Machine learning algorithm for early-stage prediction of severe morbidity in COVID-19 pneumonia patients based on bio-signals.
Seung Min BaikKyung Tae KimHaneol LeeJung Hwa LeePublished in: BMC pulmonary medicine (2023)
The proposed algorithm can predict the exacerbation of severity three days in advance in patients with mild COVID-19. This prediction can help effectively manage the reallocation of appropriate medical resources in clinical settings. Therefore, this algorithm can facilitate adequate oxygen therapy and mechanical ventilator preparation, thereby improving patient prognosis, increasing the efficiency of medical systems, and mitigating the damage caused by a global pandemic.
Keyphrases
- machine learning
- coronavirus disease
- sars cov
- early stage
- deep learning
- end stage renal disease
- healthcare
- newly diagnosed
- ejection fraction
- artificial intelligence
- chronic kidney disease
- big data
- chronic obstructive pulmonary disease
- oxidative stress
- prognostic factors
- neural network
- peritoneal dialysis
- case report
- early onset
- stem cells
- mass spectrometry
- patient reported outcomes
- lymph node
- bone marrow
- intensive care unit
- respiratory failure
- molecularly imprinted
- liquid chromatography
- cell therapy
- smoking cessation
- solid phase extraction
- sentinel lymph node
- drug induced
- locally advanced