Machine Learning for the Early Detection of Acute Episodes in Intensive Care Units

In Intensive Care Units (ICUs), mere seconds might define whether a patient lives or dies. Predictive models capable of detecting acute events in advance may allow for anticipated interventions, which could mitigate the consequences of those events and promote a greater number of lives saved. Several predictive models developed for this purpose have failed to meet the high requirements of ICUs. This might be due to the complexity of anomaly prediction tasks, and the inefficient utilization of ICU data. Moreover, some essential intensive care demands, such as continuous monitoring, are often not considered when developing these solutions, making them unfit to real contexts. This work approaches two topics within the mentioned problem: the relevance of ICU data used to predict acute episodes and the benefits of applying Layered Learning (LL) techniques to counter the complexity of these tasks. The first topic was undertaken through a study on the relevance of information retrieved from physiological signals and clinical data for the early detection of Acute Hypotensive Episodes (AHE) in ICUs. Then, the potentialities of LL were accessed through an in-depth analysis of the applicability of a recently proposed approach on the same topic. Furthermore, different optimization strategies enabled by LL configurations were proposed, including a new approach aimed at false alarm reduction. The results regarding data relevance might contribute to a shift in paradigm in terms of information retrieved for AHE prediction. It was found that most of the information commonly used in the literature might be wrongly perceived as valuable, since only three features related to blood pressure measures presented actual distinctive traits. On another note, the different LL-based strategies developed confirm the versatile possibilities offered by this paradigm. Although these methodologies did not promote significant performance improvements in this specific context, they can be further explored and adapted to other domains.Em Unidades de Cuidados Intensivos (UCIs), meros segundos podem ser o fator determinante entre a vida e a morte de um paciente. Modelos preditivos para a previsão de eventos adversos podem promover intervenções antecipadas, com vista à mitigação das consequências destes eventos, e traduzir-se num maior número de vidas salvas. Múltiplos modelos desenvolvidos para este propósito não corresponderam às exigências das UCIs. Isto pode dever-se à complexidade de tarefas de previsão de anomalias e à ineficiência no uso da informação gerada em UCIs. Além disto, algumas necessidades inerentes à provisão de cuidados intensivos, tais como a monitorização contínua, são muitas vezes ignoradas no desenvolvimento destas soluções, tornando-as desadequadas para contextos reais. Este projeto aborda dois tópicos dentro da problemática introduzida, nomeadamente a relevância da informação usada para prever episódios agudos, e os benefícios de técnicas de Aprendizagem em Camadas (AC) para contrariar a complexidade destas tarefas. Numa primeira fase, foi conduzido um estudo sobre o impacto de diversos sinais fisiológicos e dados clínicos no contexto da previsão de episódios agudos de hipotensão. As potencialidades do paradigma de AC foram avaliadas através da análise de uma abordagem proposta recentemente para o mesmo caso de estudo. Nesta segunda fase, diversas estratégias de otimização compatíveis com configurações em camadas foram desenvolvidas, incluindo um modelo para reduzir falsos alarmes. Os resultados relativos à relevância da informação podem contribuir para uma mudança de paradigma em termos da informação usada para treinar estes modelos. A maior parte da informação poderá estar a ser erroneamente considerada como importante, uma vez que apenas três variáveis, deduzidas dos valores de pressão arterial, foram identificadas como realmente impactantes. Por outro lado, as diferentes estratégias baseadas em AC confirmaram a versatilidade oferecida por este paradigma. Apesar de não terem promovido melhorias significativas neste contexto, estes métodos podem ser adaptados a outros domínios

Dynamical probabilistic graphical models applied to physiological condition monitoring

Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task of physiological condition monitoring is decomposed into the sub-tasks of modelling a patient’s physiology a) under the effect of physiological or artifactual events and b) under the effect of drug administration. The first sub-task is concerned with modelling artifact (such as the taking of blood samples, suction events etc.), and physiological episodes (such as bradycardia), while the second sub-task is focussed on modelling the effect of drug administration on a patient’s physiology. The first contribution of this thesis is the formulation, development and validation of the Discriminative Switching Linear Dynamical System (DSLDS) for the first sub-task. The DSLDS is a discriminative model which identifies the state-of-health of a patient given their observed vital signs using a discriminative probabilistic classifier, and then infers their underlying physiological values conditioned on this status. It is demonstrated on two real-world datasets that the DSLDS is able to outperform an alternative, generative approach in most cases of interest, and that an a-mixture of the two models achieves higher performance than either of the two models separately. The second contribution of this thesis is the formulation, development and validation of the Input-Output Non-Linear Dynamical System (IO-NLDS) for the second sub-task. The IO-NLDS is a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients. More specifically, in this thesis the focus is on modelling the effect of the widely used anaesthetic drug Propofol on a patient’s monitored depth of anaesthesia and haemodynamics. A comparison of the IO-NLDS with a model derived from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature on a real-world dataset shows that significant improvements in predictive performance can be provided without requiring the incorporation of expert physiological knowledge

