Access to care: analysis of the geographical distribution of healthcare
using Linked Open Data
- URL: http://arxiv.org/abs/2204.05206v1
- Date: Mon, 11 Apr 2022 15:51:56 GMT
- Title: Access to care: analysis of the geographical distribution of healthcare
using Linked Open Data
- Authors: Selene Baez Santamaria, Emmanouil Manousogiannis, Guusje Boomgaard,
Linh P. Tran, Zoltan Szlavik and Robert-Jan Sips
- Abstract summary: This work focuses on generating a comprehensive semantic dataset of medical facilities worldwide.
We evaluate each data source along various dimensions, such as completeness, correctness, and interlinking with other sources.
- Score: 0.03670008893193884
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: Access to medical care is strongly dependent on resource
allocation, such as the geographical distribution of medical facilities.
Nevertheless, this data is usually restricted to country official
documentation, not available to the public. While some medical facilities' data
is accessible as semantic resources on the Web, it is not consistent in its
modeling and has yet to be integrated into a complete, open, and specialized
repository. This work focuses on generating a comprehensive semantic dataset of
medical facilities worldwide containing extensive information about such
facilities' geo-location.
Results: For this purpose, we collect, align, and link various open-source
databases where medical facilities' information may be present. This work
allows us to evaluate each data source along various dimensions, such as
completeness, correctness, and interlinking with other sources, all critical
aspects of current knowledge representation technologies.
Conclusions: Our contributions directly benefit stakeholders in the
biomedical and health domain (patients, healthcare professionals, companies,
regulatory authorities, and researchers), who will now have a better overview
of the access to and distribution of medical facilities.
Related papers
- iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine [28.917691563659467]
The iASiS infrastructure is able to convert clinical notes into usable data.
Using semantic integration of data gives the opportunity to generate information rich, auditable and reliable.
Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.
arXiv Detail & Related papers (2024-07-09T10:52:19Z) - Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning [65.54680361074882]
Eye-gaze Guided Multi-modal Alignment (EGMA) framework harnesses eye-gaze data for better alignment of medical visual and textual features.
We conduct downstream tasks of image classification and image-text retrieval on four medical datasets.
arXiv Detail & Related papers (2024-03-19T03:59:14Z) - Platform for generating medical datasets for machine learning in public
health [0.0]
This paper demonstrates a concept of the platform for a sustainable generation of quality and reliable sets of multimodal medical data.
It collects data from different external sources, harmonizes it using a special service, anonymizes harmonized data, and labels processed data.
arXiv Detail & Related papers (2023-10-12T17:23:52Z) - A Review on Knowledge Graphs for Healthcare: Resources, Applications,
and Promises [53.48844796428081]
This work provides the first comprehensive review of healthcare knowledge graphs (HKGs)
It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches.
At the application level, we delve into the successful integration of HKGs across various health domains.
arXiv Detail & Related papers (2023-06-07T21:51:56Z) - Patchwork Learning: A Paradigm Towards Integrative Analysis across
Diverse Biomedical Data Sources [40.32772510980854]
"patchwork learning" (PL) is a paradigm that integrates information from disparate datasets composed of different data modalities.
PL allows the simultaneous utilization of complementary data sources while preserving data privacy.
We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources.
arXiv Detail & Related papers (2023-05-10T14:50:33Z) - EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations.
Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
arXiv Detail & Related papers (2022-08-01T18:47:03Z) - Lightweight Mobile Automated Assistant-to-physician for Global
Lower-resource Areas [9.978987200997686]
We designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data.
The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions.
arXiv Detail & Related papers (2021-10-28T14:02:16Z) - Cross-Domain Data Integration for Named Entity Disambiguation in
Biomedical Text [5.008513565240167]
We propose a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain.
We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining.
Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR.
arXiv Detail & Related papers (2021-10-15T17:38:16Z) - MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence
using Federated Evaluation [110.31526448744096]
We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data.
We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
arXiv Detail & Related papers (2021-09-29T18:09:41Z) - Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors.
We propose to model the behavior-related multi-source data streams with a local-global graph.
We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
arXiv Detail & Related papers (2021-03-23T11:10:04Z) - Learning Contextualized Document Representations for Healthcare Answer
Retrieval [68.02029435111193]
Contextual Discourse Vectors (CDV) is a distributed document representation for efficient answer retrieval from long documents.
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking.
arXiv Detail & Related papers (2020-02-03T15:47:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.