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Related Questions
- What are the implications of prioritizing precision over recall in medical text classification, and how might it affect patient outcomes?
- How do the consequences of false negatives (false dismissals) compare to false positives (false alarms) in medical text classification, and what are the trade-offs?
- Can you explain how the choice of evaluation metric (e.g., accuracy, F1-score) influences the trade-off between precision and recall in medical text classification?
- What are some techniques for optimizing precision while minimizing the loss of recall, and vice versa, in medical text classification?
- How does the complexity of the medical concept being classified impact the trade-off between precision and recall, and what are some strategies for addressing this challenge?
- What role do domain-specific ontologies and knowledge graphs play in balancing precision and recall in medical text classification?
- Can you discuss the impact of incorporating human annotation and feedback on the trade-off between precision and recall in medical text classification?
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