Big Data Technologies: New Opportunities for Diabetes Management

Early detection of Dementia in Type 2 Diabetes population: Predictive analytics using Machine learning approach

BACKGROUND

The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.

OBJECTIVE

This study aims to develop a personalized predictive model, utilizing artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with Type 2 Diabetes Mellitus (T2DM) who are prescribed antidiabetic medications.

METHODS

This retrospective multicenter study used data from Taipei Medical University Clinical Research Database, which comprises electronic medical records from three hospitals in Taiwan. This study applied eight machine learning algorithms to develop prediction models, including logistic regression (LR), linear discriminant analysis (LDA), gradient boosting machine (GBM), lightGBM (LBGM), AdaBoost, random forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN). These models incorporated a range of variables, encompassing patient characteristics, comorbidities, medication usage, laboratory results, and examination data.

RESULTS

This study involved a cohort of 43,068 patients diagnosed with T2DM, which accounted for a total of 1,937,692 visits. For model development and validation, 1,300,829 visits were utilized, while an additional 636,863 visits were reserved for external testing. The area under the curve (AUC) of the prediction models range from 0.67 for the logistic regression to 0.98 for the artificial neural networks. Based on the external test results, the model built using the ANN algorithm has the best AUC: 0.97 (5-year follow-up period) and 0.98 (10-year follow-up period). Based on the best model (ANN), age, gender, triglyceride, HbA1c, anti-diabetic agents, stroke history, and other long-term medications were the most important predictors.

CONCLUSIONS

We have successfully developed a novel computer-aided dementia risk prediction model that can facilitate the clinical diagnosis and management of patients prescribed with antidiabetic medications. However, further investigation is required to assess the model's feasibility and external validity.

CLINICALTRIAL

Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology

Background

Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research.

Methods

This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research.

Results

Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly.

Conclusions

The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.

Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction

Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.

Continuous Glucose Monitoring in Healthy Adults-Possible Applications in Health Care, Wellness, and Sports

INTRODUCTION

Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports.

RESULTS

CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise.

CONCLUSIONS

CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users.

A pilot study on the epidemiology of hyperuricemia in Chinese adult population based on big data from Electronic Medical Records 2014 to 2018

BACKGROUND

We performed this study based on big data from Electronic Medical Records (EMR) of outpatients and inpatients from 52 hospitals in China to investigate the prevalence of hyperuricemia in Chinese adults.

METHODS

In this retrospective, descriptive study, a total of 3,363,016 subjects from 52 hospitals in 13 provinces and municipalities in China were enrolled. Eligible subjects were 18 years and older performing serum uric acid test between 2014 and 2018. Subjects were divided into the total group (including the subjects from all the clinic departments) and department-amended group (including the subjects from all the departments except endocrinology, orthopedics, and rheumatology and immunology departments).

RESULTS

The prevalence of hyperuricemia in the department-amended group was lower than that in the total group (23.06% and 23.42% in 2018, respectively; P<0.0001). From 2014 to 2017, the prevalence of hyperuricemia increased year by year (18.29%, 20.02%, 20.16% and 23.06%, respectively) in the department-amended group. Besides, the prevalence of hyperuricemia was higher in men than that in women (38.00% and 11.89%, respectively; P<0.0001) and higher in southern region than in northern region (25.84% and 9.79%, respectively; P<0.0001) in department-amended group in 2018.

CONCLUSIONS

Projections from our study estimate that about 271 million Chinese adults aged 18 years and older may have had hyperuricemia in 2018. These findings will be useful for the future researches and healthcare decision.

Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris

In the future artificial intelligence (AI) will have the potential to improve outcomes diabetes care. With the creation of new sensors for physiological monitoring sensors and the introduction of smart insulin pens, novel data relationships based on personal phenotypic and genotypic information will lead to selections of tailored, effective therapies that will transform health care. However, decision-making processes based exclusively on quantitative metrics that ignore qualitative factors could create a quantitative fallacy. Difficult to quantify inputs into AI-based therapeutic decision-making processes include empathy, compassion, experience, and unconscious bias. Failure to consider these "softer" variables could lead to important errors. In other words, that which is not quantified about human health and behavior is still part of the calculus for determining therapeutic interventions.

Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables

BACKGROUND

The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares the performance of these classification algorithms to identify patients who are at risk of developing T2D in short, medium and long terms. In addition, the list of predictor variables important for prediction for T2D progression is provided.

