ArticleScience advances2021
Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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Who cites it
98 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Immune checkpoint inhibitors use and effects on prognosis of COVID-19 infection: a systematic review and meta-analysis.Immunotherapy · 2021Pooled it
- Predicting low oxygen in patients with acute COVID-19 infection isolating at home: a clinical prediction model.BMJ open respiratory research · 2026Trial
- Self-reported symptom study of COVID-19 chemosensory dysfunction in Malaysia.Scientific reports · 2022Trial
- COVIDmeter - a questionnaire-based symptom monitoring system for the surveillance of COVID-19 in Denmark, 2020-2023.Epidemiology and infection · 2025Article
- SARS-CoV-2 Infection Aggravates Physical and Mental Health in Cancer Patients Compared to Co-Living Individuals.Cancer medicine · 2025Observational
- Profiling the persistent and episodic nature of long COVID symptoms and the impact on quality of life and functional status: a cohort observation study.Journal of global health · 2025Observational
- Development and validation of an instrument for tracking and analyzing recovery from symptoms associated with COVID-19.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2025Article
- Temporal, spatial and demographic distributions characteristics of COVID-19 symptom clusters from chinese medicine perspective: a systematic cross-sectional study in China from 2019 to 2023.Chinese medicine · 2024Article
- Rapid prediction of acute thrombosis via nanoengineered immunosensors with unsupervised clustering for multiple circulating biomarkers.Science advances · 2024Article
- Symptom clusters and symptom networks of symptom experiences in patients with SARS-CoV-2 infection.Heliyon · 2024Article
- Article
- Uncovering hidden and complex relations of pandemic dynamics using an AI driven system.Scientific reports · 2024Article
- Article
- Symptom Clusters in Acute SARS-CoV-2 Infection and Long COVID Fatigue in Male and Female Outpatients.Journal of personalized medicine · 2024Article
- Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets.Scientific reports · 2023Article
- SARS-CoV-2 infection following booster vaccination: Illness and symptom profile in a prospective, observational community-based case-control study.The Journal of infection · 2023Observational
- Clusters of SARS-CoV-2 Infection Across Six Schools for Students with Intellectual and Developmental Disabilities.Infectious diseases and therapy · 2023Article
- A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.PLOS digital health · 2023Article
- Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample.BMJ open · 2023Article
- Daily Oxygenation Support for Patients Hospitalized With SARS-CoV-2 in an Integrated Health System.Respiratory care · 2023Observational
38 more citing papers are in PubMed but not listed here.
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Authors and funding
33 authors.
Funding
Abstract
As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.
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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.