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

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 8158 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.

Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods.

BACKGROUND: Radiologic imaging of trauma patients often uncovers findings that are unrelated to the trauma. These are termed as incidental findings and identifying them in radiology examination reports is necessary for appropriate follow-up. We developed and evaluated an automated pipeline to identify incidental findings at sentence and section levels in radiology reports of trauma patients. METHODS: We created an annotated dataset of 4,181 reports and investigated automated feature representations including traditional word and clinical concept (such as SNOMED CT) representations, as well as word and concept embeddings. We evaluated these representations by using them with traditional classifiers such as logistic regression and with deep learning methods such as convolutional neural networks (CNNs). RESULTS: The best performance was observed using word embeddings with CNNs with F1 scores of 0.66 and 0.52 at section and sentence levels respectively. The F1 score was statistically significantly higher for sections compared to sentences (Wilcoxon; Z < 0.001, p < 0.05). Compared to using words alone, the addition of SNOMED CT concepts did not improve performance. At the sentence level, the F1 score improved significantly from 0.46 to 0.52 when using pre-trained embeddings (Wilcoxon; Z < 0.001, p < 0.05). CONCLUSION: The results show that the best performance was achieved by using embeddings with CNNs at both sentence and section levels. This provides evidence that such a pipeline is capable of accurately identifying incidental findings in radiology reports in an automated manner.

Multivariate Conditional Outlier Detection: Identifying Unusual Input-Output Associations in Data.

We study multivariate conditional outlier detection, a special type of the conditional outlier detection problem, where data instances consist of continuous input (context) and binary output (responses) vectors. We present a novel outlier detection framework that identifies abnormal input-output associations in data using a decomposable conditional probabilistic model. Since the components of this model can vary in their quality, we combine them with the help of weights reflecting their reliability in assessment of outliers. We propose two ways of calculating the component weights: global that relies on all data and local that relies only on the instances similar to the target instance. Experimental results on data from various domains demonstrate the ability of our framework to successfully identify multivariate conditional outliers.

Multivariate Conditional Outlier Detection and Its Clinical Application.

This paper overviews and discusses our recent work on a multivariate conditional outlier detection framework for clinical applications.

A Generalized Mixture Framework for Multi-label Classification.

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd |X) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.

A Mixtures-of-Trees Framework for Multi-Label Classification.

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods.

An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification.

This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent and one cannot decompose the problem into a set of independent classification problems. To address the problem and properly represent label dependencies we propose and study a pairwise conditional random Field (CRF) model. We develop a new approach for learning the structure and parameters of the CRF from data. The approach maximizes the pseudo likelihood of observed labels and relies on the fast proximal gradient descend for learning the structure and limited memory BFGS for learning the parameters of the model. Empirical results on several datasets show that our approach outperforms several multi-label classification baselines, including recently published state-of-the-art methods.