Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates

This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR. This study utilized a cross-sectional dataset of 3,000 patients from the People's Liberation Army General Hospital in 2021. Logistic regression was used as the baseline model to compare the prediction performance of the machine learning model and the related factors. The recursive feature elimination cross-validation (RFECV) algorithm was used to select features. Four machine learning models, support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boost decision tree (GBDT), were developed to predict DR. The models were optimized using grid search to determine hyperparameters, and the model with superior performance was selected. Shapley-additive explanations (SHAP) were used to analyze the important correlation factors of DR. Among the four machine learning models, the optimal model was GBDT, with predicted accuracy, precision, recall, F1-measure, and AUC values of 0.7883, 0.8299, 0.7539, 0.7901, and 0.8672, respectively. Six key correlates of DR were identified, including rapid micronutrient protein/creatinine measurement, 24-h micronutrient protein, fasting C-peptide, glycosylated hemoglobin, blood urea, and creatinine. The logistic model had 27 risk factors, with an AUC value of 0.8341. A superior prediction model was constructed that identified easily explainable key factors. The number of correlation factors was significantly lower compared to traditional statistical methods, leading to a more accurate prediction performance than the latter.

Probabilistic neural network based visual data mining for the healthcare sector

BACKGROUND

The need for personalised care in the long-term management of patient health is paramount due to the variability in individual features and responses to specific medication. With the availability of large quantities of electronic patient records, big data analysis presents a valuable opportunity to gain insights into disease presentation and patient impact.

OBJECTIVE

This study aims to utilise data science in the medical field to extract unknown information from databases, validate previously obtained data, and enhance personalised patient care.

METHODS

An analytics suite is developed for monitoring patient health and treating cholesterol, thyroid, and diabetes disorders. This suite employs exploratory, predictive, and visual analytics to categorise patient data into multiple tiers and forecast related complication risk and treatment response.

RESULTS

The study found that the analytics suite could successfully identify correlations between various biological indicators of patients and disorders. The suite also showcased potential in predicting health risks and responses to treatments.

CONCLUSION

The analytics employed in this study suggest advanced methods of data analysis, which could serve as potential decision-making tools for healthcare providers. These methods might lead to improved treatment outcomes, contributing significantly to personalised patient care.

Baseline treatments and metabolic control of 288,913 type 2 diabetes patients in a 10-year retrospective cohort in Malaysia

Diabetes is one of the quickest-growing global health emergencies of the twenty-first century, and data-driven care can improve the quality of diabetes management. We aimed to describe the formation of a 10-year retrospective open cohort of type 2 diabetes patients in Malaysia. We also described the baseline treatment profiles and HbA1c, blood pressure, and lipid control to assess the quality of diabetes care. We used 10 years of cross-sectional audit datasets from the National Diabetes Registry and merged 288,913 patients with the same identifying information into a 10-year open cohort dataset. Treatment targets for HbA1c, blood pressure, LDL-cholesterol, HDL-cholesterol, and triglycerides were based on Malaysian clinical practice guidelines. IBM SPSS Statistics version 23.0 was used, and frequencies and percentages with 95% confidence intervals were reported. In total, 288,913 patients were included, with 62.3% women and 54.1% younger adults. The commonest diabetes treatment modality was oral hypoglycaemic agents (75.9%). Meanwhile, 19.3% of patients had ≥ 3 antihypertensive agents, and 71.2% were on lipid-lowering drugs. Metformin (86.1%), angiotensin-converting enzyme inhibitors (49.6%), and statins (69.2%) were the most prescribed antidiabetic, antihypertensive, and lipid-lowering medications, respectively. The mean HbA1c was 7.96 ± 2.11, and 31.2% had HbA1c > 8.5%. Only 35.8% and 35.2% attained blood pressure < 140/80 mmHg and LDL-cholesterol < 2.6 mmol/L, respectively. About 57.5% and 52.9% achieved their respective triglyceride and HDL-cholesterol goals. In conclusion, data integration is a feasible method in this diabetes registry. HbA1c, blood pressure, and lipids are not optimally controlled, and these findings can be capitalized as a guideline by clinicians, programme managers, and health policymakers to improve the quality of diabetes care and prevent long-term complications in Malaysia.

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Configurational path of successful entrepreneurship based on open government data: a QCA analysis

Purpose

In the era of big data, data have become an essential factor of production. In the context of open government data (OGD), encouraging the commercial development of OGD is significant for promoting mass innovation and entrepreneurship. This study aims to explore the configurational impact of data supply, external environment and entrepreneurial foundation on data-driven entrepreneurship.

