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Gladding PA, Ayar Z, Smith K, Patel P, Pearce J, Puwakdandawa S, Tarrant D, Atkinson J, McChlery E, Hanna M, Gow N, Bhally H, Read K, Jayathissa P, Wallace J, Norton S, Kasabov N, Calude CS, Steel D, Mckenzie C. A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data. Future Sci OA 2021; 7:FSO733. [PMID: 34254032 PMCID: PMC8204819 DOI: 10.2144/fsoa-2020-0207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 05/19/2021] [Indexed: 11/23/2022] Open
Abstract
AIM We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.
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Affiliation(s)
- Patrick A Gladding
- Department of Cardiology, Waitematā District Health Board, Auckland, New Zealand
| | - Zina Ayar
- Clinical Information Services, Waitematā District Health Board, Auckland, New Zealand
| | - Kevin Smith
- Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand
| | - Prashant Patel
- Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand
| | - Julia Pearce
- Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand
| | | | - Dianne Tarrant
- Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand
| | - Jon Atkinson
- Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand
| | - Elizabeth McChlery
- Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand
| | - Merit Hanna
- Department of Hematology, Waitematā District Health Board, Auckland, New Zealand
| | - Nick Gow
- Department of Infectious diseases, Waitematā District Health Board, Auckland, New Zealand
| | - Hasan Bhally
- Department of Infectious diseases, Waitematā District Health Board, Auckland, New Zealand
| | - Kerry Read
- Department of Infectious diseases, Waitematā District Health Board, Auckland, New Zealand
| | - Prageeth Jayathissa
- Institute for Innovation & Improvement (i3), Waitematā District Health Board, Auckland, New Zealand
| | - Jonathan Wallace
- Institute for Innovation & Improvement (i3), Waitematā District Health Board, Auckland, New Zealand
| | | | - Nick Kasabov
- Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand
| | - Cristian S Calude
- School of Computer Science, University of Auckland, Auckland, New Zealand
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