Technical Name 智慧急診即時決策支援系統:住院安排、停留時間、暨相似病歷取回之妥善化技術
Project Operator National Taiwan University
Project Host 方震中
The core concept of the decision support system is to establish machine learning-based predictive modelsenable their interpretability to support physicians making clinical decisions in practice. The developed technique is part of the capstone project named "Smart Emergency Department," sponsored by the Ministry of ScienceTechnology. The system comprises NTUH (National Taiwan University Hospital) EMR (Electronic Medical Record) importance analysis, accurate clinical quality indicator prediction,similar medical record retrieval. It is expected to improve the patients' flowalleviate emergency department crowding.  It has been verified in retrospective studieswill officially enter the clinical trial phase this year.
Scientific Breakthrough
Over one million EMRs recordsplenty of features in the clinical timeline for each sample have been extracted through the retrospective research.  Collected EMRs were distributed across three regional hospitals.  The quantityquality of the current database are remarkableconvincible for paper publication. The best performance of the model achieves 0.94 of AUC in predicting both hospitalizationlength of stay, which is comparable to the state-of-the-art in the literature. The newly proposed technique, unsupervised quantification of similar medical recordsretrieval, is an innovative approach for the tree-based modelalso provides interpretability. Outcomes of subsequent clinical trials have the potential to publish results in high-impact clinical journals.
Industrial Applicability
The application domain of the technique is expected in the field of providing medical services. After complete prospective studyobtain clinical evidence successfully, the experienceparameters accumulated during the development period will serve for the promotion of smart healthcarethe foundation of commercial design, which can help system manufacturers to formulate compatible standards for the operation of intelligent modulesassist in the communication of medical records. In addition, help set up computing service equipment locallyprivately. After smart healthcare becomes more popular, small equipment factories can also develop peripheral products for smart healthcare, forming a supply chain of related industries.
Matching Needs
Keyword medical big data EMR machine learning clinical decision support system smart ED predictive model feature selection patient similarity medical record retrieval medical record retrieval
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