Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks

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.

Model for Predicting the Effect of Sibutramine Therapy in Obesity

Background: The development of models predicting response to weight loss therapy using sibutramine is found in only a few cases. The objective of the work is to develop a data-driven method of personalized recommendation for obesity treatment that would predict the response to sibutramine based on the current set of patient parameters. Methods: The decision system is built on the XGBoost classification algorithm along with recursive feature selection and Shapley data valuation. Using the results of clinical trials, it was trained to estimate the probability of overcoming a weight loss threshold. The model was evaluated by the accuracy metric using the Leave-One-Out cross-validation. Results: The model for predicting response to sibutramine treatment over 3 months has an accuracy of 71%. The model for predicting outcomes at the sixth month visit based on results at 3 months has an accuracy of 80%. Conclusions: Although our developed prediction model may not exhibit high precision compared to certain benchmarks, it significantly outperforms random chance or models relying only on BMI parameters. Our model used the available range of laboratory tests, which makes it possible to use this model for routine clinical use and help doctors decide whether to prescribe sibutramine.

Comparing the accuracy of four machine learning models in predicting type 2 diabetes onset within the Chinese population: a retrospective study

OBJECTIVE

To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese population by retrospectively analyzing annual health checkup records.

METHODS

We included 46,247 patients (32,372 and 13,875 in training and validation sets, respectively) from a national health checkup center database. Univariate and multivariate Cox analyses were performed to identify factors influencing T2DM risk. Extreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), and random forest (RF) models were trained to predict 5-year T2DM risk. Model performances were analyzed using receiver operating characteristic (ROC) curves for discrimination and calibration plots for prediction accuracy.

RESULTS

Key variables included fasting plasma glucose, age, and sedentary time. The LR model showed good accuracy with respective areas under the ROC (AUCs) of 0.914 and 0.913 in training and validation sets; the RF model exhibited favorable AUCs of 0.998 and 0.838. In calibration analysis, the LR model displayed good fit for low-risk patients; the RF model exhibited satisfactory fit for low- and high-risk patients.

CONCLUSIONS

LR and RF models can effectively predict T2DM risk in the Chinese population. These models may help identify high-risk patients and guide interventions to prevent complications and disabilities.

Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness

BACKGROUND

Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness.

METHODS

The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts.

RESULTS

The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%).

CONCLUSION

A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.

A scoping review of artificial intelligence-based methods for diabetes risk prediction

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Artificial intelligence with temporal features outperforms machine learning in predicting diabetes

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities.

Predictors of Carbohydrate Metabolism Disorders and Lethal Outcome in Patients after Myocardial Infarction: A Place of Glucose Level

BACKGROUND AND AIM

The aim of this study was to reveal statistical patterns in patients with acute myocardial infarction (AMI) that cause the development of carbohydrate metabolism disorders (CMD) (type 2 diabetes mellitus and prediabetes) and death within 5 years after AMI.

METHODS

1079 patients who were treated with AMI in the Almazov National Medical Research Center were retrospectively selected for the study. For each patient, all data from electronic medical records were downloaded. Statistical patterns that determine the development of CMDs and death within 5 years after AMI were identified. To create and train the models used in this study, the classic methods of Data Mining, Data Exploratory Analysis, and Machine Learning were used.

RESULTS

The main predictors of mortality within 5 years after AMI were advanced age, low relative level of lymphocytes, circumflex artery lesion, and glucose level. Main predictors of CMDs were low basophils, high neutrophils, high platelet distribution width, and high blood glucose level. High values of age and glucose together were relatively independent predictors. With glucose level >11 mmol/L and age >70 years, the 5-year risk of death is about 40% and it rises with increasing glucose levels.

CONCLUSION

The obtained results make it possible to predict the development of CMDs and death based on simple parameters that are easily available in clinical practice. Glucose level measured on the 1st day of AMI was among the most important predictors of CMDs and death.

