ArticleComputers in biology and medicine2022
Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.Progress in biophysics and molecular biology · 2023Pooled it
- Association of thyroid dysfunction and COVID-19: A systematic review and meta-analysis.Frontiers in endocrinology · 2022Pooled it
- Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.Current gene therapy · 2025Article
- Laboratory Puzzle of Oxidative Stress, Parameters of Hemostasis and Inflammation in Hospitalized Patients with COVID-19.Biomedicines · 2024Article
- ChAdOx1 nCoV-19 Vaccine and Thrombosis with Thrombocytopenia Syndrome among Adults: A Systematic Review.Advanced pharmaceutical bulletin · 2023Review
- Minimizing outbreak through targeted blocking for disease control: a community-based approach using super-spreader node identification.Scientific reports · 2023Article
- Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.Multimedia tools and applications · 2023Article
- Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes.Computers in biology and medicine · 2023Article
- Face mask detection and social distance monitoring system for COVID-19 pandemic.Multimedia tools and applications · 2023Article
- A Review of Virus-Like Particle-Based SARS-CoV-2 Vaccines in Clinical Trial Phases.Iranian journal of pharmaceutical research : IJPR · 2022Review
- Comparative Study on International Research Hotspots and National-Level Policy Keywords of Dynamic Disaster Monitoring and Early Warning in China (2000-2021).International journal of environmental research and public health · 2022Article
- Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques.International journal of environmental research and public health · 2022Review
- Socioeconomic impact due to COVID-19: An empirical assessment.Information processing & management · 2022Article
- Autopsy Findings and Inflammatory Markers in SARS-CoV-2: A Single-Center Experience.International journal of general medicine · 2022Article
- A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.Informatics in medicine unlocked · 2022Article
- Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh.Sensors (Basel, Switzerland) · 2021Article
- Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review.Iranian journal of pharmaceutical research : IJPRReview
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22 authors.
Funding
Abstract
One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O
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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.