Personalized Modeling for Prediction with Decision-Path Models

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme; Oliveira, Alexandre César Muniz de; Cooper, Gregory F.

doi:10.1371/journal.pone.0131022

Cited by 9 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In clinical prediction research, decision tree is frequently designed to build binary classifiers, such as cancer prediction/prognosis 41 . As a method used in machine learning, it is nonparametric which makes fewer data assumptions and it can accommodate collinear independent variables 42 . It is also less sensitive to outliers and more robust to high-dimensional data, which possess many independent variables relative to outcomes 43 .…”

Section: Discussionmentioning

confidence: 99%

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Sun²,

Gao

et al. 2021

Sci Rep

View full text Add to dashboard Cite

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Sun²,

Gao

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Model-specific -Decision trees (depends on depth and number of terminal nodes; Hastie, Tibshirani, & Friedman, 2009;Stiglic, Kocbek, Pernek, & Kokol, 2012), -Linear and logistic regression models (Harrell Jr, 2015), -Generalized linear models (GLM) and generalized additive models (GAM; Hastie et al, 2009), -Naive Bayes classifier (Kononenko, 1993), -GNNExplainer (Ying et al, 2019) -Set of rules (for specific individual; Visweswaran, Ferreira, Ribeiro, Oliveira, & Cooper, 2015), -Decision trees (by tree -decomposition; Visweswaran et al, 2015) Dandapat, 2019). Local and model-agnostic interpretability can be used in interpretability of complex models, such as deep learning models.…”

Section: Global Localmentioning

confidence: 99%

Interpretability of machine learning‐based prediction models in healthcare

Štiglic

Kocbek

Fijačko

et al. 2020

WIREs Data Min & Knowl

289

148

View full text Add to dashboard Cite

There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable ML models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any ML model. Here, we give an overview of interpretability approaches using structured data and provide examples of practical interpretability of ML in different areas of healthcare, including prediction of healthrelated outcomes, optimizing treatments, or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable ML and highlight the importance of developing algorithmic solutions that can enable ML driven decision making in high-stakes healthcare problems.

show abstract

“…In clinical prediction research, decision tree is frequently designed to build binary classifiers, such as cancer prediction/prognosis [40]. As a method used in machine learning, it is nonparametric, makes fewer data assumptions and it can accommodate collinear independent variables [41]. It is also less sensitive to outliers and more robust to high-dimensional data, which possess many independent variables relative to outcomes [42].…”

Section: Discussionmentioning

confidence: 99%

Risk Factors Analysis of COVID-19 Patients with ARDS and Prediction Based on Machine Learning

Xu¹,

Sun²,

Gao

et al. 2020

Preprint

View full text Add to dashboard Cite

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the two groups were elaborately compared and both traditional machine learning algorithms and deep learning-based methods were used to build the prediction models. Results indicated the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI>25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, leukocyte counting, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, sensitivity, and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.

show abstract

Personalized Modeling for Prediction with Decision-Path Models

Cited by 9 publications

References 14 publications

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Interpretability of machine learning‐based prediction models in healthcare

Risk Factors Analysis of COVID-19 Patients with ARDS and Prediction Based on Machine Learning

Contact Info

Product

Resources

About