tetano
Editor, Senior Moderator
Sci Rep
. 2024 Jun 28;14(1):14892.
doi: 10.1038/s41598-024-63707-3. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings
Hooman H Rashidi[SUP] 1 [/SUP], Aamer Ikram[SUP] 2 [/SUP], Luke T Dang[SUP] 3 [/SUP], Adnan Bashir[SUP] 4 [/SUP], Tanzeel Zohra[SUP] 2 [/SUP], Amna Ali[SUP] 2 [/SUP], Hamza Tanvir[SUP] 2 [/SUP], Mohammad Mudassar[SUP] 2 [/SUP], Resmi Ravindran[SUP] 3 [/SUP], Nasim Akhtar[SUP] 5 [/SUP], Rana I Sikandar[SUP] 5 [/SUP], Mohammed Umer[SUP] 6 [/SUP], Naeem Akhter[SUP] 6 [/SUP], Rafi Butt[SUP] 7 [/SUP], Brandon D Fennell[SUP] 8 [/SUP], Imran H Khan[SUP] 9 [/SUP]
Affiliations
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
Keywords: Binary classifier; COVID-19; CTACK (CCL27); Chemokines; Cytokines; Eotaxin (CCL11); FGF basic (FGF2); G-CSF (CSF3); GM-CSF (CSF2); GRO-α (CXCL1); HGF; IFN-α2 (IFNA2); IFN-γ (IFNG); IL-12 (p40) (IL12B); IL-12 (p70) (IL12A); IL-18 (IL18); IL-1ra (IL1RN); IL-1α (IL1A); IL-1β (IL1B); IL-2Rα (IL2RA); IL10; IL13; IL15; IL16; IL17A; IL2; IL3; IL4; IL5; IL6; IL7; IL8 (CXCL8); IL9; IP-10 (CXCL10); LIF; M-CSF (CSF1); MCP-1 (MCAF) (CCL2); MCP-3 (CCL7); MIF; MIG (CXCL9); MIP-1α (CCL3); MIP-1β (CCL4); Machine learning; PDGF-BB (PDGFB); RANTES (CCL5); SCF (KITLG); SCGF-β (CLEC11A); SDF-1α (CXCL12); TNF-α; TNF-β (LTA); TRAIL (TNFSF10); VEGF (VEGFA); β-NGF (NGF).
. 2024 Jun 28;14(1):14892.
doi: 10.1038/s41598-024-63707-3. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings
Hooman H Rashidi[SUP] 1 [/SUP], Aamer Ikram[SUP] 2 [/SUP], Luke T Dang[SUP] 3 [/SUP], Adnan Bashir[SUP] 4 [/SUP], Tanzeel Zohra[SUP] 2 [/SUP], Amna Ali[SUP] 2 [/SUP], Hamza Tanvir[SUP] 2 [/SUP], Mohammad Mudassar[SUP] 2 [/SUP], Resmi Ravindran[SUP] 3 [/SUP], Nasim Akhtar[SUP] 5 [/SUP], Rana I Sikandar[SUP] 5 [/SUP], Mohammed Umer[SUP] 6 [/SUP], Naeem Akhter[SUP] 6 [/SUP], Rafi Butt[SUP] 7 [/SUP], Brandon D Fennell[SUP] 8 [/SUP], Imran H Khan[SUP] 9 [/SUP]
Affiliations
- PMID: 38937503
- DOI: 10.1038/s41598-024-63707-3
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
Keywords: Binary classifier; COVID-19; CTACK (CCL27); Chemokines; Cytokines; Eotaxin (CCL11); FGF basic (FGF2); G-CSF (CSF3); GM-CSF (CSF2); GRO-α (CXCL1); HGF; IFN-α2 (IFNA2); IFN-γ (IFNG); IL-12 (p40) (IL12B); IL-12 (p70) (IL12A); IL-18 (IL18); IL-1ra (IL1RN); IL-1α (IL1A); IL-1β (IL1B); IL-2Rα (IL2RA); IL10; IL13; IL15; IL16; IL17A; IL2; IL3; IL4; IL5; IL6; IL7; IL8 (CXCL8); IL9; IP-10 (CXCL10); LIF; M-CSF (CSF1); MCP-1 (MCAF) (CCL2); MCP-3 (CCL7); MIF; MIG (CXCL9); MIP-1α (CCL3); MIP-1β (CCL4); Machine learning; PDGF-BB (PDGFB); RANTES (CCL5); SCF (KITLG); SCGF-β (CLEC11A); SDF-1α (CXCL12); TNF-α; TNF-β (LTA); TRAIL (TNFSF10); VEGF (VEGFA); β-NGF (NGF).