• Corpus ID: 16755016

kHealth : Proactive Personalized Actionable Information for Better Healthcare

@inproceedings{Sheth2014kHealthP,
  title={kHealth : Proactive Personalized Actionable Information for Better Healthcare},
  author={A. Sheth and Pramod Anantharam and Krishnaprasad Thirunarayan},
  year={2014}
}
Mobile devices and sensors are profoundly changing the way we create, consume, and share information. Health aficionados and patients with chronic conditions are increasingly using sensors and mobile devices to track sleep, food, activity, and other physiological observations (e.g., weight, heart rate, blood pressure). This trend is leading to a paradigm shift from reactive medicine to predictive, preventative, personalized, and participatory medicine. This is also empowering an individual to… 

Figures and Tables from this paper

Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia

TLDR
This study validates one of its sensor platforms to ascertain whether it will be suitable for detecting physiological changes that may help us detect changes in people with dementia, and shows preliminary data collection results from six healthy participants using the commercially available Hexoskin vest, showing strong promise to derive actionable information.

Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study

TLDR
The continuous monitoring of pediatric asthma patients using the kHealth asthma kit generates insights on the relationship between their asthma symptoms and triggers across different seasons, which can ultimately inform personalized asthma management and intervention plans.

kHealth kit Objective Personalized Health Knowledge Graph

TLDR
The challenges of collecting, managing, analyzing, and integrating patients’ health data from various sources in order to synthesize and deduce meaningful information embodying the vision of the Data, Information, Knowledge, and Wisdom (DIKW) pyramid are explained.

Sensor Data Streams Correlation Platform for Asthma Management

TLDR
A cloud-based asthma management and visualization platform that integrates personalized PGHD from kHealth1 kit and outdoor environmental observations from web services and when applied to data from an individual, the tool assists in analyzing and explaining symptoms using ”personalized” causes, monitor disease progression, and improve asthma management.

Sensor Data Streams Correlation Platform for Asthma Management

TLDR
A cloud-based asthma management and visualization platform that integrates personalized PGHD from kHealth1 kit and outdoor environmental observations from web services2 and assists in analyzing and explaining symptoms using ”personalized” causes, monitor disease progression, and improve asthma management is described.

Personalized Digital Phenotype Score, Healthcare Management and Intervention Strategies using Knowledge Enabled Digital Health Framework for Pediatric Asthma

TLDR
The personalized scores, clinically relevant asthma categorization using digital phenotype score, actionable insights, and potential intervention strategies for better pediatric asthma management are described.

How Will the Internet of Things Enable Augmented Personalized Health?

The Internet of Things refers to network-enabled technologies, including mobile and wearable devices, which are capable of sensing and actuation as well as interaction and communication with other

A review of air quality sensing technologies and their potential interfaces with IoT for asthma management

TLDR
There is no standardisation among such devices and that in order for someone to deliver a holistic intervention for the management of a condition like asthma, a significant amount of proprietary effort to integrate third party data and technologies would be required.

Personalized Health Knowledge Graph

TLDR
The challenges of collecting, managing, analyzing, and integrating patients' health data from various sources in order to synthesize and deduce meaningful information embodying the vision of the Data, Information, Knowledge, and Wisdom (DIKW) pyramid are explained.

An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model

TLDR
The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels.

References

SHOWING 1-10 OF 25 REFERENCES

Telemedicine technology and clinical applications.

TLDR
Depending on one's viewpoint, telemedicine may be seen as a valuable tool for providing badly needed specialty care services and faith in this technology is not universal, however.

Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology

TLDR
The results suggested that TAM was able to provide a reasonable depiction of physicians' intention to use telemedicine technology, and suggested both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context.

Physical-Cyber-Social Computing: An Early 21st Century Approach

TLDR
The authors present an emerging paradigm called physical-cyber-social (PCS) computing, supporting the CHE vision, which encompasses a holistic treatment of data, information, and knowledge from the PCS worlds to integrate, correlate, interpret, and provide contextually relevant abstractions to humans.

An ontological approach to focusing attention and enhancing machine perception on the Web

TLDR
An ontology of perception, IntellegO, is developed that may be used to more efficiently convert observations into perceptions and is presented as an implementation that iteratively and efficiently processes low level, heterogeneous sensor data into knowledge through use of the perception ontology and domain specific background knowledge.

An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices

TLDR
This work employs OWL to formally define the inference tasks needed for machine perception and then provides efficient algorithms for these tasks, using bit-vector encodings and operations, and demonstrates dramatic improvements in both efficiency and scale.

A Semantics-based Approach to Machine Perception

TLDR
This dissertation presents a semantics-based machine perception framework to address issues of too much data and not enough knowledge, and efficient algorithms for the machine perception inference tasks to enable interpretation of sensor data on resource-constrained devices, such as smart phones.

Semantic Perception: Converting Sensory Observations to Abstractions

TLDR
The approach models perception through the integration of an abductive logic framework called Parsimonious Covering Theory with Semantic Web technologies and demonstrates this approach's utility and scalability through use cases in the healthcare and weather domains.

Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains

TLDR
It is demonstrated that this method for doing structural learning in object oriented domains is more efficient than conventional algorithms in such domains, and it is argued that the method supports a natural approach for expressing and incorporating prior information provided by domain experts.

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

TLDR
A methodology for assessing informative priors needed for learning Bayesian networks from a combination of prior knowledge and statistical data is developed and how to compute the relative posterior probabilities of network structures given data is shown.

Learning Bayesian Networks with Local Structure

TLDR
A novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks and indicates that learning curves characterizing the procedure that exploits the local structure converge faster than these of the standard procedure.