| Autor: |
Lin C; School of Medicine, National Defense Medical Center, Taipei 114, Taiwan.; School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.; Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan.; Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan., Lee YT; Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei 112, Taiwan., Wu FJ; Department of Informatics, Taoyuan Armed Forces General Hospital, Taoyuan 325, Taiwan., Lin SA; Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan., Hsu CJ; Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan., Lee CC; Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.; Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan., Tsai DJ; School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.; Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan.; Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan., Fang WH; Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.; Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan. |
| Abstrakt: |
Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set ( n = 74,959) and testing set ( n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement ( p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician's score. |
