A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients

Zeng, Zhijun; Hou, Zeng-Guang; Li, Ting; Deng, Lei; Hou, Jirui; Huang, Xinran; Li, Jun; Sun, Meirou; Wang, Yunhan; Wu, Qiyu; Zheng, Wenhao; Jiang, Hua; Wang, Qi

doi:10.48550/arxiv.2202.10921

Cited by 2 publications

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“…Although Artificial Neural Networks (ANNs) are very accurate predicting tools if compared to more conventional survival models (Topol, 2019;Zeng et al, 2022;Ivanov et al, 2022), they are often seen as black boxes. ANN models are indeed very difficult to interpret and it is challenging to identify which predictors are the most relevant (May et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Two-step interpretable modeling of Intensive Care Acquired Infections

Lancia¹,

Varkila²,

Cremer³

et al. 2023

Preprint

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We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining interpretability of the models. To go beyond the black box paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that combines routinely collected low-resolution data with predictive features extracted from a convolutional neural network, that was trained on high resolution timedependent information. We then use saliency maps to analyze and explain the extra predictive power of this model. To illustrate our methodology, we focus on healthcare-associated infections in patients admitted to an intensive care unit.

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Section: Introductionmentioning

confidence: 99%

Two-step interpretable modeling of Intensive Care Acquired Infections

Lancia¹,

Varkila²,

Cremer³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, ANN methods are often used to detect anomalous patterns of continuously monitored clinical data, such as trends, fluctuation, and periodicity; these irregularities in clinical data might often be informative over the clinical deterioration of patients (Tonekaboni et al, 2018;Henry et al, 2015;Suresh et al, 2017;Brand et al, 2018). Recently success of ANN in predicting more and more accurately healthcare outcomes has given the prospection that intelligent, high-performance algorithms could analyze an increasingly wide range of detailed healthcare data in order to predict and manage patient outcomes in a way that is not humanly possible; this has consequently generated a great deal of excitement (Topol, 2019;Zeng et al, 2022;Ivanov et al, 2022;Komorowski, 2020).…”

Section: The Icuai Problemmentioning

confidence: 99%

Interpretable predictions with Convolutional Neural Networks for complex data

Lancia¹

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Deep Learning (DL) and Artificial Intelligence (AI) are nowadays one of the most used tools to analyze massive and complex data sets. Despite being very flexible and powerful, Artificial Neural Networks (ANN) are often denoted as "black-box" methods; the causal association between predictions and data is not straightforward and easy to explain. This thesis is focused on three applications with complex data of 1-D Convolutional Neural Networks (1-D CNN), a specific type of ANN. Through Explainable Artificial Intelligence (XAI) algorithms, 1-D CNN-based predictions can be made interpretable. Firstly, we considered the possibility of improving the diagnosis of malignant tumors through the classification of Raman spectra of genomic DNA. Much of the focus is dedicated to discerning different sub-cell lines of the same tumor. Next, 1-D CNN has been implemented to predict El Niño Southen Oscillation (ENSO) from Zebiack-Cane (ZC) simulated data. We tried to understand what 1-D CNN can learn about these events' physical dynamics when trying to distort the parameters ruling the ocean-atmosphere coupling. Last, a joint work with the ICU-Department at UMCU for improving the treatment of ICU patients is presented: 1-D CNN was utilized to predict nosocomial ICU-Acquired Infections (ICU-AI) dynamically. Specifically, 1-D CNN has been trained to score the risk of an ICU-AI onset only by analyzing the massive amount of information available from the ICU monitors. The actual ICU-AI prediction has been provided through Survival Analysis techniques after embedding the 1-D CNN analysis into a wider set of traditional explainable variables.

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A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients

Cited by 2 publications

References 0 publications

Two-step interpretable modeling of Intensive Care Acquired Infections

Two-step interpretable modeling of Intensive Care Acquired Infections

Interpretable predictions with Convolutional Neural Networks for complex data

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