Use case-focused metrics to evaluate machine learning for diseases involving parasite loads. (arXiv:2209.06947v1 [cs.LG])

Communal hill-climbing, via comparison of algorithm performances, can greatly
accelerate ML research. However, it requires task-relevant metrics. For
diseases involving parasite loads, e.g., malaria and neglected tropical
diseases (NTDs) such as schistosomiasis, the metrics currently reported in ML
papers (e.g., AUC, F1 score) are ill-suited to the clinical task. As a result,
the hill-climbing system is not enabling progress towards solutions that
address these dire illnesses. Drawing on examples from malaria and NTDs, this
paper highlights two gaps in current …

arxiv case diseases machine machine learning metrics