EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

K-CUSUM: Cluster Detection Mechanism in EDMON

Source at https://www.ep.liu.se/ecp/contents.asp?issue=161. The main goal of the EDMON (Electronic Disease Monitoring Network) project is to detect the spread of contagious diseases at the earliest possible moment, and potentially before people know that they have been infected. The results shall be visualized on real-time maps as well as presented in digital communication. In this paper, a hybrid of K-nearness Neighbor (KNN) and cumulative sum (CUSUM), known as K-CUSUM, were explored and implemented with a prototype approach. The KNN algorithm, which was implemented in the K- CUSUM, recorded 99.52% accuracy when it was tested with simulated dataset containing geolocation coordinates among other features and SckitLearn KNN algorithm achieved an accuracy of 93.81% when it was tested with the same dataset. After injection of spikes of known outbreaks in the simulated data, the CUSUM module was totally specific and sensitive by correctly identifying all outbreaks and non-outbreak clusters. Suitable methods for obtaining a balance point of anonymizing geolocation attributes towards obscuring the privacy and confidentiality of diabetes subjects’ trajectories while maintaining the data requirements for public good, in terms of disease surveillance, remains a challenge

Reinforcement learning application in diabetes blood glucose control: A systematic review

Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient’s own data. Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms

A systematic review of cluster detection mechanisms in syndromic surveillance: Towards developing a framework of cluster detection mechanisms for EDMON system

Source at http://www.ep.liu.se/ecp/151/011/ecp18151011.pdf.Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level and other supporting parameters from people with type 1 diabetes, as indicator variables for outbreak detection. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework to be used in EDMON and other similar syndromic surveillance systems. Various challenges such as user mobility, privacy and confidentiality, geographical location estimation and other factors have been considered. To this end, we conducted a systematic review exploring different online scholarly databases. Considering peer reviewed journals and articles, literatures search was conducted between January and March 2018. Relevant literatures were identified using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria and a full text review were done for literatures that were found to be relevant. A total of 28 articles were included in the study. The result indicates that various clustering and aberration detection algorithms have been developed and tested up to the task. In this regard, privacy preserving policies and high computational power requirement were found challenging since it restrict usage of specific locations for syndromic surveillance

Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs’ security practices in various scenarios including big data. The intention is to determine the gap between staffs’ security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs’ security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies

mHealth: Where Is the Potential for Aiding Informal Caregivers?

The health and well-being of informal caregivers often take a backseat to those that they care for. While systems, technologies, and services that provide care and support for those with chronic illnesses are established and continuously improved, those that support informal caregivers are less explored. An international survey about motivations to use mHealth technologies was posted to online platforms related to chronic illnesses. We focused on responses regarding the facilitators and challenges of achieving health goals, including the use of mHealth technologies, for the subgroup who identified as “Caregivers”. Findings indicate that mHealth technology is not yet the most important motivational factor for achieving health goals in this group, but greater future potential is suggested

What motivates patients with NCDs to follow up their treatment?

Workshop at the 31st Medical Informatics Europe virtual conference, 29.05.21 - 31.05.21: https://efmi.org/2020/12/10/31st-medical-informatics-europe-conference-mie2021-athens-greece/.The increasing use of mobile health (mHealth) tools for self-management is considered to be important to improve health effects for patients with chronic NCDs (noncommunicable diseases). This development is supported by an increasing number of available mHealth apps. The apps range from disease management apps (e.g., diabetes diary) to health and fitness apps (e.g., dietary apps and workout apps). However, there seems to be a lack of motivation from most users to keep using these health apps over a long period of time [1]. This may be because of the way these apps were designed and developed, i.e. lack of co-participatory design techniques and lack of a tested developer guideline for creating mHealth solutions. The motivation behind this workshop is to identify motivational factors which will increase adoption and usage of mHealth apps. Since 2001, several of the presenters have been working on self-management tools for people with diabetes [2, 3]. The main tool is a diabetes diary – the “Few Touch Application” (Norwegian, “Diabetesdagboka”), available for free from Google Play, and used by several thousands of users [4-8]

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Electronic disease surveillance system based on inputs from people with diabetes: an early outbreak detection mechanism.