Pre-test probability for coronary artery disease in patients with chest pain based on machine learning techniques

BACKGROUND

A correct and prompt diagnosis of coronary artery disease (CAD) is a crucial component of disease management to reduce the risk of death and improve the quality of life in patients with CAD. Currently, the American College of Cardiology (ACC)/American Heart Association (AHA) and the European Society of Cardiology (ESC) guidelines recommend selecting an appropriate pre-diagnosis test for an individual patient according to the CAD probability. The purpose of this study was to develop a practical pre-test probability (PTP) for obstructive CAD in patients with chest pain using machine learning (ML); also, the performance of ML-PTP for CAD is compared to the final result of coronary angiography (CAG).

METHODS

We used a database from a single-center, prospective, all-comer registry designed to reflect real-world practice since 2004. All subjects underwent invasive CAG at Korea University Guro Hospital in Seoul, South Korea. We used logistic regression algorithms, random forest (RF), supporting vector machine, and K-nearest neighbor classification for the ML models. The dataset was divided into two consecutive sets according to the registration period to validate the ML models. ML training for PTP and internal validation used the first dataset registered between 2004 and 2012 (8631 patients). The second dataset registered between 2013 and 2014 (1546 patients) was used for external validation. The primary endpoint was obstructive CAD. Obstructive CAD was defined as having a stenosis diameter of >70% on the quantitative CAG of the main epicardial coronary artery.

RESULTS

We derived an ML-based model consisting of three different models according to the subject used to obtain the information, such as the patient himself (dataset 1), the community's first medical center (dataset 2), and doctors (dataset 3). The performance range of the ML-PTP models as the non-invasive test had C-statistics of 0.795 to 0.984 compared to the result of invasive testing via CAG in patients with chest pain. The training ML-PTP models were adjusted to have 99% sensitivity for CAD so as not to miss actual CAD patients. In the testing dataset, the best accuracy of the ML-PTP model was 45.7% using dataset 1, 47.2% using dataset 2, and 92.8% using dataset 3 and the RF algorithm. The CAD prediction sensitivity was 99.0%, 99.0%, and 98.0%, respectively.

CONCLUSION

We successfully developed a high-performance model of ML-PTP for CAD which is expected to reduce the need for non-invasive tests in chest pain. However, since this PTP model is derived from data of a single medical center, multicenter verification is required to use it as a PTP recommended by the major American societies and the ESC.

Predicting hypertension onset from longitudinal electronic health records with deep learning

Objective

Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs.

Materials and Methods

We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A "train and validation") using cross-validation, and then applied the models to a second dataset (dataset B "test") to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance.

Results

With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the "train and validation" dataset A and 0.94 in the "test" dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension.

Conclusion

These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.

Insights of Host Physiological Parameters and Gut Microbiome of Indian Type 2 Diabetic Patients Visualized via Metagenomics and Machine Learning Approaches

Type 2 diabetes (T2D) is a serious public health issue and may also contribute to modification in the structure of the intestinal microbiota, implying a link between T2D and microbial inhabitants in the digestive tract. This work aimed to develop efficient models for identifying essential physiological markers for improved T2D classification using machine learning algorithms. Using amplicon metagenomic approaches, an effort has also been made to understand the alterations in core gut microbial members in Indian T2D patients with respect to their control normal glucose tolerance (NGT). Our data indicate the level of fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) were the most useful physiological indicators while random forest and support vector machine with RBF Kernel were effective predictions models for identifications of T2D. The dominating gut microbial members Allopreotella, Rikenellaceae RC9 gut group, Haemophilus, Ruminococcus torques group, etc. in Indian T2D patients showed a strong association with both FBG and HbA1c. These members have been reported to have a crucial role in gut barrier breakdown, blood glucose, and lipopolysaccharide level escalation, or as biomarkers. While the dominant NGT microbiota (Akkermansia, Ligilactobacillus, Enterobacter, etc.) in the colon has been shown to influence inflammatory immune responses by acting as an anti-inflammatory agent and maintaining the gut barrier. The topology study of co-occurrence network analysis indicates that changes in network complexity in T2D lead to variations in the different gut microbial members compared to NGT. These studies provide a better understanding of the gut microbial diversity in Indian T2D patients and show the way for the development of valuable diagnostics strategies to improve the prediction and modulation of the T2D along with already established methods.

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis

AIMS/INTRODUCTION

Recently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus. However, its predictive ability remains inconclusive. This meta-analysis evaluated the current ability of ML algorithms for predicting incident type 2 diabetes mellitus.

