Publications by authors named "Min-Shian Wang"
The application of machine learning for identifying frailty in older patients during hospital admission.
BMC Med Inform Decis Mak
September 2024
Article Synopsis
- The study aimed to use machine learning (ML) to identify frailty in older patients hospitalized with acute illnesses to reduce related medical issues.* -
- Four ML models were compared for frailty prediction, with Support Vector Machine (SVM) performing the best in terms of accuracy and precision, using data from 3367 patients, of which 2843 were identified as frail.* -
- Key features influencing frailty prediction included age, gender, and various clinical indicators, showing that accessible hospital data can effectively predict frailty in older patients.*
Article Synopsis
- A study in Taiwan focused on renal recovery after dialysis-requiring acute kidney injury (AKI-D), examining 1,381 ICU patients from 2015 to 2020, finding that 27.3% achieved recovery.
- Researchers used machine learning to create models predicting successful dialysis liberation before discharge, with the XGBoost model showing strong performance (AUC of 0.85).
- Key predictors for renal recovery included urine output, comorbidity levels, vital signs trends, and lactate levels, with the aim of improving clinical decision-making in critical care settings.
Objective: The aim of this study was to develop an artificial intelligence-based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data.
Method: The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS.
Background: Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients.
Methods: We retrospectively included patients who were admitted to intensive care units during 2015-2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data.
Fluid balance is an essential issue in critical care; however, the impact of early fluid balance on the long-term mortality in critically ill surgical patients remains unknown. This study aimed to address the impact of day 1-3 and day 4-7 fluid balance on the long-term mortality in critically ill surgical patients. We enrolled patients who were admitted to surgical intensive care units (ICUs) during 2015-2019 at a tertiary hospital in central Taiwan and retrieved date-of-death from the Taiwanese nationwide death registration profile.
View Article and Find Full Text PDFThis study aimed to develop an early prediction model for identifying patients with bloodstream infections. The data resource was taken from 2015 to 2019 at Taichung Veterans General Hospital, and a total of 1647 bloodstream infection episodes and 3552 non-bloodstream infection episodes in the intensive care unit (ICU) were included in the model development and evaluation. During the data analysis, 30 clinical variables were selected, including patients' basic characteristics, vital signs, laboratory data, and clinical information.
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