Background: Individuals with type 2 diabetes (T2D) are at increased risk of developing cardiovascular disease (CVD) which necessitates monitoring of risk factors and appropriate pharmacotherapy. This study aimed to identify factors predicting emergency department visits, hospitalizations, and mortality among T2D patients after being newly diagnosed with CVD.

Methods: In a retrospective observational study conducted in Region Halland, individuals aged > 40 years with T2D diagnosed between 2011 and 2019, and a new diagnosis of CVD between 2016 and 2019, were followed for one year from the date of CVD diagnosis. The first encounter for CVD diagnosis was categorized as inpatient-, outpatient-, primary-, or emergency department care. Follow-up included laboratory tests, blood pressure, pharmacotherapies, and healthcare utilization. Hazard ratios (HR) in two Cox regression analyses determined relative risks for emergency visits/hospitalization and mortality, adjusting for age, sex, glucose regulation, lipid levels, kidney function, blood pressure, pharmacotherapy, and healthcare utilization.

Results: The study included a total of 1759 T2D individuals who received a new CVD diagnosis, with 67% diagnosed during inpatient care. The average hospitalization stay was 6.5 days, and primary care follow-up averaged 10.1 visits. Patients with CVD diagnosed in primary care had a HR 0.52 (confidence interval [CI] 0.35–0.77) for emergency department visits/hospitalization, but age had a HR 1.02 (CI 1.00-1.03). Pharmacotherapy with insulin, DPP4-inhibitors, aldosterone antagonists, and beta-blockers had a raised HR. Highest mortality risk was observed when CVD was diagnosed inpatient care, systolic blood pressure < 100 mm Hg and elevated HbA1c. Age had a HR 1.05 (CI 1.03–1.08), eGFR < 30 ml/min HR 1.46 (CI 1.01–2.11), and LDL-Cholesterol > 2,5 h 1.46 (CI 1.01–2.11) and associated with increased mortality risk. Pharmacotherapy with metformin had a HR 0.41 (CI 0.28–0.62), statins a HR 0.39 (CI 0.27–0.57), and a primary care follow-up < 30 days a HR 0.53 (CI 0.37–0.77) and associated with lower mortality risk.

Conclusions: T2D individuals who had a new diagnosis of CVD were predominantly diagnosed when hospitalized, while follow-up typically occurred in primary care. Identifying factors that predict risks of mortality and hospitalization should be a focus of follow-up care, underscoring the critical role of primary care in the effective management of T2D and CVD. © The Author(s) 2024.

The purpose of this research is to get a better understanding of how different machine learning algorithms work with different amounts of data corruption. This is important since data corruption is an overbearing issue within data collection and thus, in extension, any work that relies on the collected data. The questions we were looking at were: What feature is the most important? How significant is the correlation of features? What algorithms should be used given the data available? And, How much noise (inaccurate or unhelpful captured data) is acceptable? 

The study is structured to introduce AI in healthcare, data missingness, and the machine learning algorithms we used in the study. In the method section, we give a recommended workflow for handling data with machine learning in mind.

The results show us that when a dataset is filled with random values, the run-time of algorithms increases since many patterns are lost. Randomly removing values also caused less of a problem than first anticipated since we ran multiple trials, evening out any problems caused by the lost values. Lastly, imputation is a preferred way of handling missing data since it retained many dataset structures. One has to keep in mind if the imputation is done on categories or numerical values.

However, there is no easy "best-fit" for any dataset. It is hard to give a concrete answer when choosing a machine learning algorithm that fits any dataset. Nevertheless, since it is easy to simply plug-and-play with many algorithms, we would recommend any user try different ones before deciding which one fits a project the best.

Background: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension.

Objective: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension.

Methods: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study.

Results: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it.

Conclusions: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff. © Kobra Etminani, Carina Göransson, Alexander Galozy, Margaretha Norell Pejner, Sławomir Nowaczyk.

Background: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension.

Objective: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension.

Methods: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study.

Results: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it.

Conclusions: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff.

Trial Registration: ClinicalTrials.gov NCT04413500; https://clinicaltrials.gov/ct2/show/NCT04413500

Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care. Particularly for chronic diseases, where ongoing support is often required, patients must understand their illness and be empowered to manage their care. With the advent of smart devices such as smartphones, it is easier than ever to provide personalised digital interventions to patients, help them manage their treatment in their daily lives, and raise awareness about their illness. If such new approaches are to succeed, scalability is necessary, and solutions are needed that can act autonomously without costly human intervention. Furthermore, solutions should exhibit adaptability to the changing circumstances of an individual patient's health, needs and goals. Through the ongoing digitisation of healthcare, we are presented with the unique opportunity to develop cost-effective and scalable solutions through Artificial Intelligence (AI).

