ArticleCEUR workshop proceedings2018
Personalized Health Knowledge Graph.
Article in CEUR workshop proceedings, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of knowledge graphs in health management for chronic disease patients: a scoping review.BMC medical informatics and decision making · 2026Pooled it
- Research information systems and knowledge graphs: a review.Frontiers in research metrics and analytics · 2026Review
- The digital intelligent precise nursing framework: theory development in health recommender system.BMC nursing · 2025Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction.... IEEE Conference on Artificial Intelligence · 2025Article
- Deciphering shared molecular dysregulation across Parkinson's disease variants using a multi-modal network-based data integration and analysis.NPJ Parkinson's disease · 2025Article
- A Chinese question and answer system for liver cancer based on knowledge graph and large language mode.Frontiers in artificial intelligence · 2025Article
- Patient-centric knowledge graphs: a survey of current methods, challenges, and applications.Frontiers in artificial intelligence · 2024Review
- The importance of graph databases and graph learning for clinical applications.Database : the journal of biological databases and curation · 2023Review
- CONNECTED: leveraging digital twins and personal knowledge graphs in healthcare digitalization.Frontiers in digital health · 2023Article
- Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being.International journal of social robotics · 2023Article
- Patient preferences as human factors for health data recommender systems and shared decision making in orthopaedic practice.Frontiers in digital health · 2023Article
- Mining a stroke knowledge graph from literature.BMC bioinformatics · 2021Article
- Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS.PloS one · 2021Article
- Knowledge-Based Biomedical Data Science.Annual review of biomedical data science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Our current health applications do not adequately take into account contextual and personalized knowledge about patients. In order to design "Personalized Coach for Healthcare" applications to manage chronic diseases, there is a need to create a Personalized Healthcare Knowledge Graph (PHKG) that takes into consideration a patient's health condition (personalized knowledge) and enriches that with contextualized knowledge from environmental sensors and Web of Data (e.g., symptoms and treatments for diseases). To develop PHKG, aggregating knowledge from various heterogeneous sources such as the Internet of Things (IoT) devices, clinical notes, and Electronic Medical Records (EMRs) is necessary. In this paper, we explain 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. Furthermore, we sketch a solution that combines: 1) IoT data analytics, and 2) explicit knowledge and illustrate it using three chronic disease use cases - asthma, obesity, and Parkinson's.
Indexed as
Identifiers
34690624PMC8532078What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.