MATERIALS AND METHODS

We systematically searched longitudinal studies published from 1 January 1950 to 17 May 2020 using MEDLINE and EMBASE. Included studies had to compare ML's classification with the actual incidence of type 2 diabetes mellitus, and present data on the number of true positives, false positives, true negatives and false negatives. The dataset for these four values was pooled with a hierarchical summary receiver operating characteristic and a bivariate random effects model.

RESULTS

There were 12 eligible studies. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.81 (95% confidence interval [CI] 0.67-0.90), 0.82 [95% CI 0.74-0.88], 4.55 [95% CI 3.07-6.75] and 0.23 [95% CI 0.13-0.42], respectively. The area under the summarized receiver operating characteristic curve was 0.88 (95% CI 0.85-0.91).

CONCLUSIONS

Current ML algorithms have sufficient ability to help clinicians determine whether individuals will develop type 2 diabetes mellitus in the future. However, persons should be cautious before changing their attitude toward future diabetes risk after learning the result of the diabetes prediction test using ML algorithms.

The pneumonia severity index: Assessment and comparison to popular machine learning classifiers

INTRODUCTION

Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size.

METHODS

In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34,720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC).

RESULTS

Machine learning classifiers, such as Random Forest, provided a statistically highly(p < 0.001) significant improvement (∼33% in PR AUC and ∼6% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI).

DISCUSSION

Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.

Machine learning for diabetes clinical decision support: a review

Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.

Anti-Diabetic Effects of Ethanol Extract from Sanghuangporous vaninii in High-Fat/Sucrose Diet and Streptozotocin-Induced Diabetic Mice by Modulating Gut Microbiota

Type 2 diabetes mellitus (T2DM) may lead to abnormally elevated blood glucose, lipid metabolism disorder, and low-grade inflammation. Besides, the development of T2DM is always accompanied by gut microbiota dysbiosis and metabolic dysfunction. In this study, the T2DM mice model was established by feeding a high-fat/sucrose diet combined with injecting a low dose of streptozotocin. Additionally, the effects of oral administration of ethanol extract from Sanghuangporous vaninii (SVE) on T2DM and its complications (including hypoglycemia, hyperlipidemia, inflammation, and gut microbiota dysbiosis) were investigated. The results showed SVE could improve body weight, glycolipid metabolism, and inflammation-related parameters. Besides, SVE intervention effectively ameliorated the diabetes-induced pancreas and jejunum injury. Furthermore, SVE intervention significantly increased the relative abundances of Akkermansia, Dubosiella, Bacteroides, and Parabacteroides, and decreased the levels of Lactobacillus, Flavonifractor, Odoribacter, and Desulfovibrio compared to the model group (LDA > 3.0, p < 0.05). Metabolic function prediction of the intestinal microbiota by PICRUSt revealed that glycerolipid metabolism, insulin signaling pathway, PI3K-Akt signaling pathway, and fatty acid degradation were enriched in the diabetic mice treated with SVE. Moreover, the integrative analysis indicated that the key intestinal microbial phylotypes in response to SVE intervention were strongly correlated with glucose and lipid metabolism-associated biochemical parameters. These findings demonstrated that SVE has the potential to alleviate T2DM and its complications by modulating the gut microbiota imbalance.

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.

Artificial Intelligence in Current Diabetes Management and Prediction

PURPOSE OF REVIEW

Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied by the dramatic improvement in computer performance. In this review, we introduce AI/ML-based medical devices and prediction models regarding diabetes.

RECENT FINDINGS

In the field of diabetes, several AI-/ML-based medical devices and regarding automatic retinal screening, clinical diagnosis support, and patient self-management tool have already been approved by the US Food and Drug Administration. As for new-onset diabetes prediction using ML methods, its performance is not superior to conventional risk stratification models that use statistical approaches so far. Despite the current situation, it is expected that the predictive performance of AI will soon be maximized by a large amount of organized data and abundant computational resources, which will contribute to a dramatic improvement in the accuracy of disease prediction models for diabetes.

