A Novel Machine Learning Based Screening Method For High-Risk Covid-19 Patients Based On Simple Blood Exams

Darapaneni, Narayana; Gupta, Mohit; Paduri, Anwesh Reddy; Agrawal, Richa; Padasali, Sachin; Kumari, Abha; Purushothaman, P

doi:10.1109/iemtronics52119.2021.9422534

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…Classification of COVID-19 severity into triage categories has was also carried out on clinical data and laboratory tests obtained during patient examination in ED [31] and 26 blood routine indicators and several demographic features [26] using a specifically developed RF-SMA-SVM model. A novel SV-LAR model was exhibited for triage based on blood sample routine data [34], in line with other approaches exploiting blood sample test data for early triage [48] Some other studies also focused on the differential diagnosis of COVID-19 from other similar diseases or to detect patients with high risk of future lung diseases, such as a diagnostic model to aid in the early identification of suspected COVID-19 pneumonia patients [24] on admission in fever clinics. Others classified the diagnosis of the patients into three categories: COVID-19 pneumonia, non-COVID-19 pneumonia and the healthy ones [43].…”

Section: Patient Triage Methodsmentioning

confidence: 70%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

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Section: Patient Triage Methodsmentioning

confidence: 70%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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“…The suggestion that Covid-19 series intervals are shorter than those of Severe Acute Respiratory Syndrome (SARS) could introduce a bias in the calculations of SARS series intervals.Post-mortem SARS-coronavirus-2 (SARS-CoV-2) samples can be used to research pathological features of deceased patients. However, no pathology report was available due to the difficulty performing excision and biopsy [21]. The time course and severity of COVID-19 findings on chest radiographs and identifies severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by nuclear detection and real-time reverse transcription polymerase chain reaction (RT-PCR) detection.…”

Section: Literature Surveymentioning

confidence: 99%

Design of Covid19 Disease Detection for Risk Identification using Deep Learning Approach

Rahul Sanmugam Gopi,

Lavanya Dhanesh,

Mohammad Aljanabi

et al. 2023

ARASET

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In the twentieth century, various diseases of different variants and countless epidemics like Covid-19 have emerged worldwide. Covid-19 has been stated as a global pandemic; fly infestation is a significant cause of pollution. And it makes the international economic community vulnerable to attacks. The current coronavirus is affecting one of the essential epidemics—early detection of pre-malignant disease at low cost and appropriate isolation of patients with either covid-19 or non-covid-19. In addition to often taking a long time to diagnose the exact disease of the coronavirus, it is prone to human error. A covid19 recognition design using Deep Learning (DL) and Recursive Relational Feature Selection (RRFS) is proposed to overcome this shortcoming by implementing Deep Support Neural Networks (DSNN) models for the early detection of coronaviruses. Initially, a test dataset of Covid19 samples is collected, and during training, the raw dataset process can be started using specific models to remove unwanted noise in the grid. Then, the incompletely processed dataset can be introduced into the feature selection process to determine the best features for covid19 RRFS. We recently proposed a DSNN algorithm for classifying coronaviruses and their detection accuracy. This model can be used for timely and accurate diagnosis of various stages of corona infection. Thus, they effectively detect the apparent result of Covid-19 and achieve reliable performance compared to earlier methods.

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“…Early detection of COVID-19 and health monitoring of patients are urgent issues to be concerned by health service authorities in order to isolate infected people, provide timely treatment [32], and also to prevent the spread of the disease [30]. There were 210 articles identified from the dataset discussing this topic.…”

Section: Topic Hotspotsmentioning

confidence: 99%

Machine learning in handling disease outbreaks: a comprehensive review

Riswantini

Nugraheni

2022

Bulletin EEI

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The changes in the global environment have made impact on the evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with new technological advances. This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks. Bibliometric analysis was conducted on 3,447 scientific articles selected from the Scopus database. Further, latent dirichlet analysis (LDA) method was applied to identify the topic hotspots in attempting to deepen the analysis. The LDA results identified twelve topic hotspots that can be classified into three themes: COVID-19 disease, miscellaneous diseases, and public opinion on disease outbreaks for discussion. The study reveals that the scientific structure of this domain is dominated by machine learning research on COVID-19 diseases and miscellaneous diseases caused by pathogens or some genetic factors. A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, and pathogenesis. Public opinions toward new diseases are also an interesting topic in addition to the public perceptions in response to the health protocol and policies.

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A Novel Machine Learning Based Screening Method For High-Risk Covid-19 Patients Based On Simple Blood Exams

Cited by 10 publications

References 16 publications

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Design of Covid19 Disease Detection for Risk Identification using Deep Learning Approach

Machine learning in handling disease outbreaks: a comprehensive review

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