Systematic Design and Evaluation of Social Determinants of Health Ontology (SDoHO)

• URL: http://arxiv.org/abs/2212.01941v2
• Date: Thu, 15 Jun 2023 16:24:10 GMT
• Title: Systematic Design and Evaluation of Social Determinants of Health Ontology (SDoHO)
• Authors: Yifang Dang, Fang Li, Xinyue Hu, Vipina K. Keloth, Meng Zhang, Sunyang Fu, Jingcheng Du, J. Wilfred Fan, Muhammad F. Amith, Evan Yu, Hongfang Liu, Xiaoqian Jiang, Hua Xu, Cui Tao
• Abstract summary: Social determinants of health (SDoH) have a significant impact on health outcomes and well-being. We propose an SDoH ontology (SDoHO) which represents fundamental SDoH factors and their relationships in a standardized and measurable way.
• Score: 19.90090257979115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social determinants of health (SDoH) have a significant impact on health outcomes and well-being. Addressing SDoH is the key to reducing healthcare inequalities and transforming a "sick care" system into a "health promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. The ontology formally models classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using clinical notes data and a national survey, showed satisfactory results. SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and providing a path toward health equity across populations.

Related papers

• CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability [0.0]
  We propose an integrated ontological model, the Common Semantic Data Model for Social Determinants of Health (CSSDH) CSSDH aims to achieve interoperability within the Continuity of Care Network.
  arXiv  Detail & Related papers  (2024-12-12T12:25:33Z)
• Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare [47.23120247002356]
  Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG.
  arXiv  Detail & Related papers  (2024-11-29T20:35:01Z)
• A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models [20.11590976578911]
  Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions.
  arXiv  Detail & Related papers  (2024-03-18T17:56:37Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [59.4999994297993]
  This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs) We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications. The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit [10.764842579064636]
  Social determinants of health (SDOH) play a crucial role in a person's health and well-being. Most risk prediction models based on electronic health records do not incorporate a comprehensive set of SDOH features. Our work links a publicly available EHR database, MIMIC-IV, to well-documented SDOH features.
  arXiv  Detail & Related papers  (2023-05-22T01:27:51Z)
• The Design and Implementation of a National AI Platform for Public Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
  The Italian National Health Service is adopting Artificial Intelligence through its technical agencies. Such a vast programme requires special care in formalising the knowledge domain. Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
  arXiv  Detail & Related papers  (2023-04-24T08:00:02Z)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)
• An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study [0.8602553195689513]
  This study sought to redefine the Healthy People 2030 SDoH taxonomy to accommodate the COVID-19 pandemic. We aim to implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data.
  arXiv  Detail & Related papers  (2021-06-16T16:48:55Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)

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.