[Heart failure care in a digitalized future : A discourse on resource-sparing structures and self-determined patients]

Digital health solutions, applications of artificial intelligence (AI) and new technologies, such as cardiac magnetic resonance imaging and cardiac human genetics are currently being validated in cardiac healthcare pathways. They show promising approaches for improving existing healthcare structures in the future by strengthening the focus on predictive, preventive and personalized medicine. In addition, the accompanying use of digital health applications will become increasingly more important in the future healthcare, especially in patients with chronic diseases. In this article, the authors describe a case of chronic heart failure (HF) as an example to provide an overview of how digitalized healthcare can be efficiently designed across sectors and disciplines in the future. Moreover, the importance of a self-determined patient management for the treatment process itself is underlined. Since HF is frequently accompanied by various comorbidities during the course of the disease that are often recognized only after a delay, the necessity for a timely simultaneous and preventive treatment of multiple comorbidities in cardiovascular diseases is emphasized. Against this background the currently separately applied disease management programs (DMP) are critically questioned. The development of a holistic DMP encompassing all indications for the treatment of chronic diseases may pave the way to a more efficient medical care system.

Prospect of Artificial Intelligence Based on Electronic Medical Record

With the advent of the big data era, the interest of the international community is focusing on increasing the utilization of medical big data. Many hospitals are attempting to increase the efficiency of their operations and patient management by adopting artificial intelligence (AI) technology that enables the use of electronic medical record (EMR) data. EMR includes information about a patient's health history, such as diagnoses, medicines, tests, allergies, immunizations, treatment plans, personalized medical care, and improvement of medical quality and safety. EMR data can also be used for AI-based new drug development. In particular, it is effective to develop AI that can predict the occurrence of specific diseases or provide individualized customized treatments by classifying the individualized characteristics of patients. In order to improve performance of artificial intelligence research using EMR data, standardization and refinement of data are essential. In addition, since EMR data deal with sensitive personal information of patients, it is also vital to protect the patient's privacy. There are already various supports for the use of EMR data in the Korean government, and researchers are encouraged to be proactive.

Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort

BACKGROUND

Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) based model using a cohort representative of the Korean population.

METHODS

This study was conducted on the basis of the National Health Insurance Service-Health Screening (NHIS-HEALS) cohort of Korea. Overall, 335,302 subjects without T2DM at baseline were included. We developed the model based on 80% of the subjects, and verified the power in the remainder. Predictive models for T2DM were constructed using the recurrent neural network long short-term memory (RNN-LSTM) network and the Cox longitudinal summary model. The performance of both models over a 10-year period was compared using a time dependent area under the curve.

RESULTS

During a mean follow-up of 10.4±1.7 years, the mean frequency of periodic health check-ups was 2.9±1.0 per subject. During the observation period, T2DM was newly observed in 8.7% of the subjects. The annual performance of the model created using the RNN-LSTM network was superior to that of the Cox model, and the risk factors for T2DM, derived using the two models were similar; however, certain results differed.

CONCLUSION

The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs better than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future.

Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach

BACKGROUND

In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms.

METHODS

A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.

RESULTS

Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.

CONCLUSION

The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes

Importance

Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.

Objective

To develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data.

Design, Setting, and Participants

This decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016. A gradient boosting decision tree model was trained on data from 1 657 395 patients, validated on 243 442 patients, and tested on 236 506 patients. Costs associated with each patient were estimated using a validated costing algorithm. Data were analyzed from January 1, 2006, to December 31, 2016.

Exposures

A random sample of 2 137 343 residents of Ontario without type 2 diabetes was obtained at study start time. More than 300 features from data sets capturing demographic information, laboratory measurements, drug benefits, health care system interactions, social determinants of health, and ambulatory care and hospitalization records were compiled over 2-year patient medical histories to generate quarterly predictions.

Main Outcomes and Measures

Discrimination was assessed using the area under the receiver operating characteristic curve statistic, and calibration was assessed visually using calibration plots. Feature contribution was assessed with Shapley values. Costs were estimated in 2020 US dollars.

Results

This study trained a gradient boosting decision tree model on data from 1 657 395 patients (12 900 257 instances; 6 666 662 women [51.7%]). The developed model achieved a test area under the curve of 80.26 (range, 80.21-80.29), demonstrated good calibration, and was robust to sex, immigration status, area-level marginalization with regard to material deprivation and race/ethnicity, and low contact with the health care system. The top 5% of patients predicted as high risk by the model represented 26% of the total annual diabetes cost in Ontario.

