Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes
| العنوان: | Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes |
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| المؤلفون: | Risa B. Myers, Joseph R. Ruiz, John C. Frenzel, Chris Jermaine |
| المصدر: | ACM Transactions on Knowledge Discovery from Data. 10:1-27 |
| بيانات النشر: | Association for Computing Machinery (ACM), 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Dynamic time warping, General Computer Science, Database, business.industry, Computer science, Best practice, Feature extraction, Vital signs, Feature selection, 02 engineering and technology, computer.software_genre, Machine learning, Ordinal regression, Regression, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Hidden Markov model, computer |
| الوصف: | Anesthesiologists are taught to carefully manage patient vital signs during surgery. Unfortunately, there is little empirical evidence that vital sign management, as currently practiced, is correlated with patient outcomes. We seek to validate or repudiate current clinical practice and determine whether or not clinician evaluation of surgical vital signs correlate with outcomes. Using a database of over 90,000 cases, we attempt to determine whether those cases that anesthesiologists would subjectively decide are “low quality” are more likely to result in negative outcomes. The problem reduces to one of multi-dimensional time-series classification. Our approach is to have a set of expert anesthesiologists independently label a small number of training cases, from which we build classifiers and label all 90,000 cases. We then use the labeling to search for correlation with outcomes and compare the prevalence of important 30-day outcomes between providers. To mimic the providers’ quality labels, we consider several standard classification methods, such as dynamic time warping in conjunction with a kNN classifier, as well as complexity invariant distance, and a regression based upon the feature extraction methods outlined by Mao et al. 2012 (using features such as time-series mean, standard deviation, skew, etc.). We also propose a new feature selection mechanism that learns a hidden Markov model to segment the time series; the fraction of time that each series spends in each state is used to label the series using a regression-based classifier. In the end, we obtain strong, empirical evidence that current best practice is correlated with reduced negative patient outcomes. We also learn that all of the experts were able to significantly separate cases by outcome, with higher prevalence of negative 30-day outcomes in the cases labeled as “low quality” for almost all of the outcomes investigated. |
| تدمد: | 1556-472X 1556-4681 |
| DOI: | 10.1145/2822897 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::930f59ec3911ccf6632b098ce5d2197b https://doi.org/10.1145/2822897 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........930f59ec3911ccf6632b098ce5d2197b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1556472X 15564681 |
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| DOI: | 10.1145/2822897 |