Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

Novitski, Pavel; Cohen, Cheli Melzer; Karasik, Avraham; Hodik, Gabriel; Moskovitch, Robert

doi:10.1016/j.jbi.2022.104198

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…For T2D prediction, a patient-level sequential personalized prediction approach is proposed to quantify the relationship of the multiple T2D sequence records with prescriptions and efficacies [ 10 ]. A study introduces the temporal abstraction to predict mortality with heterogeneous EHR data, which is employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals and discovered the frequent time intervals related patterns (TIRPs) [ 19 ]. For temporal clinical case, a study employs deep learning networks of convolutional neural network (CNN) with LSTM combination to automatically detect the abnormality, which diabetes is diagnosed by the analysis of heart rate variability (HRV) signals obtained from ECG signals [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

An early warning model of type 2 diabetes risk based on POI visit history and food access management

Xie¹,

Li²,

Wang³

et al. 2023

PLoS ONE

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Type 2 diabetes (T2D) is a long-term, highly prevalent disease that provides extensive data support in spatial-temporal user case data mining studies. In this paper, we present a novel T2D food access early risk warning model that aims to emphasize health management awareness among susceptible populations. This model incorporates the representation of T2D-related food categories with graph convolutional networks (GCN), enabling the diet risk visualization from the geotagged Twitter visit records on a map. A long short-term memory (LSTM) module is used to enhance the performance of the case temporal feature extraction and location approximate predictive approach. Through an analysis of the resulting data set, we highlight the food effect category has on T2D early risk visualization and user food access management on the map. Moreover, our proposed method can provide suggestions to T2D susceptible patients on diet management.

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Section: Related Workmentioning

confidence: 99%

An early warning model of type 2 diabetes risk based on POI visit history and food access management

Xie¹,

Li²,

Wang³

et al. 2023

PLoS ONE

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show abstract

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review

Mesquita,

Bernardino,

Henriques

et al. 2023

J Diabetes Metab Disord

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Purpose Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)—a chronic condition that progressively impairs kidney function. In this context, it is important to evaluate if Machine learning models can exploit the inherent temporal factor in clinical data to predict the risk of developing DN faster and more accurately than current clinical models. Methods Three different databases were used for this literature review: Scopus, Web of Science, and PubMed. Only articles written in English and published between January 2015 and December 2022 were included. Results We included 11 studies, from which we discuss a number of algorithms capable of extracting knowledge from clinical data, incorporating dynamic aspects in patient assessment, and exploring their evolution over time. We also present a comparison of the different approaches, their performance, advantages, disadvantages, interpretation, and the value that the time factor can bring to a more successful prediction of diabetic nephropathy. Conclusion Our analysis showed that some studies ignored the temporal factor, while others partially exploited it. Greater use of the temporal aspect inherent in Electronic Health Records (EHR) data, together with the integration of omics data, could lead to the development of more reliable and powerful predictive models.

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Medical resource allocation planning by integrating machine learning and optimization models

Mizan

Taghipour

2022

Artificial Intelligence in Medicine

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Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

Cited by 7 publications

References 43 publications

An early warning model of type 2 diabetes risk based on POI visit history and food access management

An early warning model of type 2 diabetes risk based on POI visit history and food access management

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review

Medical resource allocation planning by integrating machine learning and optimization models

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