Stratifying individuals into non-alcoholic fatty liver disease risk levels using time series machine learning models

Ben‐Assuli, Ofir; Jacobi, Arie; Goldman, Orit; Shenhar‐Tsarfaty, Shani; Rogowski, Ori; Zeltser, David; Shapira, Itzhak; Berliner, Shlomo; Zelber‐Sagi, Shira

doi:10.1016/j.jbi.2022.103986

Cited by 8 publications

(3 citation statements)

References 89 publications

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“…[120] Model-derived subpopulations were commonly used in downstream prediction tasks. [39,68,121,122,125,131] For example, a SVM was used to identify sepsis using features of subpopulations with distinct dysfunction patterns discovered from a self-organizing map. [128] Only 1 article utilized a deep learning approach, specifically a deep autoencoder to discover subtypes of depression.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast to the previously discussed ML approaches, unsupervised learning is used for phenotype discovery, including identification of subphenotypes, [39,74,[120][121][122][123][124][125][126][127][128] co-occurring conditions, [69,129] and disease progression patterns. [68,[130][131][132][133][134] Among the 19 articles utilizing unsupervised learning, Latent Dirichlet Allocation (LDA) [69,124,125,127,133] and K-means were the most frequently used methods. [120,121,123,125] LDA was applied to identify the co-occurrence of allergic rhinitis and osteoporosis among patients with kidney disease [69] as well as to capture trends in mental health and end of life care among dementia patients.…”

Section: Unsupervised Learningmentioning

confidence: 99%

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Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Yang

Varghese²,

Stephenson

et al. 2022

Preprint

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ObjectiveAccurate and rapid methods for phenotyping are a prerequisite to realizing the potential of electronic health records (EHRs) data for clinical and translational research. This study reviews the literature on machine learning (ML) approaches for phenotyping with respect to the phenotypes considered, the data sources and methods used, and the contributions within the wider context of EHR-based research.Materials and MethodsWe searched for relevant articles in PubMed and Web of Science published between January 1, 2018 and April 14, 2022. After screening, we collected data on 52 variables across 106 selected articles.ResultsML-based methods were developed for 156 unique phenotypes, primarily using EHR data from a single institution or health system. 72 of 106 articles leveraged unstructured data in clinical notes. In terms of methodology, supervised learning is the most prevalent ML paradigm (n = 64, 60.4%), with half of the articles employing deep learning. Semi-supervised and weakly-supervised approaches were applied to reduce the burden of obtaining gold-standard labeled data (n = 21, 19.8%), while unsupervised learning was used for phenotype discovery (n = 20, 18.9%). Federated learning has been applied to develop algorithms across multiple institutions while preserving data privacy (n = 2, 1.9%).DiscussionWhile the use of ML for phenotyping is growing, most articles applied traditional supervised ML to characterize the presence of common, chronic conditions.ConclusionContinued research in ML-based methods is warranted, with particular attention to the development of advanced methods for complex phenotypes and standards for reporting and evaluating phenotyping algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Yang

Varghese²,

Stephenson

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on our results, we chose the method using all features, but still RF had the best result among the four classifiers, as also observed by Zou et al 60 Therefore, our observations provide valuable insight into the potential application of the AUC as a predictive measure for T2D and highlight the need. These ML methods have also been recently applied by Ben‐Assuli et al 61 for faster diagnosis and treatment of NAFLD. Our results provide a significant resource for further studies to determine the causal relationship and the progression of T2D; therefore, the prospect of using personalized medicine is a promise.…”

Section: Discussionmentioning

confidence: 99%

High‐fat diet and oral infection induced type 2 diabetes and obesity development under different genetic backgrounds

Lone

Nun

Ghnaim

et al. 2023

Anim Models and Exp Med

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Background Type 2 diabetes (T2D) is an adult‐onset and obese form of diabetes caused by an interplay between genetic, epigenetic, and environmental components. Here, we have assessed a cohort of 11 genetically different collaborative cross (CC) mouse lines comprised of both sexes for T2D and obesity developments in response to oral infection and high‐fat diet (HFD) challenges. Methods Mice were fed with either the HFD or the standard chow diet (control group) for 12 weeks starting at the age of 8 weeks. At week 5 of the experiment, half of the mice of each diet group were infected with Porphyromonas gingivalis and Fusobacterium nucleatum bacteria strains. Throughout the 12‐week experimental period, body weight (BW) was recorded biweekly, and intraperitoneal glucose tolerance tests were performed at weeks 6 and 12 of the experiment to evaluate the glucose tolerance status of mice. Results Statistical analysis has shown the significance of phenotypic variations between the CC lines, which have different genetic backgrounds and sex effects in different experimental groups. The heritability of the studied phenotypes was estimated and ranged between 0.45 and 0.85. We applied machine learning methods to make an early call for T2D and its prognosis. The results showed that classification with random forest could reach the highest accuracy classification (ACC = 0.91) when all the attributes were used. Conclusion Using sex, diet, infection status, initial BW, and area under the curve (AUC) at week 6, we could classify the final phenotypes/outcomes at the end stage of the experiment (at 12 weeks).

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Intestinal cancer development in response to oral infection with high-fat diet-induced Type 2 diabetes (T2D) in collaborative cross mice under different host genetic background effects

et al. 2023

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Stratifying individuals into non-alcoholic fatty liver disease risk levels using time series machine learning models

Cited by 8 publications

References 89 publications

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

High‐fat diet and oral infection induced type 2 diabetes and obesity development under different genetic backgrounds

Intestinal cancer development in response to oral infection with high-fat diet-induced Type 2 diabetes (T2D) in collaborative cross mice under different host genetic background effects

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