Towards a Reference Architecture for Female-Sensitive Drug Management

Due to various biological factors, males and females differ in their response to drug treatment. However, there is still a lack of knowledge of the effects resulting from sex-differences in the medical field, especially due to the issue of underrepresentation of females in clinical studies. Considering severe diseases that are related to the cardiovascular system, which are likely to be perilous, counteracting this lack and emphasizing the need for sex-dependent drug treatment is of high importance. Thus, this research-in-progress paper aims at strengthening the female perspective in drug management by proposing design considerations on IS regarding recommender systems in healthcare for reinforcing shared decision-making and person-centered care. The resulting artefact presented will be a reference architecture with a mobile application as the interface to patients and healthcare professionals as well as a data- driven backend to collect and process data on sex specificity in the medical treatment of cardiovascular diseases (CVD)

Practical approach to designing and implementing a recommendation system for healthy challenges

Background: The COVID-19 pandemic has worsened sedentary lifestyles and unhealthy eating habits. It is crucial to promote proper training and healthy habits for all to prevent physical and cognitive decline. This should be a priority in health and education initiatives to reduce deaths and noncommunicable diseases. Guidelines for nutrition, physical activity, and sleep emphasize the importance of healthy habits. The goal is to develop a recommendation tool with a diverse range of challenges to positively impact users’ health. Methods: The process involves thoroughly obtaining precise user profiles through widely used questionnaires such as the Short-Form Health survey, the short Healthy Eating Index, and the Oviedo Sleep Questionnaire, and characterizing the challenges. Then, an algorithm will be developed to identify and prioritize the most suitable challenges for each user, ensuring personalized recommendations. Results: A pool of 30 health challenges was created based on reputable recommendations and experts. The system underwent validation by external experts and received positive user feedback, confirming its effectiveness. The panel of experts and users validated the personalized and reliable recommendations. Conclusions: Simple lifestyle interventions have shown promise for primary prevention in developed countries. A prototype system has been created to evaluate the individual weakness of users and suggest evidence-based lifestyle challenges. The system conducts a thorough health assessment and ensures feasibility for preventive purposes. Validation has proven the system’s effectiveness in recommending health-enhancing challenges with no adverse effects. The design of the model supports the seamless addition of new challenges by eventual third parties, ensuring interoperability and scalability.Agencia Estatal de Investigación | Ref. PID2020-115137RB-I00Xunta de Galicia | Ref. ED481A-2021/35

The role of technology in healthy living medicine

Health care consumers are taking control of their health information and desire a greater role in managing their health. Approximately 77% of Americans now own a smartphone and the use of health apps have doubled over the past two years. These effects are particularly notable in patients with chronic disease, now representing half the adult population and responsible for 86% of United States health care (HC) costs and 70% of deaths. New opportunities exist as a result of recent advances in home-based wireless devices, apps, wearables, and interactive systems enabling health delivery systems to monitor, advise and treat disease near real time and engage patients in healthy living medicine. These technologies will provide a new framework for patient engagement and care delivery that will enhance clinical outcomes and generate precision interventions that ultimately reduce HC costs

Designing a Mobile Recommender System for Treatment Adherence Improvement among Hypertensives

Impelling the ambulatory hypertensive patients to stick to the prescribed treatment throughout a long term is a challenging problem. To address the problem, the personal monitoring system can be used providing the possibility both to gather various health state parameters and life style-related data and to intervene in case the patient does not stick to the appointed instructions. The subsystem related to health state monitoring have been presented in our previous work. In this paper, we introduce the recommender system intended to patient's behavior correction

An intelligent recommender system based on short-term disease risk prediction for patients with chronic diseases in a telehealth environment

Clinical decisions are usually made based on the practitioners' experiences with limited support from data-centric analytic processes from medical databases. This often leads to undesirable biases, human errors and high medical costs affecting the quality of services provided to patients. Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and reducing the costs and workload involved in their daily healthcare. In the telehealth environment, patients suffering from chronic diseases such as heart disease or diabetes have to take various medical tests such as measuring blood pressure, blood sugar and blood oxygen, etc. This practice adversely affects the overall convenience and quality of their everyday living. In this PhD thesis, an effective recommender system is proposed utilizing a set of innovative disease risk prediction algorithms and models for short-term disease risk prediction to provide chronic disease patients with appropriate recommendations regarding the need to take a medical test on the coming day. The input sequence of sliding windows based on the patient's time series data, is analyzed in both the time domain and the frequency domain. The time series medical data obtained for each chronicle disease patient is partitioned into consecutive sliding windows for analysis in both the time and the frequency domains. The available time series data are readily available in time domains which can be used for analysis without any further conversion. For data analysis in the frequency domain, Fast Fourier Transformation (FFT) and Dual-Tree Complex Wavelet Transformation (DTCWT) are applied to convert the data into the frequency domain and extract the frequency information. In the time domain, four innovative predictive algorithms, Basic Heuristic Algorithm (BHA), Regression-Based Algorithm (RBA) and Hybrid Algorithm (HA) as well as a structural graph-based method (SG), are proposed to study the time series data for producing recommendations. While, in the frequency domain, three predictive classifiers, Artificial Neural Network, Least Squares-Support Vector Machine, and Naïve Bayes, are used to produce the recommendations. An ensemble machine learning model is utilized to combine all the used predictive models and algorithms in both the time and frequency domains to produce the final recommendation. Two real-life telehealth datasets collected from chronic disease patients (i.e., heart disease and diabetes patients) are utilized for a comprehensive experimental evaluation in this study. The results show that the proposed system is effective in analysing time series medical data and providing accurate and reliable (very low risk) recommendations to patients suffering from chronic diseases such as heart disease and diabetes. This research work will help provide high-quality evidence-based intelligent decision support to clinical disease patients that significantly reduces workload associated with medical checkups would otherwise have to be conducted every day in a telehealth environment

