The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity
Open accessAbstract
The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationPathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395
Clinical decision support in the era of artificial intelligence
Shortliffe, E. H., & Sepúlveda, M. J. (2018) · JAMA · doi:10.1001/jama.2018.17163 · Cited by 920
Cited by
Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070
Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020) · IEEE Access · doi:10.1109/access.2020.2992341 · Cited by 1,099
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naudé, W. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00978-0 · Cited by 368
Toraman, S., Alakus, T. B., & Turkoglu, I. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110122 · Cited by 327
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review
Channa, A., Popescu, N., Skibinska, J., & Burget, R. (2021) · Sensors · doi:10.3390/s21175787 · Cited by 209
Robotics and artificial intelligence in healthcare during COVID-19 pandemic: a systematic review
Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103902 · Cited by 199
Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19
Tsikala Vafea, M., Atalla, E., Georgakas, J., Shehadeh, F., Mylona, E. K., Kalligeros, M., & Mylonakis, E. (2020) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-020-00629-w · Cited by 153
Syrowatka, A., Kuznetsova, M., Alsubai, A., Beckman, A. L., Bain, P. A., Craig, K. J. T., Hu, J., Jackson, G. P., Rhee, K., & Bates, D. W. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00459-8 · Cited by 130
Machine learning to assist clinical decision-making during the COVID-19 pandemic
Debnath, S., Barnaby, D. P., Coppa, K., Makhnevich, A., Kim, E. J., Chatterjee, S., Tóth, V., Levy, T. J., Paradis, M. D., Cohen, S. L., et al. (2020) · Bioelectronic Medicine · doi:10.1186/s42234-020-00050-8 · Cited by 104
A systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87
JAK1 Inhibition Blocks Lethal Immune Hypersensitivity in a Mouse Model of Down Syndrome
Tuttle, K. D., Waugh, K. A., Araya, P., Minter, R., Orlicky, D. J., Ludwig, M., Andrysik, Z., Burchill, M. A., Tamburini, B. A. J., Sempeck, C., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108407 · Cited by 72
Rinderknecht, M. D., & Klopfenstein, Y. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00482-9 · Cited by 23
Metadata
- Authors
- Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., Wu, Z., He, G., & Huang, Y.
- Venue
- Computers, Materials & Continua
- Publisher
- Tech Science Press
- Published
- 2020
- Volume
- 62
- Issue
- 3
- Pages
- 537-551
- ISSN
- 1546-2218 (ISSN-L)
- 1546-2226
- OpenAlex
- W3014289208
- Cited by
- 445
- References
- 5
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
