A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data
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 pat...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-08-01
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| Series: | Future Science OA |
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| Online Access: | https://www.future-science.com/doi/10.2144/fsoa-2020-0207 |
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| author | Patrick A Gladding Zina Ayar Kevin Smith Prashant Patel Julia Pearce Shalini Puwakdandawa Dianne Tarrant Jon Atkinson Elizabeth McChlery Merit Hanna Nick Gow Hasan Bhally Kerry Read Prageeth Jayathissa Jonathan Wallace Sam Norton Nick Kasabov Cristian S Calude Deborah Steel Colin Mckenzie |
| author_facet | Patrick A Gladding Zina Ayar Kevin Smith Prashant Patel Julia Pearce Shalini Puwakdandawa Dianne Tarrant Jon Atkinson Elizabeth McChlery Merit Hanna Nick Gow Hasan Bhally Kerry Read Prageeth Jayathissa Jonathan Wallace Sam Norton Nick Kasabov Cristian S Calude Deborah Steel Colin Mckenzie |
| author_sort | Patrick A Gladding |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-552cd80b07ea4cee9b08e830ba8f61da |
| institution | OA Journals |
| issn | 2056-5623 |
| language | English |
| publishDate | 2021-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Future Science OA |
| spelling | doaj-art-552cd80b07ea4cee9b08e830ba8f61da2025-08-20T02:25:48ZengTaylor & Francis GroupFuture Science OA2056-56232021-08-017710.2144/fsoa-2020-0207A machine learning PROGRAM to identify COVID-19 and other diseases from hematology dataPatrick A Gladding0Zina Ayar1Kevin Smith2Prashant Patel3Julia Pearce4Shalini Puwakdandawa5Dianne Tarrant6Jon Atkinson7Elizabeth McChlery8Merit Hanna9Nick Gow10Hasan Bhally11Kerry Read12Prageeth Jayathissa13Jonathan Wallace14Sam Norton15Nick Kasabov16Cristian S Calude17Deborah Steel18Colin Mckenzie191Department of Cardiology, Waitematā District Health Board, Auckland, New Zealand2Clinical Information Services, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand3Clinical laboratory, Waitematā District Health Board, Auckland, New Zealand4Department of Hematology, Waitematā District Health Board, Auckland, New Zealand5Department of Infectious diseases, Waitematā District Health Board, Auckland, New Zealand5Department of Infectious diseases, Waitematā District Health Board, Auckland, New Zealand5Department of Infectious diseases, Waitematā District Health Board, Auckland, New Zealand6Institute for Innovation & Improvement (i3), Waitematā District Health Board, Auckland, New Zealand6Institute for Innovation & Improvement (i3), Waitematā District Health Board, Auckland, New Zealand7Nanix Ltd, Dunedin, New Zealand8Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand9School of Computer Science, University of Auckland, Auckland, New Zealand10Sysmex New Zealand Ltd, Auckland, New Zealand10Sysmex New Zealand Ltd, Auckland, New ZealandAim: 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.https://www.future-science.com/doi/10.2144/fsoa-2020-0207biological ageCOVID-19full blood countheart failurehematologymachine learning |
| spellingShingle | Patrick A Gladding Zina Ayar Kevin Smith Prashant Patel Julia Pearce Shalini Puwakdandawa Dianne Tarrant Jon Atkinson Elizabeth McChlery Merit Hanna Nick Gow Hasan Bhally Kerry Read Prageeth Jayathissa Jonathan Wallace Sam Norton Nick Kasabov Cristian S Calude Deborah Steel Colin Mckenzie A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data Future Science OA biological age COVID-19 full blood count heart failure hematology machine learning |
| title | A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data |
| title_full | A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data |
| title_fullStr | A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data |
| title_full_unstemmed | A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data |
| title_short | A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data |
| title_sort | machine learning program to identify covid 19 and other diseases from hematology data |
| topic | biological age COVID-19 full blood count heart failure hematology machine learning |
| url | https://www.future-science.com/doi/10.2144/fsoa-2020-0207 |
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