Applications of Artificial Intelligence in Thrombocytopenia
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Sepsis Associated Thrombocytopenia
3.1.1. Diagnosis
3.1.2. Prognosis
3.2. Drug-Induced Immune Thrombocytopenia
3.2.1. Diagnosis of DITP
3.2.2. Predicting Drugs Causing DITP
3.3. Hospital Acquired Thrombocytopenia
3.4. Immune Thrombocytopenia
3.4.1. Diagnosis
3.4.2. Prognosis
3.5. Severe Fever with Thrombocytopenia
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study (Year) | Outcome | Advantages | Disadvantages |
---|---|---|---|
Jiang, X., et al. (2022) [11] | Predicting SAT and severe SAT |
|
|
Ling, J., et al. (2021) [12] | Predicting the 28-day mortality risk in patients with SAT |
|
|
Takahashi, S., et al. (2021) [13] | Predicting LAT in patients taking linezolid treatment |
|
|
Marray, L., et al. (2022) [14] | Predicting LAT in ICU patients started on linezolid treatment |
|
|
Nilius, H., et al. (2022) [15] | Diagnosing HIT based on clinical and laboratory data |
|
|
Wang, B., et al. (2022) [16] | Distinguishing DITP toxicants from non-toxicants |
|
|
Cheng, Y., et al. (2021) [17] | Prediction of HAT risk in patients following surgery |
|
|
Miao, D., et al. (2020) [18] | Mapping global potential hotspots for SFTS Transmission |
|
|
Cho, G., S. Lee, and H. Lee (2021) [19] | Mapping SFTS Virus Transmission in South Korea |
|
|
Study (Year) | Outcomes | Best Models | Validation | AUC | ACC | SEN | SPE |
---|---|---|---|---|---|---|---|
Jiang, X., et al. (2022) [11] | Predicting Thrombocytopenia | NNET | Internal | 0.79 | NR | NR | NR |
External | 0.72 | 0.68 | NR | 0.71 | |||
Predicting Severe Thrombocytopenia | Bayes | Internal | 0.89 | NR | NR | NR | |
External | 0.77 | 0.68 | NR | 0.62 | |||
Ling, J., et al. (2021) [12] | Prognosis of SAT | XGBoost based on RDW | Internal | 0.646 | NR | 0.70 | 57 |
Takahashi, S., et al. (2021) [13] | Predicting LAT | CART | Internal | NR | NR | 0.922 | 0.783 |
Maray, I., et al. (2022) [14] | Predicting LAT | LogR Model 1 | Internal | 0.89 | 0.79 | 0.71 | 0.80 |
LogR Model 2 | Internal | 0.88 | 0.79 | 0.71 | 0.80 | ||
Nilius, H., et al. (2022) [15] | Diagnosing DIT using ELISA | SVM | Internal | 0.985 | NR | 0.89 | 0.95 |
Diagnosing DIT using CLIA | XGBoost | Internal | 0.989 | NR | 0.96 | 0.95 | |
Diagnosing DIT using PaGIA | SVM | Internal | 0.991 | NR | 1.00 | 0.95 | |
Wang, B., et al. (2022) [16] | Predicting DITP toxicity of drugs | k-NN based on RDMD-PubChem | Internal | 0.628 | 0.627 | 0.69 | 0.566 |
External | 0.769 | 0.756 | 0.833 | 0.704 | |||
Cheng, Y., et al. (2021) [17] | Predicting HAT following surgery | RF | Internal | 0.834 | NR | 0.793 | 0.791 |
GB | Internal | 0.829 | NR | 0.736 | 0.737 | ||
Miao, D., et al. (2020) [18] | Predicting Potential transmission of SFTS | BRT | Internal | 0.893 | NR | NR | NR |
Cho, G., S. Lee, and H. Lee (2021) [19] | Predicting SFTS Occurrence | GB | Internal | 0.986 | NR | NR | NR |
BT | Internal | 1.00 | NR | NR | NR |
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Elshoeibi, A.M.; Ferih, K.; Elsabagh, A.A.; Elsayed, B.; Elhadary, M.; Marashi, M.; Wali, Y.; Al-Rasheed, M.; Al-Khabori, M.; Osman, H.; Yassin, M. Applications of Artificial Intelligence in Thrombocytopenia. Diagnostics 2023, 13, 1060. https://doi.org/10.3390/diagnostics13061060
Elshoeibi AM, Ferih K, Elsabagh AA, Elsayed B, Elhadary M, Marashi M, Wali Y, Al-Rasheed M, Al-Khabori M, Osman H, Yassin M. Applications of Artificial Intelligence in Thrombocytopenia. Diagnostics. 2023; 13(6):1060. https://doi.org/10.3390/diagnostics13061060
Chicago/Turabian StyleElshoeibi, Amgad M., Khaled Ferih, Ahmed Adel Elsabagh, Basel Elsayed, Mohamed Elhadary, Mahmoud Marashi, Yasser Wali, Mona Al-Rasheed, Murtadha Al-Khabori, Hani Osman, and Mohamed Yassin. 2023. "Applications of Artificial Intelligence in Thrombocytopenia" Diagnostics 13, no. 6: 1060. https://doi.org/10.3390/diagnostics13061060