention recommendation for the given patient based on the personalized intervention effectiveness rankings relative to the patient given an assigned goal and an individual intervention effect estimation; and provides the intervention recommendation to the care manager.

Personalized patient engagement in care management using explainable behavioral phenotypes

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

Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.

Identifying group and individual-level risk factors via risk-driven patient stratification

Instruments of determined level of self-efficacy may be chosen dynamically for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. Customization points in care plans may be identified, e.g., by monitoring an accumulative change in the patient's health literacy level. Clinical decision support at the point of care may be provided by adjusting patient engagement strategies and allocating resources accordingly.

Dynamic and accretive composition of patient engagement instruments for personalized plan generation

A computer-implemented method, computer program product, and system for determination of critical parts and component correlations in a circuit using a correlation graph and centrality analysis including; receiving a circuit layout portion of a larger circuit layout, converting the circuit layout portion into a correlation graph representing components as nodes and connecting wires as edges, determining, using ground truth and Na&#xef;ve Bayes to determine correlation weighting, scaling the correlation graph to represent the larger circuit, and presenting the larger correlation graph on a graphical user interface (GUI).

Graph method for system sensitivity analyses

A method, apparatus and system for measuring electrical activity generated within a brain is disclosed. A headpiece having a first transistor is placed in contact with a head that contains the brain to bring the first transistor into electrical contact with the head. An electronic signal is generated at the first transistor in response to the electrical activity generated within the brain. The electronic signal is processed at the headpiece in order to measure the electrical activity.

High definition EEG

One or more biological signals are obtained. The one or more biological signals are converted to one or more graph structures. Correlation between two or more of the biological signals are determined using the one or more graph structures. One or more changes in the one or more graph structures within a time window are recorded. A signal graph model is generated based on the recorded changes.

Graph-based analysis for bio-signal event sensing

A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Tracking eye recovery

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Hawthorne, NY, United States of America

Pei-Yun S Hsueh