38,097 research outputs found
Digital Twins for Patient Care via Knowledge Graphs and Closed-Form Continuous-Time Liquid Neural Networks
Digital twin technology has is anticipated to transform healthcare, enabling
personalized medicines and support, earlier diagnoses, simulated treatment
outcomes, and optimized surgical plans. Digital twins are readily gaining
traction in industries like manufacturing, supply chain logistics, and civil
infrastructure. Not in patient care, however. The challenge of modeling complex
diseases with multimodal patient data and the computational complexities of
analyzing it have stifled digital twin adoption in the biomedical vertical.
Yet, these major obstacles can potentially be handled by approaching these
models in a different way. This paper proposes a novel framework for addressing
the barriers to clinical twin modeling created by computational costs and
modeling complexities. We propose structuring patient health data as a
knowledge graph and using closed-form continuous-time liquid neural networks,
for real-time analytics. By synthesizing multimodal patient data and leveraging
the flexibility and efficiency of closed form continuous time networks and
knowledge graph ontologies, our approach enables real time insights,
personalized medicine, early diagnosis and intervention, and optimal surgical
planning. This novel approach provides a comprehensive and adaptable view of
patient health along with real-time analytics, paving the way for digital twin
simulations and other anticipated benefits in healthcare.Comment: 6 page
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domain
including providing solutions to medication recommendations, especially
recommending medication combination for patients with complex health
conditions. Existing approaches either do not customize based on patient health
history, or ignore existing knowledge on drug-drug interactions (DDI) that
might lead to adverse outcomes. To fill this gap, we propose the Graph
Augmented Memory Networks (GAMENet), which integrates the drug-drug
interactions knowledge graph by a memory module implemented as a graph
convolutional networks, and models longitudinal patient records as the query.
It is trained end-to-end to provide safe and personalized recommendation of
medication combination. We demonstrate the effectiveness and safety of GAMENet
by comparing with several state-of-the-art methods on real EHR data. GAMENet
outperformed all baselines in all effectiveness measures, and also achieved
3.60% DDI rate reduction from existing EHR data.Comment: AAAI 2019; change the template and fix some typo
KindMed: Knowledge-Induced Medicine Prescribing Network for Medication Recommendation
Extensive adoption of electronic health records (EHRs) offers opportunities
for its use in various clinical analyses. We could acquire more comprehensive
insights by enriching an EHR cohort with external knowledge (e.g., standardized
medical ontology and wealthy semantics curated on the web) as it divulges a
spectrum of informative relations between observed medical codes. This paper
proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed)
framework to recommend medicines by inducing knowledge from myriad
medical-related external sources upon the EHR cohort, rendering them as medical
knowledge graphs (KGs). On top of relation-aware graph representation learning
to unravel an adequate embedding of such KGs, we leverage hierarchical sequence
learning to discover and fuse clinical and medicine temporal dynamics across
patients' historical admissions for encouraging personalized recommendations.
In predicting safe, precise, and personalized medicines, we devise an attentive
prescribing that accounts for and associates three essential aspects, i.e., a
summary of joint historical medical records, clinical condition progression,
and the current clinical state of patients. We exhibited the effectiveness of
our KindMed on the augmented real-world EHR cohorts, etching leading
performances against graph-driven competing baselines
Q&A Platforms Evaluated Using Butler University Q&A Intelligence Index
A new study using the Butler University Q&A Intelligence Index measures how various mobile Q&A platforms deliver quality, accurate answers in a timely manner to a broad variety of questions. Based on the results of our analysis, ChaCha led all Q&A platforms on mobile devices.
Results of the study are based upon review of a large set of responses from each of the major Q&A platforms, coupled with a comparison of disparate Q&A platforms that serve answers in different ways. Our methodology included the creation of a new metric, termed the Butler University Q&A Intelligence Index, which measures the likelihood that a user can expect to receive a correct answer in a timely manner to any random question asked using natural language. We asked questions via mobile services and randomized the questions to cover both popular and long-tail knowledge requests
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
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