Selected article for: "early stage and ground truth"
Author: Hsu, A. I.; Yeh, A.; Chen, S.-L.; Yeh, J. J.; Lv, D.; Hsu, J. Y.-j.; Huang, P. J.
Title: AI4CoV: Matching COVID-19 Patients to Treatment Options Using Artificial Intelligence
  • Cord-id: l04h7l8f
  • Document date: 2020_11_30
    • ID: l04h7l8f
    Snippet: We developed AI4CoV, a novel AI system to match thousands of COVID-19 clinical trials to patients based on each patient's eligibility to clinical trials in order to help physicians select treatment options for patients. AI4CoV leveraged Natural Language Processing (NLP) and Machine Learning to parse through eligibility criteria of trials and patients' clinical manifestations in their clinical notes, both presented in English text, to accomplish 92.76% AUROC on a cross-validation test with 3,156
    Document: We developed AI4CoV, a novel AI system to match thousands of COVID-19 clinical trials to patients based on each patient's eligibility to clinical trials in order to help physicians select treatment options for patients. AI4CoV leveraged Natural Language Processing (NLP) and Machine Learning to parse through eligibility criteria of trials and patients' clinical manifestations in their clinical notes, both presented in English text, to accomplish 92.76% AUROC on a cross-validation test with 3,156 patient-trial pairs labeled with ground truth of suitability. Our retrospective multiple-site review shows that according to AI4CoV, severe patients of COVID-19 generally have less treatment options suitable for them than mild and moderate patients and that suitable and unsuitable treatment options are different for each patient. Our results show that the general approach of AI4CoV is useful during the early stage of a pandemic when the best treatments are still unknown.

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