Clinical risk modelling with machine learning: adverse outcomes of pregnancy

As a complex biological process, there are various health issues that are related to pregnancy. Prenatal care, a type of preventative healthcare at different points in gestation is comprised of management, treatment, and mitigation of such issues. This also includes risk prediction for adverse pregnancy outcomes, where probabilistic modelling is used to calculate individual’s risk at the early stages of pregnancy. This type of modelling can have a definite clinical scope such as in prenatal screening, and an educational aim where awareness of a healthy lifestyle is promoted, such as in health education. Currently, the most used models are based on traditional statistical approaches, as they provide sufficient predictive power and are easily interpreted by clinicians. Machine learning, a subfield of data science, contains methods for building probabilistic models with multidimensional data. Compared to existing prediction models related to prenatal care, machine learning models can provide better results by fitting more intricate nonlinear decision boundary areas, improve data-driven model fitting by generating synthetic data, and by providing more automation for routine model adjustment processes. This thesis presents the evaluation of machine learning methods to prenatal screening and health education prediction problems, along with novel methods for generating synthetic rare disorder data to be used for modelling, and an adaptive system for continuously adjusting a prediction model to the changing patient population. This way the thesis addresses all the four main entities related to predicting adverse outcomes of pregnancy: the mother or patient, the clinician, the screening laboratory and the developer or manufacturer of screening materials and systems.Kliinisen riskin mallinnus koneoppimismenetelmin: raskaudelle haitalliset lopputulemat Raskaus on kompleksinen biologinen prosessi, jonka etenemiseen liittyy useita terveysongelmia. Äitiyshoito voidaan kuvata ennalta ehkäiseväksi terveydenhuolloksi, jossa pyritään käsittelemään, hoitamaan ja lievittämään kyseisiä ongelmia. Tähän hoitoon sisältyy myös raskauden haitallisten lopputulemien riskilaskenta, missä probabilistista mallinnusta hyödynnetään määrittämään yksilön riski raskauden varhaisissa vaiheissa. Tällä mallinnuksella voi olla selkeä kliininen tarkoitus kuten prenataaliseulonta, tai terveyssivistyksellinen tarkoitus missä odottavalle äidille esitellään raskauden kannalta terveellisiä elämäntapoja. Tällä hetkellä eniten käytössä olevat ennustemallit perustuvat perinteiseen tilastolliseen mallinnukseen, sille ne tarjoavat riittävän ennustetehokkuuden ja ovat helposti tulkittavissa. Koneoppiminen on datatieteen osa-alue, joka pitää sisällään menetelmiä millä voidaan mallintaa moniulotteista dataa ennustekäyttöön. Verrattuna olemassa oleviin äitiyshoidon ennustemalleihin, koneoppiminen mahdollistaa parempien ennustetulosten tuottamisen sovittamalla hienojakoisempia epälineaarisia päätösalueita, tehostamalla datakeskeisten mallien sovitusta luomalla synteettisiä havaintoja ja tarjoamalla enemmän automaatiota rutiininomaiseen mallien hienosäätöön. Tämä väitös esittelee koneoppimismenetelmien evaluaation prenataaliseulonta-ja terveyssivistysongelmiin, ja uusia menetelmiä harvinaisten sairauksien datan luomiseen mallinnustarkoituksiin ja jatkuvan ennustemallin hienosäätämisen järjestelmän muuttuvia potilaspopulaatiota varten. Näin väitös käy läpi kaikki neljä asianomaista jotka liittyvät haitallisten lopputulemien ennustamiseen: odottava äiti eli potilas, kliinikko, seulontalaboratorio ja seulonnassa käytettävien materiaalien ja järjestelmien kehittäjä tai valmistaja

Feature Selection on Pregnancy Risk Classification Using C5.0 Method

The maternal mortality rate in Indonesia is still relatively high. This is caused by several factors, including the ignorance of pregnant women about the risk status of pregnancy. Several methods are proposed for early detection of the risk of a mother's pregnancy. However, no one has highlighted what features are most influential in the process of classifying the risk of pregnancy. In this research, we use data of pregnant women in one of the health centers in Malang, Indonesia, as a dataset. The dataset has 107 features, therefore, feature selection is needed for the classification process. We propose to use the C5.0 method to select important features while classifying dataset into low, high, and very high risk of pregnancy. C5.0 was chosen because this method has a better pruning algorithm and requires relatively smaller memory compared to C4.5. Another classification method (SVM, Naive Bayes, and Nearest Neighbor) is then used to compare the accuracy values between datasets that use all features with datasets that only use the selected features. The test results show that feature selection can increase accuracy by up to 5%

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants is most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography – EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organized network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. The proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%

Health and health systems in the Commonwealth of Independent States.