This thesis presents work that we conducted as part of the project improving Medication Adherence through Person-Centered Care and Adaptive Interventions (iMedA) that aims to provide personalised adaptive interventions to hypertensive patients, supporting them in managing their medication regiment. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA and discussions on limitations of such approaches for analysing MA. Furthermore, providing personalised adaptive interventions is framed in the contextual bandit setting and addresses the challenge of delivering relevant interventions in environments where contextual information is significantly corrupted.       

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs and (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments.  

This thesis presents work conducted in the domain of sequential decision-making in general and Bandit problems in particular, tackling challenges from a practical and theoretical perspective, framed in the contexts of mobile Health. The early stages of this work have been conducted in the context of the project ``improving Medication Adherence through Person-Centred Care and Adaptive Interventions'' (iMedA) which aims to provide personalized adaptive interventions to hypertensive patients, supporting them in managing their medication regimen. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA, and discussions on limitations of such approaches for analyzing MA. Providing personalized adaptive interventions is framed in several bandit settings and addresses the challenge of delivering relevant interventions in environments where contextual information is unreliable and full of noise. Furthermore, the need for good initial policies is explored and improved in the latent-bandits setting, utilizing prior collected data to optimal selection the best intervention at every decision point. As the final concluding work, this thesis elaborates on the need for privacy and explores different privatization techniques in the form of noise-additive strategies using a realistic recommendation scenario.         

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs, (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments. (4) Algorithmic improvements, equipping the agent with information-gathering capabilities for active action selection in the latent bandit setting, and (5) exploring important privacy aspects using a realistic recommender scenario.   

Recent advancements in Artificial Intelligence has prompted a shearexplosion of new research initiatives and applications, improving notonly existing technologies, but also opening up opportunities for newand exiting applications. This thesis explores the MIMIC-III intensive care unit database and conducts experiment on an interpretable feature space based on sever-ty scores, defining a patient health state, commonly used to predict mortality in an ICU setting. Patient health state trajectories are clustered and correlated with administered medication and performed procedures to get a better understanding of the potential usefulness in evaluating treatments on their effect on said health state, where commonalities and deviations in treatment can be understood. Furthermore, medication and procedure classification is carried out to explore their predictability using the severity subscore feature space.

This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailored privacy techniques that address specific attack patterns rather than relying on one-size-fits-all solutions. We use the latent bandit setting to evaluate the trade-off between privacy and recommender performance by employing various aggregation strategies, such as averaging, nearest neighbor, and clustering combined with noise injection. More specifically, we simulate a linkage attack scenario leveraging publicly available auxiliary information acquired by the adversary. Our results on three open real-world datasets reveal that adding noise using the Laplace mechanism to an individual user's data record is a poor choice. It provides the highest regret for any noise level, relative to de-anonymization probability and the ADS metric. Instead, one should combine noise with appropriate aggregation strategies. For example, using averages from clusters of different sizes provides flexibility not achievable by varying the amount of noise alone. Generally, no single aggregation strategy can consistently achieve the optimum regret for a given desired level of privacy.

In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.

In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. © 2022 The Author(s). 

Background and purpose

Low adherence to medication in chronic disease patients leads to increased morbidity, mortality, and healthcare costs. The widespread adoption of electronic prescription and dispensation records allows a more comprehensive overview of medication utilization. In combination with electronic health records (EHR), such data provides new opportunities for identifying patients at risk of nonadherence and provide more targeted and effective interventions. The purpose of this article is to study the predictability of medication adherence for a cohort of hypertensive patients, focusing on healthcare utilization factors under various predictive scenarios. Furthermore, we discover common proportion of days covered patterns (PDC-patterns) for patients with index prescriptions and simulate medication-taking behaviours that might explain observed patterns.

Procedures

We predict refill adherence focusing on factors of healthcare utilization, such as visits, prescription information and demographics of patient and prescriber. We train models with machine learning algorithms, using four different data splits: stratified random, patient, temporal forward prediction with and without index patients. We extract frequent, two-year long PDC-patterns using K-means clustering and investigate five simple models of medication-taking that can generate such PDC-patterns.

