Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling

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

doi:10.1109/bibm.2018.8621484

Cited by 7 publications

(12 citation statements)

References 19 publications

(21 reference statements)

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“…This section summarises the clinical implications and shows how the obtained experimental findings in the previous works [83,84,97] and their significance have led to developing explanatory AI models. For example Table 3 illustrated the promising results obtained by the proposed Stepwise approach discussed in [33,84].…”

Section: Experimental Results and Conclusionmentioning

confidence: 91%

“…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%

“…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. In the recent work conducted in [83,85], the identification of informative hidden factors is investigated followed by methods to cluster patients into meaningful subgroups along with the identification of a latent temporal phenotype and the characterisation of these groups using temporal association rules (as illustrated in Figure 2).…”

Section: The Suggested Methodologymentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Experimental Results and Conclusionmentioning

confidence: 91%

Section: The Suggested Methodologymentioning

confidence: 99%

Section: The Suggested Methodologymentioning

confidence: 99%

See 1 more Smart Citation

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

Yousefi¹,

Tucker²

2021

Type 2 Diabetes - From Pathophysiology to Cyber Systems

Self Cite

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show abstract

“…We developed methods to infer the location of hidden variables within these models in order to improve prediction 2 . The behavior of these hidden variables over the course of the disease process can be thought of as a “temporal phenotype” for an individual patient, 3 which is considered as a “latent phenotype.”…”

Section: Introductionmentioning

confidence: 99%

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

Yousefi

Swift

Arzoky

et al. 2020

Computational Intelligence

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It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases 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. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data.

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“…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%

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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Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling

Cited by 7 publications

References 19 publications

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

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

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

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

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