Adoption of Big Data and AI methods to manage medication administration and intensive care environments

Artificial Intelligence (AI) has proven to be very helpful in different areas, including the medical field. One important parameter for healthcare professionals’ decision-making process is blood pressure, specifically mean arterial pressure (MAP). The application of AI in medicine, more specifically in Intensive Care Units (ICU) has the potential to improve the efficiency of healthcare and boost telemedicine operations with access to real-time predictions from remote locations. Operations that once required the presence of a healthcare professional, can be done at a distance, which facing the recent COVID-19 pandemic, proved to be crucial. This dissertation presents a solution to develop an AI system capable of accurately predicting MAP values. Many ICU patients suffer from sepsis or septic shock, and they can be identified by the need for vasopressors, such as noradrenaline, to keep their MAP above 65 mm Hg. The presented solution facilitates early interventions, thereby minimising the risk to patients. The current study reviews various machine learning (ML) models, training them to predict MAP values. One of the challenges is to see how the different models behave during their training process and choose the most promising one to test in a controlled environment. The dataset used to train the models contains identical data to the one generated by bedside monitors, which ensures that the models’ predictions align with real-world scenarios. The medical data generated is processed by a separate component that performs data cleaning, after which is directed to the application responsible for loading, classifying the data and utilising the ML model. To increase trust between healthcare professionals and the system to be developed, it is also intended to provide insights into how the results are achieved. The solution was integrated, for validation, with one of the telemedicine hubs deployed by the European project ICU4Covid through its CPS4TIC component.A Inteligência Artificial (IA) é muito útil em diferentes áreas, incluindo a saúde. Um parâmetro importante para a tomada de decisão dos profissionais de saúde é a pressão arterial, especificamente a pressão arterial média (PAM). A aplicação da IA na medicina, mais especificamente nas Unidades de Cuidados Intensivos (UCI), tem o potencial de melhorar a eficiência dos cuidados de saúde e impulsionar operações de telemedicina com acesso a previsões em tempo real a partir de locais remotos. As operações que exigiam a presença de um profissional de saúde, podem ser feitas à distância, o que, face à recente pandemia da COVID-19, se revelou crucial. Esta dissertação apresenta como solução um sistema de IA capaz de prever valores de PAM. Muitos pacientes nas UCI sofrem de sepse ou choque séptico, e podem ser identificados pela necessidade de vasopressores, como a noradrenalina, para manter a sua PAM acima dos 65 mm Hg. A solução apresentada facilita intervenções antecipadas, minimizando o risco para doentes. O estudo atual analisa vários modelos de machine learning (ML), e treina-os para preverem valores de PAM. Um desafio é ver o desempenho dos diferentes modelos durante o seu treino, e escolher o mais promissor para testar num ambiente controlado. O dataset utilizado para treinar os modelos contém dados idênticos aos gerados por monitores de cabeceira, o que assegura que as previsões se alinhem com cenários realistas. Os dados médicos gerados são processados por um componente separado responsável pela sua limpeza e envio para a aplicação responsável pelo seu carregamento, classificação e utilização do modelo ML. Para aumentar a confiança entre os profissionais de saúde e o sistema, pretende-se também fornecer uma explicação relativa à previsão dada. A solução foi integrada, para validação, com um dos centros de telemedicina implantado pelo projeto europeu ICU4Covid através da sua componente CPS4TIC

Secondary Analysis of Electronic Health Records

Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science

Toward Precision Medicine in Intensive Care: Leveraging Electronic Health Records and Patient Similarity

The growing adoption of Electronic Health Record (EHR) systems has resulted in an unprecedented amount of data. This availability of data has also opened up the opportunity to utilize EHRs for providing more customized care for each patient by considering individual variability, which is the goal of precision medicine. In this context, patient similarity (PS) analytics have been introduced to facilitate data analysis through investigating the similarities in patients’ data, and, ultimately, to help improve the healthcare system. This dissertation is presented in six chapters and focuses on employing PS analytics in data-rich intensive care units. Chapter 1 provides a review of the literature and summarizes studies describing approaches for predicting patients’ future health status based on EHR and PS. Chapter 2 demonstrates the informativeness of missing data in patient profiles and introduces missing data indicators to use this information in mortality prediction. The results demonstrate that including indicators with observed measurements in a set of well-known prediction models (logistic regression, decision tree, and random forest) can improve the predictive accuracy. Chapter 3 builds upon the previous results and utilizes these missing indicators to reveal patient subpopulations based on their similarity in laboratory test ordering being used for them. In this chapter, the Density-based Spatial Clustering of Applications with Noise method, was employed to group the patients into clusters using the indicators generated in the previous study. Results confirmed that missing indicators capture the laboratory-test-ordering patterns that are informative and can be used to identify similar patient subpopulations. Chapter 4 investigates the performance of a multifaceted PS metric constructed by utilizing appropriate similarity metrics for specific clinical variables (e.g. vital signs, ICD-9, etc.). The proposed PS metric was evaluated in a 30-day post-discharge mortality prediction problem. Results demonstrate that PS-based prediction models with the new PS metric outperformed population-based prediction models. Moreover, the multifaceted PS metric significantly outperformed cosine and Euclidean PS metric in k-nearest neighbors setting. Chapter 5 takes the previous results into consideration and looks for potential subpopulations among septic patients. Sepsis is one of the most common causes of death in Canada. The focus of this chapter is on longitudinal EHR data which are a collection of observations of measurements made chronologically for each patient. This chapter employs Functional Principal Component Analysis to derive the dominant modes of variation in septic patients’ EHR's. Results confirm that including temporal data in the analysis can help in identifying subgroups of septic patients. Finally, Chapter 6 provides a discussion of results from previous chapters. The results indicate the informativeness of missing data and how PS can help in improving the performance of predictive modeling. Moreover, results show that utilizing the temporal information in PS calculation improves patient stratification. Finally, the discussion identifies limitations and directions for future research

Detecting hazardous intensive care patient episodes using real-time mortality models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 229-237).The modern intensive care unit (ICU) has become a complex, expensive, data-intensive environment. Caregivers maintain an overall assessment of their patients based on important observations and trends. If an advanced monitoring system could also reliably provide a systemic interpretation of a patient's observations it could help caregivers interpret these data more rapidly and perhaps more accurately. In this thesis I use retrospective analysis of mixed medical/surgical intensive care patients to develop predictive models. Logistic regression is applied to 7048 development patients with several hundred candidate variables. These candidate variables range from simple vitals to long term trends and baseline deviations. Final models are selected by backward elimination on top cross-validated variables and validated on 3018 additional patients. The real-time acuity score (RAS) that I develop demonstrates strong discrimination ability for patient mortality, with an ROC area (AUC) of 0.880. The final model includes a number of variables known to be associated with mortality, but also computationally intensive variables absent in other severity scores. In addition to RAS, I also develop secondary outcome models that perform well at predicting pressor weaning (AUC=0.825), intraaortic balloon pump removal (AUC=0.816), the onset of septic shock (AUC=0.843), and acute kidney injury (AUC=0.742). Real-time mortality prediction is a feasible way to provide continuous risk assessment for ICU patients. RAS offers similar discrimination ability when compared to models computed once per day, based on aggregate data over that day.(cont.) Moreover, RAS mortality predictions are better at discrimination than a customized SAPS II score (Day 3 AUC=0.878 vs AUC=0.849, p < 0.05). The secondary outcome models also provide interesting insights into patient responses to care and patient risk profiles. While models trained for specifically recognizing secondary outcomes consistently outperform the RAS model at their specific tasks, RAS provides useful baseline risk estimates throughout these events and in some cases offers a notable level of predictive utility.by Caleb Wayne Hug.Ph.D

Detecting Hazardous Intensive Care Patient Episodes Using Real-time Mortality Models

PhD thesisThe modern intensive care unit (ICU) has become a complex, expensive, data-intensive environment. Caregivers maintain an overall assessment of their patients based on important observations and trends. If an advanced monitoring system could also reliably provide a systemic interpretation of a patient's observations it could help caregivers interpret these data more rapidly and perhaps more accurately. In this thesis I use retrospective analysis of mixed medical/surgical intensive care patients to develop predictive models. Logistic regression is applied to 7048 development patients with several hundred candidate variables. These candidate variables range from simple vitals to long term trends and baseline deviations. Final models are selected by backward elimination on top cross-validated variables and validated on 3018 additional patients. The real-time acuity score (RAS) that I develop demonstrates strong discrimination ability for patient mortality, with an ROC area (AUC) of 0.880. The final model includes a number of variables known to be associated with mortality, but also computationally intensive variables absent in other severity scores. In addition to RAS, I also develop secondary outcome models that perform well at predicting pressor weaning (AUC=0.825), intraaortic balloon pump removal (AUC=0.816), the onset of septic shock (AUC=0.843), and acute kidney injury (AUC=0.742). Real-time mortality prediction is a feasible way to provide continuous risk assessment for ICU patients. RAS offers similar discrimination ability when compared to models computed once per day, based on aggregate data over that day. Moreover, RAS mortality predictions are better at discrimination than a customized SAPS II score (Day 3 AUC=0.878 vs AUC=0.849, p < 0.05). The secondary outcome models also provide interesting insights into patient responses to care and patient risk profiles. While models trained for specifically recognizing secondary outcomes consistently outperform the RAS model at their specific tasks, RAS provides useful baseline risk estimates throughout these events and in some cases offers a notable level of predictive utility.Ph.D. in Computer Scienc