ArticleNPJ digital medicine2018
Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits.
Article in NPJ digital medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Pooled it
- Digital Biomarker-Based Interventions: Systematic Review of Systematic Reviews.Journal of medical Internet research · 2022Pooled it
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges.BMC medical ethics · 2025Article
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- The digital intelligent precise nursing framework: theory development in health recommender system.BMC nursing · 2025Article
- Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach.JMIR cancer · 2025Article
- A novel tool using social and environmental determinants of health to assess pediatric asthma in the emergency department.Journal of the American College of Emergency Physicians open · 2024Article
- Article
- The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study.Frontiers in oncology · 2024Article
- Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review.Journal of medical Internet research · 2023Article
- Indoor Bacterial and Fungal Burden in "Moldy" versus "Non-Moldy" Homes: A Case Study Employing Advanced Sequencing Techniques in a US Metropolitan Area.Pathogens (Basel, Switzerland) · 2023Article
- The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance.Disaster medicine and public health preparedness · 2022Article
- Article
- Digital Biomarker-Based Studies: Scoping Review of Systematic Reviews.JMIR mHealth and uHealth · 2022Article
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Social Disparities of Pain and Pain Intensity Among Women Diagnosed With Early Stage Breast Cancer.Frontiers in oncology · 2022Article
- Outcomes of Digital Biomarker-Based Interventions: Protocol for a Systematic Review of Systematic Reviews.JMIR research protocols · 2021Article
- A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.SSM - population health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarker-based model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.
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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.