Intelligent nutrition in healthcare and continuous care

In the healthcare industry, the patient's nutrition is a key factor in their treatment process. Every user has their own specific nutritional needs and requirements. An appropriate nutrition policy can therefore help the patient's recovery process and alleviate possible symptoms. Food recommender systems are platforms that offer personalised suggestions of recipes to users. However, there is a lack of usage of recipe recommendation systems in the healthcare sector. Multiple challenges in representing the domain of food and the patient's needs make it complicated to implement these systems. The present project aims to develop a platform for an intelligent planning of the user's meals, based on their clinical conditions. The application of machine learning algorithms on nutrition, in healthcare services and continuous care is thus a key topic of research. This platform will be tested and deployed at the Social Cafeteria of Vila Verde (Cantina Social da Santa Casa da Misericórdia de Vila Verde). The development of this project will use the Design Science Research (DSR) investigation methodology, ensuring that the solution to the problem accomplishes all needs and requirements of the professionals, while elucidating new knowledge both for the institution and the scientific community.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2019

Project of a conceptual design in BIM of a hospital

Differences in climate, habits and diseases in different parts of the world create a great variability of care needs that the population needs to be covered. The most important of these is health. The aim of this project is to study the rooms needed in two specific units of a hospital, the surgical and the emergency unit, and to relate them to the population, in order to obtain an algorithm that quantifies and sizes them according to the population. Based on this algorithm, it is wanted to visualise the data dynamically and dump it in Revit to obtain a BIM model that is capable of varying the distributions depending on the selected inputs. This project begins with the study of the care units of a hospital and then focuses on the functional plan of the surgical and the emergency unit. Based on the functional plan, the relationship between the inputs and the number and dimensions of the rooms in both units is studied. From these relationships, an algorithm is created and written in Python in order to calculate the results based on the data entered and to visualise them in a web environment. Finally, the formulation of coordinates that can locate each of the rooms in a plan according to the inputs, without overlapping one over the other, is studied and the data is dumped into Dynamo for its design in Revit. This study demonstrates the variability in the dimensions and number of rooms needed in a hospital to serve a specific population, by number of people and by location. In the case of this project, the regulations were studied and applied at a state level, but the surgical and emergency unit attendance is different in each autonomous community, which is reason enough to present the possibility of making this study more generic and with more variables so that it can be used in any part of the world

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

PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

How the presentation of patient information and decision-support advisories influences opioid prescribing behavior: A simulation study

ObjectiveThe United States faces an opioid crisis. Integrating prescription drug monitoring programs into electronic health records offers promise to improve opioid prescribing practices. This study aimed to evaluate 2 different user interface designs for prescription drug monitoring program and electronic health record integration.Materials and MethodsTwenty-four resident physicians participated in a randomized controlled experiment using 4 simulated patient cases. In the conventional condition, prescription opioid histories were presented in tabular format, and computerized clinical decision support (CDS) was provided via interruptive modal dialogs (ie, pop-ups). The alternative condition featured a graphical opioid history, a cue to visit that history, and noninterruptive CDS. Two attending pain specialists judged prescription appropriateness.ResultsParticipants in the alternative condition wrote more appropriate prescriptions. When asked after the experiment, most participants stated that they preferred the alternative design to the conventional design.ConclusionsHow patient information and CDS are presented appears to have a significant influence on opioid prescribing behavior

Integrating Wearable Devices and Recommendation System: Towards a Next Generation Healthcare Service Delivery

Researchers have identified lifestyle diseases as a major threat to human civilization. These diseases gradually progress without giving any warning and result in a sudden health aggravation that leads to a medical emergency. As such, individuals can only avoid the life-threatening condition if they regularly monitor their health status. Health recommendation systems allow users to continuously monitor their health and deliver proper health advice to them. Also, continuous health monitoring depends on the real-time data exchange between health solution providers and users. In this regard, healthcare providers have begun to use wearable devices and recommendation systems to collect data in real time and to manage health conditions based on the generated data. However, we lack literature that has examined how individuals use wearable devices, what type of data the devices collect, and how providers use the data for delivering solutions to users. Thus, we decided to explore the available literature in this domain to understand how wearable devices can provide solutions to consumers. We also extended our focus to cover current health service delivery frameworks with the help of recommender systems. Thus, this study reviews health-monitoring services by conglomerating both wearable device and recommendation system to come up with personalized health and fitness solutions. Additionally, the paper elucidates key components of an advanced-level real-time monitoring service framework to guide future research and practice in this domain

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given