Design/methodology/approach

This research used a fuzzy set qualitative comparative analysis (fsQCA). Fourteen start-ups using OGD were taken as a case sample.

Findings

This study produces two paths to achieving high entrepreneurial performance, one is a financing-enhanced entrepreneurial path and the other is a data-driven entrepreneurial path. Besides, four conditions are necessary for high performance of OGD-based entrepreneurship: good data quality, mature legal environment, favorable market environment and abundant big data entrepreneurial talents.

Practical implications

The findings have important practical implications for formulating policies related to promoting the application of government open data and innovation and entrepreneurship in terms of strengthening top-level design, improving the legal environment, developing the data market and cultivating entrepreneurial talents.

Originality/value

Although many studies have been conducted on OGD, studies on the paths to successful entrepreneurship based on OGD are limited. In this study, this issue is investigated from a configurational perspective by using the fsQCA technique.

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying

Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently and economically, an intelligent prediction of glucose is demanding. A glucose trajectory prediction system based on subcutaneous interstitial continuous glucose monitoring data and deep learning models for ensuing glucose trajectory was constructed, followed by the application of personalised prediction models on one participant with type 2 diabetes in a community. The predictive accuracy was then assessed by RMSE (root mean square error) using blood glucose data. Changes in glycaemic parameters of the participant before and after model intervention were also compared to examine the efficacy of this intelligence-aided health care. Individual Recurrent Neural Network model was developed on glucose data, with an average daily RMSE of 1.59 mmol/L in the application segment. In terms of the glucose variation, the mean glucose decreased by 0.66 mmol/L, and HBGI dropped from 12.99 × 10² to 9.17 × 10². However, the participant also had increased stress, especially in eating and social support. Our research presented a personalised care system for people with diabetes based on deep learning. The intelligence-aided health management system is promising to enhance the outcome of diabetic patients, but further research is also necessary to decrease stress in the intelligence-aided health management and investigate the stress impacts on diabetic patients.

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.

Optimizing the use of technology to support people with diabetes: research recommendations from Diabetes UK's 2019 diabetes and technology workshop

AIMS

To identify key gaps in the research evidence base that could help improve how technology supports people with diabetes, and provide recommendations to researchers and research funders on how best to address them.

METHODS

A research workshop was conducted, bringing together research experts in diabetes, research experts in technology, people living with diabetes and healthcare professionals.

RESULTS

The following key areas within this field were identified, and research recommendations for each were developed: Matching the pace of research with that of technology development Time in range as a measure Health inequalities and high-risk groups How to train people to use technology most effectively Impact of technology usage on mental health CONCLUSIONS: This position statement outlines recommendations through which research could improve how technology is employed to care for and support people living with diabetes, and calls on the research community and funders to address them in future research programmes and strategies.

Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data

This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.

Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review

BACKGROUND

Diabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences.

OBJECTIVE

This study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus.

METHODS

A systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale.

RESULTS

The search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level.

CONCLUSIONS

The use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.

Accuracy of Japanese claims data in identifying diabetes-related complications

PURPOSE

To evaluate the accuracy of various claims-based definitions of diabetes-related complications (coronary artery disease [CAD], heart failure, cerebrovascular disease and dialysis).

METHODS

We evaluated data on 1379 inpatients who received care at the Niigata University Medical & Dental Hospital in September 2018. Manual electronic medical chart reviews were conducted for all patients with regard to diabetes-related complications and were used as the gold standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each claims-based definition associated with diabetes-related complications based on Diagnosis Procedure Combination (DPC), International Classification of Diseases, Tenth Revision (ICD-10) codes, procedure codes and medication codes were calculated.

RESULTS

DPC-based definitions had higher sensitivity, specificity, and PPV than ICD-10 code definitions for CAD and cerebrovascular disease, with sensitivity of 0.963-1.000 and 0.905-0.952, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. Sensitivity, specificity, and PPV were high using procedure codes for CAD and dialysis, with sensitivity of 0.963 and 1.000, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. DPC and/or ICD-10 codes + medication were better for heart failure than the ICD-10 code definition, with sensitivity of 0.933, specificity of 1.000, and PPV of 1.000. The PPVs were lower than 60% for all diabetes-related complications using ICD-10 codes only.

CONCLUSION

The DPC-based definitions for CAD and cerebrovascular disease, procedure codes for CAD and dialysis, and DPC or ICD-10 codes with medication codes for heart failure could accurately identify these diabetes-related complications from claims databases.