Andrew Michelson

Medicine & Life Sciences

1. Coronavirus 100%
2. SARS Virus 87%
3. Severe Acute Respiratory Syndrome 51%
4. Patients' Rooms 49%
5. Sepsis 49%
6. Pandemics 47%
7. Mortality 46%
8. Machine Learning 42%
9. Cross Infection 39%
10. Electronic Health Records 38%
11. Infections 35%
12. Early Warning Score 31%
13. Anti-Bacterial Agents 30%
14. Confidence Intervals 28%
15. Deep Learning 27%
16. Drug Repositioning 27%
17. Sex Factors 25%
18. Type II DNA Topoisomerase 25%
19. Heuristics 25%
20. Bronchiolitis 24%
21. Delivery of Health Care 23%
22. Cochlear Implants 23%
23. Transplant Recipients 23%
24. Carbapenems 23%
25. Age Distribution 22%
26. Renin-Angiotensin System 20%
27. Intensive Care Units 20%
28. Obstructive Sleep Apnea 20%
29. Artificial Respiration 19%
30. Hematopoietic Stem Cells 19%
31. Datasets 19%
32. Drug Interactions 19%
33. Treatment Failure 19%
34. Acute Myeloid Leukemia 18%
35. Acute Kidney Injury 18%
36. Hematopoietic Stem Cell Transplantation 18%
37. Human Influenza 18%
38. Pneumonia 17%
39. Opioid Analgesics 17%
40. Logistic Models 17%
41. Estrogens 16%
42. Data Anonymization 16%
43. Respiratory System 14%
44. Population 14%
45. Hormones 14%
46. Phosphorylation 13%
47. ROC Curve 13%
48. Big Data 13%
49. Terminology 13%
50. Antihypertensive Agents 13%