| 한글제목(Korean Title) |
Explainable Machine Learning Based a Packed Red Blood Cell Transfusion Prediction and Evaluation for Major Internal Medical Condition |
| 영문제목(English Title) |
Explainable Machine Learning Based a Packed Red Blood Cell Transfusion Prediction and Evaluation for Major Internal Medical Condition |
| 저자(Author) |
Seongbin Lee
Seunghee Lee
Duhyeuk Chang
Mi-Hwa Song
Jong-Yeup Kim
Suehyun Lee
|
| 원문수록처(Citation) |
VOL 18 NO. 03 PP. 0302 ~ 0310 (2022. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Efficient use of limited blood products is becoming very important in terms of socioeconomic status and patient recovery. To predict the appropriateness of patient-specific transfusions for the intensive care unit (ICU) patients who require real-time monitoring, we evaluated a model to predict the possibility of transfusion dynamically by using the Medical Information Mart for Intensive Care III (MIMIC-III), an ICU admission record at Harvard Medical School. In this study, we developed an explainable machine learning to predict the possibility of red blood cell transfusion for major medical diseases in the ICU. Target disease groups that received packed red blood cell transfusions at high frequency were selected and 16,222 patients were finally extracted. The prediction model achieved an area under the ROC curve of 0.9070 and an F1-score of 0.8166 (LightGBM). To explain the performance of the machine learning model, feature importance analysis and a partial dependence plot were used. The results of our study can be used as basic data for recommendations related to the adequacy of blood transfusions and are expected to ultimately contribute to the recovery of patients and prevention of excessive consumption of blood products. |
| 키워드(Keyword) |
Explainable AI
Feature Importance Analysis
LightGBM
Partial Dependence Plot
prediction
Transfusion
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| 파일첨부 |
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