Findings

Model performance varies between data splits (AUC test set: 0.77–0.89). Including historical information increases the performance slightly in most cases (approx. 1–2% absolute AUC uplift). Models show low predictive performance (AUC test set: 0.56–0.66) on index-prescriptions and patients with sudden drops in PDC (Recall: 0.58–0.63). We find 21 distinct two-year PDC-patterns, ranging from good adherence to intermittent gaps and early discontinuation in the first or second year. Simulations show that observed PDC-patterns can only be explained by specific medication consumption behaviours.

Conclusions

Prediction models developed using EHR exhibit bias towards patients with high healthcare utilization. Even though actual medication-taking is not observable, consumption patterns may not be as arbitrary, provided that medication refilling and consumption is linked.  © 2020 The Authors. Published by Elsevier Inc.

We propose a new sequential decision-making setting, combining key aspects of two established online learning problems with bandit feedback. The optimal action to play at any given moment is contingent on an underlying changing state which is not directly observable by the agent. Each state is associated with a context distribution, possibly corrupted, allowing the agent to identify the state. Furthermore, states evolve in a Markovian fashion, providing useful information to estimate the current state via state history. In the proposed problem setting, we tackle the challenge of deciding on which of the two sources of information the agent should base its arm selection. We present an algorithm that uses a referee to dynamically combine the policies of a contextual bandit and a multi-armed bandit. We capture the time-correlation of states through iteratively learning the action-reward transition model, allowing for efficient exploration of actions. Our setting is motivated by adaptive mobile health (mHealth) interventions. Users transition through different, time-correlated, but only partially observable internal states, determining their current needs. The side information associated with each internal state might not always be reliable, and standard approaches solely rely on the context risk of incurring high regret. Similarly, some users might exhibit weaker correlations between subsequent states, leading to approaches that solely rely on state transitions risking the same. We analyze our setting and algorithm in terms of regret lower bound and upper bounds and evaluate our method on simulated medication adherence intervention data and several real-world data sets, showing improved empirical performance compared to several popular algorithms. 

We consider a variant of the novel contextual bandit problem with corrupted context, which we call the contextual bandit problem with corrupted context and action correlation, where actions exhibit a relationship structure that can be exploited to guide the exploration of viable next decisions. Our setting is primarily motivated by adaptive mobile health interventions and related applications, where users might transitions through different stages requiring more targeted action selection approaches. In such settings, keeping user engagement is paramount for the success of interventions and therefore it is vital to provide relevant recommendations in a timely manner. The context provided by users might not always be informative at every decision point and standard contextual approaches to action selection will incur high regret. We propose a meta-algorithm using a referee that dynamically combines the policies of a contextual bandit and multi-armed bandit, similar to previous work, as wells as a simple correlation mechanism that captures action to action transition probabilities allowing for more efficient exploration of time-correlated actions. We evaluate empirically the performance of said algorithm on a simulation where the sequence of best actions is determined by a hidden state that evolves in a Markovian manner. We show that the proposed meta-algorithm improves upon regret in situations where the performance of both policies varies such that one is strictly superior to the other for a given time period. To demonstrate that our setting has relevant practical applicability, we evaluate our method on several real world data sets, clearly showing better empirical performance compared to a set of simple algorithms.

Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMIC-III intensive care unit database and conduct experiments on an interpretable feature space based on the fusion of severity subscores, commonly used to predict mortality in an ICU setting. Clustering of medication and procedure context vectors based on a semantic representation has been performed to find common and individual treatment patterns. Two-day patient health state trajectories of a cohort of congestive heart failure patients are clustered and correlated with the treatment and evaluated based on an increase or reduction of probability of mortality on the second day of stay. Experimental results show differences in treatments and outcomes and the potential for using patient health state trajectories as a starting point for further evaluation of medical treatments and interventions. © Springer Nature Switzerland AG 2019.

Background and purpose: Patients’ adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records.

Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference.

Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases.

Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals. © 2020 The Authors. Published by Elsevier B.V. 

Användningen av yttermått hos produkter har en stor betydelse inom en stor mängd områden och en digital mätning av detta kan medföra en mer strömlinjeformad arbetsgång. I detta projektet undersöks sensorers lämplighet för mätning av oregelbundet formade objekt och det byggs en prototyp för digital mätning. Prototypen består av en mätvagn, Raspberry Pi, två kameror och MQTT med React-baserat webbgränssnitt. Mätning startas av en klient och rapporteras tillbaka till klienten med mätstationens fastslagna mätvärdena. Resultatet slutar i att 79% av uppmätta dimensioner hamnar inom ±10 mm marginal av manuellt uppmätt värde. Ett resultat som baseras på tester av 10 objekt med avvikande egenskaper för att utmana mätkapaciteten. Det största utmaningen som uppkommit har varit hanteringen av objekts perspektiv i förhållande till kamerorna. Det är ett av områdena som fortsatt har möjlighet att vidarutvecklas för att ytterliggare förbättra mätstationens pålitlighet.

The development of smart ID cards necessitates the development of secure methods for verifying the authenticity of these cards by organizations, businesses, and government authorities. This motivated the development of such a scanner capable of reading several ID cards from various countries. 365id presented its solution in 2016 by introducing a unique scanner that can verify the authenticity of ID cards. The verification process works by capturing three images using the scanner's three distinct techniques to validate the ID cards' hidden features. After many years of the company's success and the services it provides, including the 365id scanner, it is now the time to take the scanner a step further and improve its services. Thus, this project was founded by the 365id company to research and generate various suggestions for improving the quality of the scanner's photo environment. Either by modifying the way light is reflected inside the scanner or by modifying the parameters that control the camera's operation and investigating the possibility of replacing the camera with a better alternative. This is to increase the image quality and accuracy of the verification procedure.

The results proved that the desired effect could be achieved at the lowest costs by adjusting the camera settings and adding new materials to reduce and isolate the light's reflection inside the scanner. In comparison, the results proved that it is not feasible to replace the camera at present. Nevertheless, it is an option that may be available soon.

Background: The development of diabetes technology is rapid and requires education and resources to be successfully implemented in diabetes care management.

Method: In an observational study, we evaluated the use of advanced diabetes technology, resource utilization, and glycemic control. The study population was 725 individuals with type 1 diabetes (T1D) living in Region Halland, Sweden. The study cohort was followed for 7 years between 2013 and 2019.

Results: Children aged 0 to 17 years were associated with significantly better glucose control than young adults aged 18 to 25 years. The mean HbA1c in children and young adults was 53 mmol/mol (7.0%) compared to 61 mmol/mol (7.7%) (P <.0001), respectively. Comorbidities such as attention deficit hyperactivity disorder (ADHD), autism, and coelic disease were associated with higher HbA1c. All groups, regardless of age and comorbidity, showed a positive effect on glucose control after visiting a dietitian or psychologist. Differences were found between the age groups in terms of more use of advanced diabetes technology and more frequent visits to a physician in children compared to young adults.

Conclusions: More frequent visits to physicians, and a visit to dietitians, and psychologists were associated with improved glucose control in individuals with T1D 0 to 25 years. Increased resources, including access to more advanced technologies, may be required in young adults with T1D. © 2022 Diabetes Technology Society.

BACKGROUND: The development of diabetes technology is rapid and requires education and resources to be successfully implemented in diabetes care management.

METHOD: In an observational study, we evaluated the use of advanced diabetes technology, resource utilization, and glycemic control. The study population was 725 individuals with type 1 diabetes (T1D) living in Region Halland, Sweden. The study cohort was followed for 7 years between 2013 and 2019.

RESULTS: Children aged 0 to 17 years were associated with significantly better glucose control than young adults aged 18 to 25 years. The mean HbA1c in children and young adults was 53 mmol/mol (7.0%) compared to 61 mmol/mol (7.7%) (P < .0001), respectively. Comorbidities such as attention deficit hyperactivity disorder (ADHD), autism, and coelic disease were associated with higher HbA1c. All groups, regardless of age and comorbidity, showed a positive effect on glucose control after visiting a dietitian or psychologist. Differences were found between the age groups in terms of more use of advanced diabetes technology and more frequent visits to a physician in children compared to young adults.

CONCLUSIONS: More frequent visits to physicians, and a visit to dietitians, and psychologists were associated with improved glucose control in individuals with T1D 0 to 25 years. Increased resources, including access to more advanced technologies, may be required in young adults with T1D.

This project proposes the creation of an innovative social media platform, blending key features from other social media platforms with a unique twist. Central to this initiative is an AI-driven system designed to tailor content into specific categories for each user, addressing the challenge of navigating through the vast content on current platforms. This enhances the overall user experience by providing a more structured and engaging way to interact with social media. The approach integrates state-of-the-art OpenAI GPT models and user-centric design principles, aiming to transcend traditional boundaries in social media engagement. The expected outcome is a significant elevation in user satisfaction and the establishment of new benchmarks in digital communication. This project represents a step forward in harmonizing AI technology with user preferences to redefine digital interactions.

