Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices
Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the S...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/13/12/1625 |
| _version_ | 1797380436771471360 |
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| author | Mohammed M. Ali Subi Gandhi Samian Sulaiman Syed H. Jafri Abbas S. Ali |
| author_facet | Mohammed M. Ali Subi Gandhi Samian Sulaiman Syed H. Jafri Abbas S. Ali |
| author_sort | Mohammed M. Ali |
| collection | DOAJ |
| description | Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (<i>n</i> = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (<i>n</i> = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies. |
| first_indexed | 2024-03-08T20:37:13Z |
| format | Article |
| id | doaj.art-d5c4e8194742434f96def8747e7e856f |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-08T20:37:13Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-d5c4e8194742434f96def8747e7e856f2023-12-22T14:19:45ZengMDPI AGJournal of Personalized Medicine2075-44262023-11-011312162510.3390/jpm13121625Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency ChoicesMohammed M. Ali0Subi Gandhi1Samian Sulaiman2Syed H. Jafri3Abbas S. Ali4Multidisciplinary Studies Programs, Eberly College of Arts and Sciences, West Virginia University, Morgantown, WV 26506, USADepartment of Medical Lab Sciences, Public Health and Nutrition Science, Tarleton State University, 1333 West Washington, Stephenville, TX 76402, USADepartment of Cardiology, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV 26501, USADepartment of Accounting, Finance and Economics, Tarleton State University, 1333 West Washington, Stephenville, TX 76402, USADepartment of Cardiology, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV 26501, USACardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (<i>n</i> = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (<i>n</i> = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.https://www.mdpi.com/2075-4426/13/12/1625CVDartificial intelligencelarge language modelCVD mortalitysocial vulnerability indexdigital literacy |
| spellingShingle | Mohammed M. Ali Subi Gandhi Samian Sulaiman Syed H. Jafri Abbas S. Ali Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices Journal of Personalized Medicine CVD artificial intelligence large language model CVD mortality social vulnerability index digital literacy |
| title | Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices |
| title_full | Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices |
| title_fullStr | Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices |
| title_full_unstemmed | Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices |
| title_short | Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices |
| title_sort | mapping the heartbeat of america with chatgpt 4 unpacking the interplay of social vulnerability digital literacy and cardiovascular mortality in county residency choices |
| topic | CVD artificial intelligence large language model CVD mortality social vulnerability index digital literacy |
| url | https://www.mdpi.com/2075-4426/13/12/1625 |
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