a-g SHapley Additive exPlanation (SHAP) values for model features by class (MIS-C/Non-MIS-C PASC).

<p>The plots show the most significant features as determined by the sum of SHAP value magnitudes over all samples. For each feature, SHAP values for each patient are plotted, with color representing the feature value (e.g. red if feature was present and blue if absent in case of a binary vari...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Vitaly Lorman (16814000) (author)
مؤلفون آخرون:	Hanieh Razzaghi (599184) (author), Xing Song (5815787) (author), Keith Morse (16814003) (author), Levon Utidjian (16814006) (author), Andrea J. Allen (16814009) (author), Suchitra Rao (265585) (author), Colin Rogerson (16814012) (author), Tellen D. Bennett (10701123) (author), Hiroki Morizono (6167360) (author), Daniel Eckrich (16814015) (author), Ravi Jhaveri (362195) (author), Yungui Huang (509186) (author), Daksha Ranade (16814018) (author), Nathan Pajor (16814021) (author), Grace M. Lee (10050450) (author), Christopher B. Forrest (7252067) (author), L. Charles Bailey (16814024) (author)
منشور في:	2023
الموضوعات:	Medicine Biotechnology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified multisystem inflammatory syndrome health systems data assessed using 5 pasc diagnosis codes clinical definition evolves specific features contribute patient features included validated grid search pedsnet ehr network machine learning algorithm 2 test results reliably identify patients identify pasc patients used less frequently machine learning less reliably medications using clinical understanding clinical trials procedure features identifying patients xlink "> positive sars long covid higher recall higher precision feature importance diagnostic testing better understanding based phenotype acute sequelae
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a-g SHapley Additive exPlanation (SHAP) values for model features by class (MIS-C/Non-MIS-C PASC).

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