Detta projekt syftar till att utveckla en automatiserad bildförbehandlingskedja för att extrahera värdefull information från apparatmärken före återvinning. Det primära målet är att förbättra noggrannheten för optisk teckenigenkänning genom att hantera brusproblem med hjälp av förstärkningsinlärning och en evolutionär algoritm. Python valdes som det primära programmeringsspråket för detta projekt på grund av dess omfattande stöd för maskininlärnings- och datorseendebibliotek. Olika tekniker implementeras för att förbättra textutvinningen från etiketterna. Binary Robust Invariant Scalable Keypoints (BRISK) används för att räta ut etiketter och separera etiketten från bakgrunden. You Only Look Once version 8x (YOLOv8x) används sedan för att extrahera områden som innehåller den önskade texten. Datasetet för förstärkningsinlärningsmodellen och den genetiska algoritmen skapas genom att använda BRISK med YOLOv8x. Resultaten visade att förbehandlingen av bilder i datasetet, som tillhandahålls genom BRISK och YOLOv8x, inte påverkar noggrannheten för textutvinning, som föreslagits av förstärkningsinlärning och evolutionära algoritmer.

Artificial Intelligence (AI) has its roots in every field in present scenario. Healthcare is one of the sectors where AI is reaching considerable growth in recent years. Tremendous increase in healthcare data availability and considerable growth in big data analytic methods has paved way for success of AI in healthcare and research is being driven towards improvement in quality of service. Healthcare data is stored in the form of Electronic Health Records (EHR) which consists of temporally ordered patient information. There are many challenges with EHR data like heterogeneity, missing values, biases, noise, temporality etc. This master thesis focuses on addressing the problem of visit level irregularity which refers to irregular timing between events (patient’s visits).

Digitalisering av sjukvården är ett av de mest prioriterade området inom den medicinska utvecklingen. Inom området behandling av kroniska sår, som anses vara en dold sjukdom och är en av den mest kostsamma och olösta bördan inom sjukvården, har utvecklingen av nästa generations sårförband som ska övervaka ett kroniskt sår i realtid uppmärksammats och skapat ett stort intresse. Den mest vanliga komplikationen i ett kroniskt sår är att såret blir infekterat. Om en infektion i ett sår inte blir upptäckt och behandlat i tid kan det leda till amputering av lemmar och till och med död. Med ett real-tids övervakande förband kan en infektion upptäckas i tid men kan även förhindra onödiga byten av förband då även förbandsbyten påverkar det kroniska sårets läkning. Genom att integrera flexibla och biokompatibla sensorer i ett förband som mäter temperatur och pH-värde i såret, och sedan skicka sensor data via Bluetooth till en Android applikation där data visuellt visas, har en prototyp av en infektions detekterande enhet utvecklats. Projektet resulterade i ett system där sensorerna och en Bluetooth modul kopplades till en Arduino Nano. Sensor data skickas via Bluetooth till en Android applikation där mätvärdena visas och även en indikator som uppdateras beroende på ändringar i sårets status. Framtagningen av protypen har genererat viktiga frågeställningar att överväga när nästa generations real-tids övervakande förband ska utvecklas.

Driver monitoring is expected to contribute greatly to safety in nascent smart cities, also in complex, mixed-traffic scenarios with autonomous vehicles (AVs), vulnerable road users (VRUs), and manually driven vehicles. Until now, one focus has been on detecting bio signals during the relatively short time when a person is inside a vehicle; but, life outside of the vehicle can also affect driving. For example, loneliness, depression, and sleep-deprivation, which might be difficult to detect in time, can increase the risk of accidents–raising possibilities for new and alternative intervention strategies. Thus, the current conceptual paper explores one idea for how continuous care could be provided to improve drivers’ mental states; in particular, the idea of a “robot” that could positively affect a driver’s health through interactions supported by social media mining on Facebook. A speculative design approach is used to present some potential challenges and solutions in regard to a robot’s interaction strategy, user modeling, and ethics. For example, to address how to generate appropriate robot activities and mitigate the risk of damage to the driver, a hybrid neuro-symbolic recognition strategy leveraging stereotypical and self-disclosed information is described. Thereby, the aim of this conceptual paper is to navigate through some “memories” of one possible future, toward stimulating ideation and discussion within the increasingly vital area of safety in smart cities Copyright © 2021