16,552 research outputs found
Utilization of big data to improve management of the emergency departments. Results of a systematic review
Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes.
Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field.
Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time.
Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
ART Neural Networks: Distributed Coding and ARTMAP Applications
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Dynamic Prediction for Alternating Recurrent Events Using a Semiparametric Joint Frailty Model
Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of a transition from one event type to the other within a pre-specified time period. To circumvent numerical integration in calculating the predictive probability, we obtain the approximate transition probability by a Taylor expansion. Simulation results demonstrate that our tool provides better prediction performance in discrimination, as measured by the area under the ROC curve (AUC) and sensitivity, than prediction approaches that rely on standard binary regression models. Also, simulation shows that prediction results from approximate transition probability are as close as results from the exact predictive probability. We illustrate predictions in analyses of relapses of chronic bronchitis exacerbation from a pharmaceutical trial and hospital readmissions in patients with diabetes from Medicaid claims data.
The final part of this dissertation (Chapter 6) compares predictive performance between logistic regression and random forests for 30-day readmission using longitudinal claims data. Several studies have compared these and other prediction models using longitudinal electronic health records or claims data. Because most of them applied logistic regression to the longitudinal observations, ignoring the lack of independence within subjects, or claims data consisting of independent observations, a correct comparison of the models under longitudinal data remains obscure. Moreover, those studies did not compare the out-of-sample performance. We address these issues and compare the prediction performance of the models using longitudinal claims data. We implement simulations by randomly choosing a record from each patient\u27s multiple records in the training set, fitting the two models, applying the models to the training, test, and external sets, and obtaining AUC and sensitivity for each. We observe that although random forests generally gives better predictions on the training set, logistic regression performs better on test and external sets. In an empirical study, we apply the prediction methods to Medicaid claims data covering inpatient admissions of patients with heart failure
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