Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt–Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.
Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.
BACKGROUND Postoperative length of stay is a key indicator in the management of medical resources and an indirect parameter of the incidence of surgical complications and recovery of systemic conditions in cancer surgery. To our knowledge, machine learning models have not been used to predict prolonged length of stay after cancer surgery using extensive medical information. OBJECTIVE To develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach. METHODS In our retrospective study, electronic medical records (EHR) of 42,751 patients who underwent primary surgery for 17 types of cancer from January 1, 2000 to December 31, 2017, sourced from a single cancer center, were used. Those records include various variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multiple layer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer is defined as bed-days of the group accounting for top 50% of the distribution of bed-days by cancer type. RESULTS In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrate excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve (AUC) > 0.85). A moderate performance (AUC: 0.70–0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases, the extreme gradient boosting classifier model outperformed the other models. We identified risk variables for the prediction of prolonged postoperative length of stay for each cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables. CONCLUSIONS A machine learning approach using EHR may improve the prediction of prolonged length of stay after primary cancer surgery. This algorithm may help in a more effective allocation of medical resources in cancer surgery. CLINICALTRIAL This study was approved by the institutional review board of the National Cancer Center-Korea, with a waiver for written informed consent (NCC-2018-0113).
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