ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

Abstract

To improve the performance of Intensive Care Units (ICUs), the field of biostatistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight on the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multi-scale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Our results show our model attains state of the art performance while remaining interpretable. Supporting code can be found at https://github.com/williamcaicedo/ISeeU.

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

“…The same concept had been used in [19] and [59], where only heart-rate signals were used and then aggregated during the first hour for mortality prediction. In [60], the authors built a deep learning model that used only on nursing notes to predict mortality.…”

“…They achieved an AUC score of 85.5%. Caicedo-Torres et al proposed a deep learning model called ConvNet for mortality prediction [64]. Their system achieved 87.3% in terms of AUC, and they added further steps by using ConvNet in handling both static and dynamic data.…”

Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review

“…Davoodi et al [29] and Hoogendoorn et al [24] predicted after 24 h and within a median of 72 h, respectively. Studies by Tang et al [33], Caicedo-Torres et al [36], Sha et al [38], and Zhang et al [41] predicted in-hospital mortality irrespective of the admission or discharge time.…”

“…In traditional ML techniques, Random Forest, Decision Tree, and Logistic Regression were the most commonly used algorithms. However, recent studies by Caicedo-Torres et al [36], Du et al [25], and Zahid et al [30] have used DL methods for mortality prediction with a promising accuracy ranging from 0.86-0.87 as reported in the Supplementary Table S1. Traditional ML models can be easily interpretable when compared to DL models that have many levels of features and hidden layers to predict outcomes.…”

Um Modelo para Predição Individualizada da Deterioração da Saúde de Pacientes no âmbito da Internet das Coisas

“…Na abordagem populacionalé selecionado um conjunto de variáveis de todos os pacientes do RES para ser utilizado como entrada do modelo de ML com diferentes objetivos como a predição de mortalidade [Thorsen-Meyer et al 2020, Caicedo-Torres and Gutierrez 2019, Todd et al 2019. Em outros casos são utilizados dados de grupos de pacientes com características comuns como comorbidades [dos Santos et al 2020, Ershoff et al 2020, Lin et al 2019, gênero [Qi et al 2018, Mansoor et al 2017] e idade [Brajer et al 2020, Cooper et al 2018, van Loon et al 2017.…”