Conclusions and Relevance

In this decision analytical model study, a machine learning model approach accurately predicted the incidence of diabetes in the population using routinely collected health administrative data. These results suggest that the model could be used to inform decision-making for population health planning and diabetes prevention.

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.

The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms

Purpose

Build machine learning models for predicting pressure ulcer nursing adverse event, and find an optimal model that predicts the occurrence of pressure ulcer accurately.

Patients and Methods

Retrospectively enrolled 5814 patients, of which 1673 suffer from pressure ulcer events. Support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN) models were used to construct the pressure ulcer prediction models, respectively. A total of 19 variables are included, and the importance of screening variables is evaluated. Meanwhile, the performance of the prediction models is evaluated and compared.

Results

The experimental results show that the four pressure ulcer prediction models all achieve good performance. Also, the AUC values of the four models are all greater than 0.95. Besides, the comparison of the four models indicates that RF model achieves a higher accuracy for the prediction of pressure ulcer.

Conclusion

This research verifies the feasibility of developing a management system for predicting nursing adverse event based on big data technology and machine learning technology. The random forest and decision tree model are more suitable for constructing a pressure ulcer prediction model. This study provides a reference for future pressure ulcer risk warning based on big data.

[Artificial intelligence in cardiology : Relevance, current applications, and future developments]

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as a basis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

BACKGROUND

The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals.

RESULTS

We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes.

CONCLUSIONS

The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .

Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes

PURPOSE OF REVIEW

Machine learning approaches-which seek to predict outcomes or classify patient features by recognizing patterns in large datasets-are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020.

RECENT FINDINGS

Machine learning approaches using tree-based learners-which produce decision trees to help guide clinical interventions-frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and "deep learning" can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process. Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.

PSO-FCM based data mining model to predict diabetic disease

BACKGROUND AND OBJECTIVE

Diabetic disease is typically composed because of higher than normal blood sugar levels. Instead the production of insulin may be regarded insufficient. It has been noted in recent days that the percentage of diabetes-affected patients have grown to a larger extent throughout the world. Evidently, this problem must be taken more seriously in the coming days to ensure that the average percentages of diabetes-affected individuals are reduced. Recently, several research teams conducted detailed research on the data mining platform to determine the precision of each other. Data mining can be used by parametric modeling from the health data, including diabetic patient data sets, to synthesize expertise in the field.

METHODS

In this study, a new model is proposed for forecasting type 2 diabetes mellitus (T2DM) based on data mining strategies. The combined Particle Swarm Optimization (PSO) and Fuzzy Clustering Means (FCM) (PSO-FCM) are used to evaluate a set of medical data relating to a diabetes diagnosis challenge.

RESULTS

Experiments are performed on the Pima Indians Diabetes Database. The sensitivity, specificity and accuracy metrics widely used in medical studies have been used to assess the effectiveness of the proposed system reliability. It was found that the prototype has achieved 8.26 percent more accuracy than the other methods.

CONCLUSION

The conclusion produced by using the method shows that, as compared with other models, the proposed PSO-FCM method delivers greater performance.

Nationwide prediction of type 2 diabetes comorbidities

Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Leveraging logistic regression-, random forest- and gradient boosting models and register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we predicted five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.04, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 1.97 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.

Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot

Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra. We show that (i) the spectra alone allows good discrimination of the samples based on rheology, (ii) 3D-PCA allows comprehensive and informative visualization of the data, and (iii) that the rheological parameters are predicted accurately using a regression method such as PLSR; instead of using chemical parameters which are laborious to obtain, Mid-FTIR spectra gathering all physicochemical information could be used for efficient prediction of firmness. As a conclusion, rheological and chemical parameters allow good discrimination of the samples according to their firmness. However, using only the IR spectra leads to better results. A good predictive model was built for the prediction of the firmness of the fruit, and we reached a coefficient of determination R² value of 0.90. This method outperforms a model based on physicochemical descriptors only. Such an approach could be very helpful to technologists and farmers.