AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus

Molecular Aspects in the Development of Type 2 Diabetes and Possible Preventive and Complementary Therapies

The incidence of diabetes, including type 2 diabetes (T2DM), is increasing sharply worldwide. To reverse this, more effective approaches in prevention and treatment are needed. In our review, we sought to summarize normal insulin action and the pathways that primarily influence the development of T2DM. Normal insulin action involves mitogenic and metabolic pathways, as both are important in normal metabolic processes, regeneration, etc. However, through excess energy, both can be hyperactive or attenuated/inactive leading to disturbances in the cellular and systemic regulation with the consequence of cellular stress and systemic inflammation. In this review, we detailed the beneficial molecular changes caused by some important components of nutrition and by exercise, which act in the same molecular targets as the developed drugs, and can revert the damaged pathways. Moreover, these induce entire networks of regulatory mechanisms and proteins to restore unbalanced homeostasis, proving their effectiveness as preventive and complementary therapies. These are the main steps for success in prevention and treatment of developed diseases to rid the body of excess energy, both from stored fats and from overnutrition, while facilitating fat burning with adequate, regular exercise in healthy people, and together with necessary drug treatment as required in patients with insulin resistance and T2DM.

Artificial intelligence for diabetes care: current and future prospects

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Interpretable prediction model for assessing diabetes complication risks in Chinese sufferers

AIMS

With growing concerns over complications in diabetes sufferers, this study sought to develop an interpretable machine learning model to offer enhanced diagnostic and treatment recommendations.

METHODS

We assessed coronary heart disease, diabetic nephropathy, diabetic retinopathy, and fatty liver disease using logistic regression, decision tree, random forest, and CatBoost algorithms. The SHAP algorithm was employed to elucidate the model's predictions, offering a more in-depth understanding of influential features.

RESULTS

The CatBoost model notably outperformed other algorithms in AUC, achieving an average AUC of 90.47 % for the four complications. Through SHAP analysis and visualization, we provided clear and actionable insights into risk factors, enabling better complication risk assessment.

CONCLUSIONS

We introduced an innovative, interpretable complication risk model for people with diabetes. This not only offers a potent tool for healthcare professionals but also empowers sufferers with clearer self-assessment capabilities, encouraging earlier preventive actions. Further studies will underscore the model's clinical applicability.

Predicting coronary heart disease in Chinese diabetics using machine learning

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

Machine learning in precision diabetes care and cardiovascular risk prediction

Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates

BACKGROUND

Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown.

AIMS

The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant.

METHODS

A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation.

RESULTS

The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction.

CONCLUSIONS

A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.

Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms

OBJECTIVE

To establish a prediction model of coronary heart disease (CHD) in elderly patients with diabetes mellitus (DM) based on machine learning (ML) algorithms.

METHODS

Based on the Medical Big Data Research Centre of Chinese PLA General Hospital in Beijing, China, we identified a cohort of elderly inpatients (≥ 60 years), including 10,533 patients with DM complicated with CHD and 12,634 patients with DM without CHD, from January 2008 to December 2017. We collected demographic characteristics and clinical data. After selecting the important features, we established five ML models, including extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), adaptive boosting (Adaboost) and logistic regression (LR). We compared the receiver operating characteristic curves, area under the curve (AUC) and other relevant parameters of different models and determined the optimal classification model. The model was then applied to 7447 elderly patients with DM admitted from January 2018 to December 2019 to further validate the performance of the model.

RESULTS

Fifteen features were selected and included in the ML model. The classification precision in the test set of the XGBoost, RF, DT, Adaboost and LR models was 0.778, 0.789, 0.753, 0.750 and 0.689, respectively; and the AUCs of the subjects were 0.851, 0.845, 0.823, 0.833 and 0.731, respectively. Applying the XGBoost model with optimal performance to a newly recruited dataset for validation, the diagnostic sensitivity, specificity, precision, and AUC were 0.792, 0.808, 0.748 and 0.880, respectively.

CONCLUSIONS

The XGBoost model established in the present study had certain predictive value for elderly patients with DM complicated with CHD.

Longitudinal clinical trajectory analysis of individuals before and after diagnosis of Type 2 Diabetes Mellitus (T2DM) indicates that vascular problems start early

INTRODUCTION

Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi-morbidity/treatment complexity. Some headway has been made to identify genetic and non-genetic risk factors for T2DM. However, longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi-morbidity.

METHODS

This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre- and post-T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n = 237 vs matched non-T2DM controls n = 44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year.

RESULTS

For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis, a greater indication of ischaemic heart disease proportions was observed. Post-T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure.

CONCLUSION

These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks

The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation.