34 results on '"Radakovich, Nathan A."'
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2. Why Machine Learning Should Be Taught in Medical Schools
3. A geno-clinical decision model for the diagnosis of myelodysplastic syndromes
4. Personalized Prediction of Hospital Mortality in COVID-19–Positive Patients
5. Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study
6. Acute myeloid leukemia and artificial intelligence, algorithms and new scores
7. Machine learning in haematological malignancies
8. Lessons Learned in Using Laser Interstitial Thermal Therapy for Treatment of Brain Tumors: A Case Series of 238 Patients from a Single Institution
9. Artificial Intelligence in Hematology: Current Challenges and Opportunities
10. The future of artificial intelligence in healthcare
11. Contributors
12. Mimicry of an HIV broadly neutralizing antibody epitope with a synthetic glycopeptide
13. A Machine Learning Model of Response to Hypomethylating Agents in Myelodysplastic Syndromes
14. Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes
15. Chapter 16 - The future of artificial intelligence in healthcare
16. A Personalized Clinical-Decision Tool to Improve the Diagnostic Accuracy of Myelodysplastic Syndromes
17. Why Machine Learning Should Be Taught in Medical Schools (Preprint)
18. The effect of antibiotic use within 30 days of initiation of immune checkpoint inhibitor (ICI) efficacy in patients with metastatic urothelial carcinoma (mUC) in real-world setting.
19. Impact of primary tumor location, histology, and host factors on objective response to immune checkpoint inhibitors in metastatic urothelial carcinoma.
20. Personalized Transcriptomic Analyses Identify Unique Signatures That Correlate with Genomic Subtypes in Acute Myeloid Leukemia (AML) Using Explainable Artificial Intelligence
21. Multicenter Validation of a Personalized Model to Predict Hypomethylating Agent Response in Myelodysplastic Syndromes (MDS)
22. Genotype-Phenotype Correlations in Patients with Myeloid Malignancies Using Explainable Artificial Intelligence
23. Machine Learning in Oncology: What Should Clinicians Know?
24. The effect of antibiotic use on immune-checkpoint inhibitor efficacy in patients with advanced urothelial carcinoma.
25. A personalized prediction model for hospital readmission risk for cancer patients.
26. Molecular dissection of normal karyotype acute myeloid leukemia.
27. The Impact of Clinical Decision Support Alerts onClostridioides difficileTesting: A Systematic Review
28. A Personalized Prediction Model to Risk Stratify Patients with Acute Myeloid Leukemia (AML) Using Artificial Intelligence
29. Geno-Clinical Model for the Diagnosis of Bone Marrow Myeloid Neoplasms
30. Predicting Response to Hypomethylating Agents in Patients with Myelodysplastic Syndromes (MDS) Using Artificial Intelligence (AI)
31. Spatial heterogeneity and organization of tumor mutation burden and immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer
32. The Impact of Clinical Decision Support Alerts on Clostridioides difficile Testing: A Systematic Review.
33. In Vivo validation of novel p53-independent therapeutic modalities for small cell lung cancer (SCLC).
34. Virus-like Particles Identify an HIV V1V2 Apex-Binding Neutralizing Antibody that Lacks a Protruding Loop
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