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management

Uncovering Bias in Personal Informatics

Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. %Despite their widespread usage, the processing of particularly sensitive personal data, and their proximity to domains known to be susceptible to bias, such as healthcare, bias in PI has not been investigated systematically. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed

Semantic web system for differential diagnosis recommendations

There is a growing realization that healthcare is a knowledge-intensive field. The ability to capture and leverage semantics via inference or query processing is crucial for supporting the various required processes in both primary (e.g. disease diagnosis) and long term care (e.g. predictive and preventive diagnosis). Given the wide canvas and the relatively frequent knowledge changes that occur in this area, we need to take advantage of the new trends in Semantic Web technologies. In particular, the power of ontologies allows us to share medical research and provide suitable support to physician's practices. There is also a need to integrate these technologies within the currently used healthcare practices. In particular the use of semantic web technologies is highly demanded within the clinicians' differential diagnosis process and the clinical pathways disease management procedures as well as to aid the predictive/preventative measures used by healthcare professionals

Nutritional management and recommendations for hospital users and medical inpatients

Dissertação de mestrado em Engenharia InformáticaNutrition is fundamental to human well-being and health, especially when applied to patients who need special health care. In these cases, it is crucial that each patient has adequate nutrition to meet their needs, in order to accelerate their recovery process. Recommender systems make it possible to offer suggestions to users, adapted to their preferences and to previously obtained information about them. Food recommender systems are recommender systems applied to nutrition and diet. They are usually implemented feeding plans recommendation platforms based on food and the person using it. In this sense, the existing gap in the use of these recommendation systems applied to nutrition in health care is notorious. This is mainly due to the difficulty in associating the nutritional value of each food with the needs of patients. The main objective of this project is to fill the existing void, through the development and implementation of a platform that will allow the planning of meals taking into account the nutritional plan of the food and the specific needs associated with the users of the Vila Verde Social Canteen. The use of machine learning algorithms will allow us to identify how the connection between food and patient requirements can be made, making this task possible, which is complex due to the wide domain associated with it. This platform will be used for the generation of kitchen meal plans, which shall be produced using the algorithms developed after a bibliographic study and an investigation of the existing work, in order to understand how they can be implemented and which are the most adequate to the nutritional recommendations system.A nutrição é fundamental no bem-estar e na saúde do ser humano, principalmente quando aplicada a pacientes que necessitam de cuidados de saúde especiais. Nestes casos, é fulcral que cada paciente tenha uma nutrição adequada às suas necessidades, de forma a acelerar o seu processo de recuperação. Os sistemas de recomendação permitem oferecer sugestões aos utilizadores, adequados às suas preferências e às informações previamente obtidas acerca dos mesmos. Os sis-temas de recomendação de alimentos são sistemas de recomendação aplicados à nutrição e alimentação. Estes são usualmente implementados em plataformas de recomendações de receitas e planos de alimentação tendo como base a comida e a pessoa. Neste sentido, é notória a falha atual no que diz respeito à utilização destes sistemas de recomendação aplicados à nutrição em cuidados de saúde. Isto deve-se maioritariamente à dificuldade na associação entre o valor nutricional de cada alimento e as necessidades dos pacientes. Este projeto tem como principal objetivo preencher a lacuna existente, através do desen-volvimento e implementação de uma plataforma que irá permitir o planeamento de refeições tendo em conta o plano nutricional dos alimentos e as necessidades específicas associadas aos utentes da Cantina Social de Vila Verde. A utilização de algoritmos de machine learning permitirá perceber como pode ser feita a conexão entre os alimentos e os requisitos dos pacientes, tornando possível esta tarefa, que é complexa devido ao largo domínio associado à mesma. Esta plataforma será utilizada para a geração de planos de refeições da cozinha, sendo estes produzidos utilizando os algoritmos desenvolvidos após um estudo bibliográfico e uma investigação ao trabalho existente com o objetivo de perceber como poderão ser implementados e quais os mais adequados ao sistema de recomendações nutricional