RESUMO
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.
RESUMO
The interrelationships between ventilation rate, indoor air quality, and energy consumption in operation rooms at rest are yet to be understood. We investigate the effect of ventilation rate on indoor air quality indices and energy consumption in ORs at rest. The study investigates the air temperature, relative humidity, concentrations of carbon dioxide, particulate matter (PM), and airborne bacteria at different ventilation rates in operation rooms at rest of a medical center. The energy consumption and cost analysis of the heating, ventilating, and air conditioning (HVAC) system in the operation rooms at rest were also evaluated for all ventilation rates. No air-conditioned operation rooms had very highest PM and airborne bacterial concentrations in the operation areas. The bacterial concentration in the operation areas with 6-30 air changes per hour (ACH) was below the suggested level set by the United Kingdom (UK) for an empty operation room. A 70% of reduction in annual energy cost by reducing the ventilation rate from 30 to 6 ACH was found in the operation rooms at rest. Maintenance of operation rooms at ventilation rate of 6 ACH could save considerable amounts of energy and achieve the goal of air cleanliness.
