scholarly journals A New Method for Predicting Patient Survivorship Using Efficient Bayesian Network Learning

2014 ◽  
Vol 13 ◽  
pp. CIN.S13053 ◽  
Author(s):  
Xia Jiang ◽  
Diyang Xue ◽  
Adam Brufsky ◽  
Seema Khan ◽  
Richard Neapolitan
Keyword(s):  
Decision Support ◽  
Bayesian Network ◽  
Molecular Taxonomy ◽  
High Dimensional Data ◽  
Prediction Method ◽  
Clinical Decision ◽  
Sensitivity Analyses ◽  
High Dimensional ◽  
Additional Information ◽  
Central Hypothesis

The purpose of this investigation is to develop and evaluate a new Bayesian network (BN)-based patient survivorship prediction method. The central hypothesis is that the method predicts patient survivorship well, while having the capability to handle high-dimensional data and be incorporated into a clinical decision support system (CDSS). We have developed EBMC_Survivorship (EBMC_S), which predicts survivorship for each year individually. EBMC_S is based on the EBMC BN algorithm, which has been shown to handle high-dimensional data. BNs have excellent architecture for decision support systems. In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis.

scholarly journals Multivariate time series prediction of high dimensional data based on deep reinforcement learning

2021 ◽  
Vol 256 ◽  
pp. 02038
Author(s):  
Xin Ji ◽  
Haifeng Zhang ◽  
Jianfang Li ◽  
Xiaolong Zhao ◽  
Shouchao Li ◽  
...  
Keyword(s):  
Time Series ◽  
Reinforcement Learning ◽  
Phase Space ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
High Dimensional Data ◽  
Prediction Method ◽  
Conditional Entropy ◽  
High Dimensional ◽  
Low Dimensional

In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

scholarly journals Equation Chapter 1 Section 1 Differentially Private High-Dimensional Binary Data Publication via Adaptive Bayesian Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Sun Lan ◽  
Jinxin Hong ◽  
Junya Chen ◽  
Jianping Cai ◽  
Yilei Wang
Keyword(s):  
Bayesian Network ◽  
Time Complexity ◽  
Binary Data ◽  
Differential Privacy ◽  
Functional Performance ◽  
High Dimensional Data ◽  
Real Life ◽  
Synthetic Dataset ◽  
High Dimensional

When using differential privacy to publish high-dimensional data, the huge dimensionality leads to greater noise. Especially for high-dimensional binary data, it is easy to be covered by excessive noise. Most existing methods cannot address real high-dimensional data problems appropriately because they suffer from high time complexity. Therefore, in response to the problems above, we propose the differential privacy adaptive Bayesian network algorithm PrivABN to publish high-dimensional binary data. This algorithm uses a new greedy algorithm to accelerate the construction of Bayesian networks, which reduces the time complexity of the GreedyBayes algorithm from O n k C m + 1 k + 2 to O n m 4 . In addition, it uses an adaptive algorithm to adjust the structure and uses a differential privacy Exponential mechanism to preserve the privacy, so as to generate a high-quality protected Bayesian network. Moreover, we use the Bayesian network to calculate the conditional distribution with noise and generate a synthetic dataset for publication. This synthetic dataset satisfies ε -differential privacy. Lastly, we carry out experiments against three real-life high-dimensional binary datasets to evaluate the functional performance.

scholarly journals Clinical Decision Support for In-Hospital AKI

2017 ◽  
Vol 29 (2) ◽  
pp. 654-660 ◽  
Author(s):  
Mohammed Al-Jaghbeer ◽  
Dilhari Dealmeida ◽  
Andrew Bilderback ◽  
Richard Ambrosino ◽  
John A. Kellum
Keyword(s):  
Decision Support ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Clinical Decision Support ◽  
Odds Ratio ◽  
Clinical Decision ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Sensitivity Analyses ◽  
Mortality And Morbidity

AKI carries a significant mortality and morbidity risk. Use of a clinical decision support system (CDSS) might improve outcomes. We conducted a multicenter, sequential period analysis of 528,108 patients without ESRD before admission, from October of 2012 to September of 2015, to determine whether use of a CDSS reduces hospital length of stay and in-hospital mortality for patients with AKI. We compared patients treated 12 months before (181,696) and 24 months after (346,412) implementation of the CDSS. Coprimary outcomes were hospital mortality and length of stay adjusted by demographics and comorbidities. AKI was diagnosed in 64,512 patients (12.2%). Crude mortality rate fell from 10.2% before to 9.4% after CDSS implementation (odds ratio, 0.91; 95% confidence interval [95% CI], 0.86 to 0.96; P=0.001) for patients with AKI but did not change in patients without AKI (from 1.5% to 1.4%). Mean hospital duration decreased from 9.3 to 9.0 days (P<0.001) for patients with AKI, with no change for patients without AKI. In multivariate mixed-effects models, the adjusted odds ratio (95% CI) was 0.76 (0.70 to 0.83) for mortality and 0.66 (0.61 to 0.72) for dialysis (P<0.001). Change in adjusted hospital length of stay was also significant (incidence rate ratio, 0.91; 95% CI, 0.89 to 0.92), decreasing from 7.2 to 6.0 days for patients with AKI. Results were robust to sensitivity analyses and were sustained for the duration of follow-up. Hence, implementation of a CDSS for AKI resulted in a small but sustained decrease in hospital mortality, dialysis use, and length of stay.

scholarly journals Robust parameter extraction for decision support using multimodal intensive care data

2008 ◽  
Vol 367 (1887) ◽  
pp. 411-429 ◽  
Author(s):  
G.D Clifford ◽  
W.J Long ◽  
G.B Moody ◽  
P Szolovits
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Information Flow ◽  
Clinical Decision ◽  
Parameter Extraction ◽  
High Dimensional ◽  
Digital Information ◽  
Prior Probabilities ◽  
Recent Developments ◽  
Robust Parameter

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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