Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Yousefi, Leila; Swift, Stephen; Arzoky, Mahir; Saachi, Lucia; Chiovato, Luca; Tucker, Allan

doi:10.1111/coin.12313

Cited by 8 publications

(16 citation statements)

References 41 publications

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“…Then the inferred latent variable probabilities were employed to predict a target complication earlier than the actual occurrence time (in the right-hand side). This figure also revealed that [5] The proposed methodology of this research, in other works [26], are combined with pattern mining approaches to validate the target hidden variables and enhance the understanding of the sub-types of the disease based upon the developing disease complications.…”

Section: Discussionmentioning

confidence: 74%

“…Whilst this ratio for HYP is ({1:5}. [1] It is possible to show patterns of complications for each single visit with respect to any combination of complication co-occurrences, chosen from C as demonstrated in [26]. [2] For more information see Supplementary Material (Pre-processing and Data Structure).…”

Section: Data Sourcementioning

confidence: 99%

“…In our previous work, an intuitive stepwise method to learn the latent effects was developed based upon the IC* algorithm, while using a Pair-Sampling re-balancing method [23]. In [24][25][26], patients were clustered into different sub-groups. In each sub-group, they shared a similar profile of observed risk factors, without taking account of the cluster decision making process.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

Yousefi

Al-Luhaybi

Sacchi

et al. 2020

Preprint

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Background: Type 2 Diabetes is a chronic disease with an onset that is commonly associated with multiple life-threatening co morbidities (complications). Early prediction of diabetic complications while discovering the behaviour of associated aggressive risk factors can reduce the patients’ suffering time. Therefore, models of the time series diabetic data (which are often imbalanced, incomplete and involve complex interactions) are needed to better manage diabetic complications. Aims: The aim of this work is to both deals with imbalanced clinical data using a bootstrapping approach, whilst determining the precise position of latent variables within probabilistic networks generated from the observations. The main motivation behind this paper is to stratify patient groups by means of latent variables to discover how complications in diabetes interact. Methods: We propose a time series bootstrapping method for building Dynamic Bayesian Networks that includes hidden / latent variables, applied to a case for predicting T2DM complications. A combination of the IC* algorithm on time series bootstrapped data is utilized to identify the latent variables within Bayesian model. Then, an exploration of inference methods assessed the influences of these latent variables. Results: Our promising findings show how this targeted use of latent variables improves prediction accuracy, specificity, and sensitivity over standard approaches as well as aiding the understanding of relationships between these latent variable sand disease complications / risk factors. The contribution of this paper compared to the previous papers in which time series bootstrapping is used for re-balancing the data and providing confidence in the prediction results. Conclusion: Our results showed that our re-balancing approach by the use of Time Series bootstrapping method for an unequal number of time series visits demonstrated an improvement in the prediction performance. Additionally, the most highlighted contribution of this paper gained insight by interpreting the latent states (looking at the associated distributions of complications), which led to a better understanding of risk factors and patient-specific interventions: here the fact that the latent variable demonstrated that a patient falls into a sub-group that is hypertensive but not suffering from retinopathy.

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Section: Discussionmentioning

confidence: 74%

Section: Data Sourcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

Yousefi

Al-Luhaybi

Sacchi

et al. 2020

Preprint

Self Cite

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“…The proposed hybrid methodology to find explainable subgroup of patients by personalising diabetic patients in precision medicine. This figure is an abstract methodology explained in Figures 1-4 in the previous work in [83].…”

Section: The Suggested Methodologymentioning

confidence: 99%

“…Among these, studies on explaining unknown risk factors and identifying temporal phenotypes by using hybrid methods (including descriptive and predictive) are rare to find in literature. It represented the reason of the earlier research conducted by the author in [32,33,[83][84][85]. The current work of this chapter's author has attempted to address these issues in the previous research in [32,33,84], after describing the case study data as a starting point, the suggested methodology is explored as a framework for modelling real time-series clinical data.…”

Section: The Suggested Methodologymentioning

confidence: 99%

Predicting Type 2 Diabetes Complications and Personalising Patient Using Artificial Intelligence Methodology

Yousefi¹,

Tucker²

2021

Type 2 Diabetes - From Pathophysiology to Cyber Systems

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The prediction of the onset of different complications of disease, in general, is challenging due to the existence of unmeasured risk factors, imbalanced data, time-varying data due to dynamics, and various interventions to the disease over time. Scholars share a common argument that many Artificial Intelligence techniques that successfully model disease are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners’ and patients’ perspective. There is a need for appropriate Artificial Intelligence techniques to build predictive models that not only capture unmeasured effects to improve prediction, but are also transparent in how they model data so that knowledge about disease processes can be extracted and trust in the model can be maintained by clinicians. The proposed strategy builds probabilistic graphical models for prediction with the inclusion of informative hidden variables. These are added in a stepwise manner to improve predictive performance whilst maintaining as simple a model as possible, which is regarded as crucial for the interpretation of the prediction results. This chapter explores this key issue with a specific focus on diabetes data. According to the literature on disease modelling, especially on major diseases such as diabetes, a patient’s mortality often occurs due to the associated complications caused by the disease over time and not the disease itself. This is often patient-specific and will depend on what type of cohort a patient belongs to. Another main focus of this study is patient personalisation via precision medicine by discovering meaningful subgroups of patients which are characterised as phenotypes. These phenotypes are explained further using Bayesian network analysis methods and temporal association rules. Overall, this chapter discussed the earlier research of the chapter’s author. It explores Artificial Intelligence (IDA) techniques for modelling the progression of disease whilst simultaneously stratifying patients and doing so in a transparent manner as possible. To this end, it reviews the current literature on some of the most common Artificial Intelligent (AI) methodologies, including probabilistic modelling, association rule mining, phenotype discovery and latent variable discovery by using diabetes as a case study.

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Predicting Urban Heat Island severity on the census-tract level using Bayesian networks

Assaf

Huang

Assaad

2023

Sustainable Cities and Society

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Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Cited by 8 publications

References 41 publications

Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

Predicting Type 2 Diabetes Complications and Personalising Patient Using Artificial Intelligence Methodology

Predicting Urban Heat Island severity on the census-tract level using Bayesian networks

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