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Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records. | LitMetric

AI Article Synopsis

  • - The study focuses on using free text from electronic health records (EHRs) to detect anastomosis leakage (AL), a serious complication after colorectal cancer (CRC) surgery, before it occurs.
  • - Researchers applied a bag-of-words model and various feature selection strategies using a robust support vector machine to analyze the high-dimensional data from EHRs.
  • - Their findings showcased a high sensitivity (100%) and moderate specificity (72%) for early detection of complications, which could aid in developing predictive models to assist surgeons and patients in making informed preoperative decisions.

Article Abstract

The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.

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Source
http://dx.doi.org/10.1109/JBHI.2014.2361688DOI Listing

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