Objective: Generally, the purpose of this thesis project is to develop an effective electronic disease surveillance system, which is capable of detecting a cluster of people with elevated blood glucose (BG) levels within a specific region and timeframe by analyzing diabetes data. Specifically, we mainly focus on developing an early outbreak detection algorithm that can analyze BG data and detect individuals with an elevated BG level (aberrant patterns) using continues blood glucose measurement (CGM) and the mobile phone- based diabetes patients’ historical data – the diabetes diary. Material: This thesis project was conducted using data from two individuals with type-1 diabetes. The Dexcom continuous glucose monitoring device (CGM)) was used for the data collection. The collected data were CGM (in 5 minutes’ intervals) for a period of one month. We used these datasets to train and validate the developed system. After training and validating the system, for its goodness of fit to the individual BG dynamics, in the non-infection status of the two subjects using normal BG values, we tested our system with artificially simulated datasets, which resemble the individual BG evolution during infections. The simulated datasets were consisted of elevated or high BG values of varies size, duration and shape through a course of time, i.e. a week or more. It was simulated so as to resemble the elevated BG after one is infected, by considering various increments per minutes (∆BG/ (minutes (t))) and various durations of elevated BG. The system was developed using Matlab version R2015b. Method: We presented a system that is consisted of four modules: the data collection module, the blood glucose prediction, the outbreak detection, and the information dissemination and reporting module. There are two types of early outbreak detection approaches incorporated in the system, a type of statistical control (prediction interval-based) algorithm and a moving window based z-score process. The first approach, the prediction interval-based algorithm combined a novel mechanism for BG prediction, which is an interval prediction based on a set of autoregressive models and predicts the expected BG intervals for an individual with diabetes. The actual BG value is compared against the predicted intervals, which is generated using auto-regressive (AR) and Autoregressive moving average (ARMA) methods. We evaluated and compared the performance of these methods using the mean square errors (MSE) and root mean square errors (RMSE) functions. The second approach, the moving window based z-score process calculates a running mean and standard deviation based on a specific window size. The running mean and standard deviation are used to check the agreement of the current BG reading with the previous trend in the window. The performance of the process was evaluated based on the accuracy of detecting the specific surveillance case definition, i.e. sensitivity, specificity and positive predictive value (PPV). Result: Both the prediction interval-based algorithm and the moving window based z-score process were tested against the artificially simulated datasets and were capable of detecting statistically significant BG deviation of various sizes and durations. The prediction methods were capable of predicting the single step - ahead BG values with a reasonable accuracy, which were tested against validation datasets (unseen datasets during training). All the methods, autoregressive (AR), autoregressive (AR) with ratio of consecutive data as inputs, and autoregressive moving average (ARMA) have attained minimum root mean square errors (RMSE) for both subjects. However, the second methods predict well attaining the lowest RMSE for both subjects, which demonstrates the advantage gained through the use of ratio of consecutive data points rather than the raw blood glucose data. Moreover, we accurately monitored the BG fluctuations of both individuals with a significance level of α =0.01. However, there are difference in window size and RMSE attained by these subjects for a comparable interval width, where the first subject attained smaller than the second subject. In addition, for comparable detection capability, the size of the moving window used to calculate the z-score for the first subject is less than the second subject. This clearly shows the effect of personal behavior towards diabetes management on the detection capability, which is mainly due to the significant fluctuations in BG readings as a result of poor personal behavior in managing his/her diabetes. Conclusion: Generally, both of our early outbreak detection approaches have produced optimal detection results and were capable of detecting statistically significant BG deviation of various size and duration. However, considering flexibility, simplicity, computational time, and needs of computational power the moving window based z-score process is better than the prediction interval-based algorithm. Moreover, both the approaches are found to be affected by the quality of personal behavior towards diabetes management and this needs to be taken into account during large scale implementations. Besides, these results have clearly shown the effectiveness of the proposed approaches for detecting a cluster of people with similar patterns. . Consequently, after validating these approaches on a large scale basis, this promising results will hopefully lead the way for the development of the early outbreak detection system (prototype) based on inputs from people with diabetes, which is considered to be the next generation electronic disease surveillance system

Appendix E Telemedisinske løsninger i maritime operasjoner og redningstjeneste

Folk som jobber i maritime miljø har ikke enkel tilgang til sentrale helsetjenester, og det gjelder spesielt for sjøfolk som arbeider i arktiske områder. Selv om telemedisin har vært en suksess på land, har telemedisin kun i begrenset grad blitt tatt i bruk til havs. Dette skyldes blant annet fravær av gode kommunikasjonsløsninger, dårlige værforhold, store avstander og lange perioder utenfor rekkevidde for søk- og rednings- (SAR) helikopter - noe som reduserer muligheten for medisinsk evakuering (MEDEVAC). Adopsjon av landbasert teknologi kan fremstå som en rask løsning for å fremskaffe tilgang til medisinskfaglig kompetanse, men dette er ofte utfordrende av ulike grunner. Siden maritim og landbasert telemedisin både kan være konvergent og divergent med hensyn til strukturelle, praktiske og politiske forskjeller så er det nødvendig å identifisere disse forskjellene og studere disse før man eventuelt overfører teknologi og forskningsresultater til maritime forhold. Til tross for disse begrensningene har vi nylig erfart at maritim telemedisin har lykkes i å levere telemedisinske tjenester i Arktis, Antarktis og i andre områder med ekstremvær. Disse tjenestene inkluderer telekonsultasjon, teleradiologi, telekardiologi, tele-ØNH (øre-nese-hals), og teledermatologi. De fleste av disse tjenestene har vært realisert ved hjelp av ulike former for kommunikasjon (satellitt, mobil, radio og andre). Dessuten har alle disse studiene vist bruk av ulike telemedisinske modaliteter inkludert video, stillbilder, lyd og medisinske data. Imidlertid er bruk av telemedisin i forhold til søk og redningstjenester (SAR) ennå ikke fullt utnyttet. Vi ser for oss disse implementert og evaluert slik at telemedisinske tjenester vil danne en underliggende modell for en vellykket gjennomføring av fremtidige søk og redningstjenester. Formålet med denne rapporten er å anslå og analysere nåværende status til telemedisinske tjenester i sammenheng med maritim, ulykker og akuttmedisinske behov under ekstremvær og i arktiske områder. Videre gjennomgås nåværende state-of-the-art systemer for å gjennomføre vellykkede telemedisinske tjenester i arktiske og fjerntliggende områder. Rapporten avsluttes med konkrete anbefalinger for å kunne møte eksisterende problemer med hensyn til søke- og redningsoperasjoner i arktiske strøk