METHODS

This study uses 10,911 records generated in 36 clinics from the 15th of November 2008-15th of November 2016. Syntactic minority oversampling and random under sampling were used to create a balanced dataset. The performance of Neural Networks, Support Vector Machines, Decision Tress and Logistic Regression to identify patients developing T2D in short, medium and long terms was compared. The measures were Area Under Curve, Sensitivity, Specificity, Matthew correlation coefficient and Mean Calibration Error. Through importance analysis and information fusion techniques the predictors of developing T2D were identified for short, medium and long-term risk analysis.

RESULTS

The findings show that the performance of analytics techniques depends on both period and purpose of prediction whether the prediction is to identify people who will not develop T2D or to determine at risk patients. Oversampling as opposed to under sampling improved performance. 16 predictors and their importance to determine patients at risk of T2D in short, medium and long terms were identified.

CONCLUSIONS

This study provides guidelines for an automated system to prompt patients for screening. Several predictors are reportable by patients, others can be examined by physicians or ordered for further lab examination, which offers a potential reduction of the burden placed upon the clinical settings.

Concurrence of big data analytics and healthcare: A systematic review

BACKGROUND

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care.

PURPOSE

This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges.

DATA SOURCES

A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered.

STUDY SELECTION

Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected.

DATA EXTRACTION

Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare.

RESULTS

A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare.

CONCLUSION

This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

Clinical Research Informatics Contributions from 2015

OBJECTIVES

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015.

METHOD

A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers.

RESULTS

Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations.

CONCLUSIONS

A major trend in the 2015 publications is the analysis of observational, "nonexperimental" information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.

Glucose: archetypal biomarker in diabetes diagnosis, clinical management and research

The clinical utility of diabetes biomarkers can be considered in terms of diagnosis, management and prediction of long-term vascular complications. Glucose satisfies all of these requirements. Thresholds of hyperglycemia diagnostic of diabetes reflect inflections that confer a risk of developing long-term microvascular complications. Degrees of hyperglycemia (impaired fasting glucose, impaired glucose tolerance) that lie below the diagnostic threshold for diabetes identify individuals at risk of progression to diabetes and/or development of atherothrombotic cardiovascular disease. Self-measured glucose levels usefully complement hemoglobin A1c levels to guide daily management decisions. Continuous glucose monitoring provides detailed real-time data that is of value in clinical decision making, assessing response to new diabetes drugs and the development of closed-loop artificial pancreas technology.

Use of an holistic approach for effective adoption of User-Centred-Design techniques in diabetes disease management: Experiences in user need elicitation

One of the most important challenges of designing eHealth tools for Chronic Disease Management is to understand how transforming cutting-edge innovations in something that can impact the current clinical practice and improve the performance of the health care systems. The adoption of User Centered Design techniques is fundamental in order to integrate these systems in an effective and successful way. The work presented in this paper describe the methodologies used in the context of two multidisciplinary research projects, METABO and MOSAIC. The adoption of the methodologies have been driven by the CeHRes Roamap, a holistic framework that support participatory development of eHealth. The work reported in this paper describes the results of the first two (out of the five) phases in eliciting user needs.

A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps

The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.

Integration of Administrative, Clinical, and Environmental Data to Support the Management of Type 2 Diabetes Mellitus: From Satellites to Clinical Care

A very interesting perspective of "big data" in diabetes management stands in the integration of environmental information with data gathered for clinical and administrative purposes, to increase the capability of understanding spatial and temporal patterns of diseases. Within the MOSAIC project, funded by the European Union with the goal to design new diabetes analytics, we have jointly analyzed a clinical-administrative dataset of nearly 1.000 type 2 diabetes patients with environmental information derived from air quality maps acquired from remote sensing (satellite) data. Within this context we have adopted a general analysis framework able to deal with a large variety of temporal, geo-localized data. Thanks to the exploitation of time series analysis and satellite images processing, we studied whether glycemic control showed seasonal variations and if they have a spatiotemporal correlation with air pollution maps. We observed a link between the seasonal trends of glycated hemoglobin and air pollution in some of the considered geographic areas. Such findings will need future investigations for further confirmation. This work shows that it is possible to successfully deal with big data by implementing new analytics and how their exploration may provide new scenarios to better understand clinical phenomena.