The countries of the Commonwealth of Independent States differ substantially in their post-Soviet economic development but face many of the same challenges to health and health systems. Life expectancies dropped steeply in the 1990s, and several countries have yet to recover the levels noted before the dissolution of the Soviet Union. Cardiovascular disease is a much bigger killer in the Commonwealth of Independent States than in western Europe because of hazardous alcohol consumption and high smoking rates in men, the breakdown of social safety nets, rising social inequality, and inadequate health services. These former Soviet countries have embarked on reforms to their health systems, often aiming to strengthen primary care, scale back hospital capacities, reform mechanisms for paying providers and pooling funds, and address the overall shortage of public funding for health. However, major challenges remain, such as frequent private out-of-pocket payments for health care and underdeveloped systems for improvement of quality of care

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

HERA - Environmental Risk Assessment of a contaminated estuarine environment: a case study

Sado River estuary is located in the west coast of Portugal. Previous environmental studies identified industrial contamination, non-point anthropogenic sources and contamination coming from the river, all promoting accumulation of polluted sediments with known impacts on the ecological system. Surrounding human populations have intense economic fishery activities. Together with agriculture, estuary fishing products are available to local residents. Food usage previously characterized through ethnographic studies suggests exposure to estuarine products, farming products, and water in daily activities, as potential routes of contamination. It is well established that long term exposure to heavy metals are associated with renal and neurological diseases, most heavy metals are classified as carcinogenic and teratogenic.Instituição Financiadora: FCT; Instituições participantes: IMAR -Instituto do Mar (coord.)e PRÓ-INSA, Associação para a Promoção da Investigação em Saúde, Instituto Nacional de Saúde Doutor Ricardo Jorg

Improving access to ultrasound imaging in northern, remote communities

Access to healthcare services—including access to medical imaging—is an important determinant of health outcomes. This thesis aims to improve understanding of and address gaps in access to ultrasound imaging for patients in northern, remote communities, and advance a novel ultrasound technology with the ultimate goal of improving patient care and health outcomes. This thesis first brings greater understanding of patients’ perceptions of access and factors which shape access to ultrasound imaging in northern, remote communities in Saskatchewan, Canada. A qualitative study was performed using interpretive description as a methodological approach and a multi-dimensional conceptualization of access to care as a theoretical framework. The study identified barriers which patients in northern, remote communities face in accessing ultrasound imaging, and demonstrated that geographic remoteness from imaging facilities was a central barrier. To determine whether disparities in access to ultrasound imaging resulted in disparities in utilization of ultrasound services, two population-based studies assessed the association between sociodemographic and geographic factors and obstetrical and non-obstetrical ultrasound utilization in Saskatchewan. In the first study investigating obstetrical ultrasound utilization, multivariate logistic regression analysis demonstrated that women living in rural areas, remote areas, and low income neighbourhoods, as well as status First Nations women, were less likely to have a second trimester ultrasound, an important aspect of prenatal care. In a second study investigating non-obstetrical ultrasound utilization across the entire provincial population, multivariate Poisson regression analysis similarly demonstrated lower rates of non-obstetrical ultrasound utilization among individuals living in rural and remote areas, individuals residing in low income neighbourhoods, and status First Nations persons. To address the barriers which patients in northern, remote communities face in accessing ultrasound imaging and to minimize disparities in ultrasound imaging utilization as identified in previous studies in this thesis, telerobotic ultrasound technology was investigated as a solution to improve access to ultrasound imaging. Using this technology, radiologists and sonographers could remotely manipulate an ultrasound probe via a robotic arm, thereby remotely performing an ultrasound exam while patients remained in their home community. A clinical trial comparing conventional and telerobotic ultrasound approaches was undertaken, validating this technology for obstetrical ultrasound imaging. To determine the feasibility of using telerobotic technology to establish an ultrasound service delivery model to remotely provide diagnostic ultrasound exams in underserved communities, pilot telerobotic ultrasound clinics were developed in three northern, remote communities. Telerobotic ultrasound exams were sufficient for diagnosis in the majority of cases, minimizing travel or reducing wait times for these patients. This technology was subsequently evaluated during a COVID-19 outbreak in northern Saskatchewan, demonstrating the potential of this technology to provide critical ultrasound services to an underserved northern population and minimize health inequities during the COVID-19 pandemic. An economic evaluation was performed to compare a service delivery model using telerobotic ultrasound technology to alternative service delivery models. Telerobotic ultrasound combined with an itinerant sonographer service was found to be the lowest cost option from both a publicly funded healthcare payer perspective and a societal perspective for many northern, remote communities. This thesis provides key insights for health system leaders seeking improved understanding and novel solutions to improve access to ultrasound imaging in northern, remote communities. Findings suggest that telerobotic ultrasound is a viable solution to improve access to ultrasound imaging and reduce costs associated with ultrasound service delivery. Evidence in this thesis may be used to help improve ultrasound services and health equity for patients in underserved northern, remote communities. Continued respectful collaboration with northern, remote, Indigenous peoples and communities will be a critical aspect to ensure that ultrasound services meet community needs