Outlier detection for patient monitoring and alerting

Journal of Biomedical Informatics

Batal

Hong

et al. 2016

Self Cite

Medical errors remain a significant problem in healthcare. This paper investigates a data-driven outlier-based monitoring and alerting framework that uses data in the Electronic Medical Records (EMRs) repositories of past patient cases to identify any unusual clinical actions in the EMR of a current patient. Our conjecture is that these unusual clinical actions correspond to medical errors often enough to justify their detection and alerting. Our approach works by using EMR repositories to learn statistical models that relate patient states to patient-management actions. We evaluated this approach on the EMR data for 24,658 intensive care unit (ICU) patient cases. A total of 16,500 cases were used to train statistical models for ordering medications and laboratory tests given the patient state summarizing the patient’s clinical history. The models were applied to a separate test set of 8,158 ICU patient cases and used to generate alerts. A subset of 240 alerts generated by the models were evaluated and assessed by eighteen ICU clinicians. The overall true positive rates for the alerts (TPARs) ranged from 0.44 to 0.71. The TPAR for medication order alerts specifically ranged from 0.31 to 0.61 and for laboratory order alerts from 0.44 to 0.75. These results support outlier-based alerting as a promising new approach to data-driven clinical alerting that is generated automatically based on past EMR data.

Section: Introductionmentioning

confidence: 99%

Outlier-based detection of unusual patient-management actions: An ICU study

Journal of Biomedical Informatics

Batal

Hong

et al. 2016

Self Cite

“…We have tested our approach on time series data obtained from electronic health records of approximately 4,500 post-surgical cardiac patients stored in PCP database [13][14][15]. To test the performance of our prediction model, we randomly selected 1000 patients with the Complete Blood Count(CBC) panel test 1 whose hospitalization is longer than 10 days.…”

Section: Resultsmentioning

confidence: 99%

Clinical Time Series Prediction with a Hierarchical Dynamical System

Liu

Artificial Intelligence in Medicine

2013

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Abstract. In this work we develop and test a novel hierarchical framework for modeling and learning multivariate clinical time series data. Our framework combines two modeling approaches: Linear Dynamical Systems (LDS) and Gaussian Processes (GP), and is capable to model and work with time series of varied length and with irregularly sampled observations. We test our framework on the problem of learning clinical time series data from the complete blood count panel, and show that our framework outperforms alternative time series models in terms of its predictive accuracy.

“…We test our adaptive model switching framework on a clinical MTS data obtained from EHRs of post-surgical cardiac patients [14, 26]. We take 500 patients from the database who had their Complete Blood Count (CBC) tests 1 done during their hospitalization.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection

Liu

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

2017

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Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient’s own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.