Generalized additive neural network with flexible parametric link function: model estimation using simulated and real clinical data

Brás-Geraldes, Carlos; Papoila, Ana Luísa; Xufre, Patrícia

doi:10.1007/s00521-017-3105-6

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…A recent trend is the combination of neural networks with statistical regression models in various ways [41,42,43,44,45,46,47]. In this work, we make use of semi-structured deep distributional regression [SDDR,48].…”

Section: Semi-structured Deep Distributional Regressionmentioning

confidence: 99%

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

Fritz,

Dorigatti,

Rügamer

2021

Preprint

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During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. Several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches are often applying standard models of the respective research field. This habit, however, often comes along with certain restrictions. For instance, most statistical or epidemiological models cannot directly incorporate unstructured data sources, including relational data that may encode human mobility. In contrast, machine learning approaches may yield better predictions by exploiting these data structures, yet lack intuitive interpretability as they are often categorized as black-box models. We propose a trade-off between both research directions and present a multimodal learning approach that combines the advantages of statistical regression and machine learning models for predicting local COVID-19 cases in Germany. This novel approach enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest MSE scores throughout our observational period in our benchmark study. The results corroborate the necessity of including mobility data and showcase the flexibility and interpretability of our approach.

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Section: Semi-structured Deep Distributional Regressionmentioning

confidence: 99%

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

Fritz,

Dorigatti,

Rügamer

2021

Preprint

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“…Pairing of features was not assessed in this study to avoid cluttering the final interpretation. Bras-Geraldes et al showed GAM-NNs could be used to predict mortality in the ICU with an AUC of 0.83, using 19 features from vital signs, lab values, demographics, admission information, and comorbidities 8 .…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality

et al. 2021

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While deep neural networks (DNNs) and other machine learning models often have higher accuracy than simpler models like logistic regression (LR), they are often considered to be “black box” models and this lack of interpretability and transparency is considered a challenge for clinical adoption. In healthcare, intelligible models not only help clinicians to understand the problem and create more targeted action plans, but also help to gain the clinicians’ trust. One method of overcoming the limited interpretability of more complex models is to use Generalized Additive Models (GAMs). Standard GAMs simply model the target response as a sum of univariate models. Inspired by GAMs, the same idea can be applied to neural networks through an architecture referred to as Generalized Additive Models with Neural Networks (GAM-NNs). In this manuscript, we present the development and validation of a model applying the concept of GAM-NNs to allow for interpretability by visualizing the learned feature patterns related to risk of in-hospital mortality for patients undergoing surgery under general anesthesia. The data consists of 59,985 patients with a feature set of 46 features extracted at the end of surgery to which we added previously not included features: total anesthesia case time (1 feature); the time in minutes spent with mean arterial pressure (MAP) below 40, 45, 50, 55, 60, and 65 mmHg during surgery (6 features); and Healthcare Cost and Utilization Project (HCUP) Code Descriptions of the Primary current procedure terminology (CPT) codes (33 features) for a total of 86 features. All data were randomly split into 80% for training (n = 47,988) and 20% for testing (n = 11,997) prior to model development. Model performance was compared to a standard LR model using the same features as the GAM-NN. The data consisted of 59,985 surgical records, and the occurrence of in-hospital mortality was 0.81% in the training set and 0.72% in the testing set. The GAM-NN model with HCUP features had the highest area under the curve (AUC) 0.921 (0.895–0.95). Overall, both GAM-NN models had higher AUCs than LR models, however, had lower average precisions. The LR model without HCUP features had the highest average precision 0.217 (0.136–0.31). To assess the interpretability of the GAM-NNs, we then visualized the learned contributions of the GAM-NNs and compared against the learned contributions of the LRs for the models with HCUP features. Overall, we were able to demonstrate that our proposed generalized additive neural network (GAM-NN) architecture is able to (1) leverage a neural network’s ability to learn nonlinear patterns in the data, which is more clinically intuitive, (2) be interpreted easily, making it more clinically useful, and (3) maintain model performance as compared to previously published DNNs.

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Odds ratio function estimation using a generalized additive neural network

Brás-Geraldes

Papoila

Xufre

2019

Neural Comput & Applic

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Generalized additive neural network with flexible parametric link function: model estimation using simulated and real clinical data

Cited by 5 publications

References 18 publications

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality

Odds ratio function estimation using a generalized additive neural network

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