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

Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study

BackgroundAcute neurological injury is a leading cause of permanent disability and death in the pediatric intensive care unit (PICU). No predictive model has been validated for critically ill children with acute neurological injury.ObjectivesWe hypothesized that PICU patients with concern for acute neurological injury are at higher risk for morbidity and mortality, and advanced analytics would derive robust, explainable subgroup models.MethodsWe performed a secondary subgroup analysis of the Trichotomous Outcomes in Pediatric Critical Care (TOPICC) study (2011–2013), predicting mortality and morbidity from admission physiology (lab values and vital signs in 6 h surrounding admission). We analyzed patients with suspected acute neurological injury using standard machine learning algorithms. Feature importance was analyzed using SHapley Additive exPlanations (SHAP). We created a Fast Healthcare Interoperability Resources (FHIR) application to demonstrate potential for interoperability using pragmatic data.Results1,860 patients had suspected acute neurological injury at PICU admission, with higher morbidity (8.2 vs. 3.4%) and mortality (6.2 vs. 1.9%) than those without similar concern. The ensemble regressor (containing Random Forest, Gradient Boosting, and Support Vector Machine learners) produced the best model, with Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.91 [95% CI (0.88, 0.94)] and Average Precision (AP) of 0.59 [0.51, 0.69] for mortality, and decreased performance predicting simultaneous mortality and morbidity (0.83 [0.80, 0.86] and 0.59 [0.51, 0.64]); at a set specificity of 0.995, positive predictive value (PPV) was 0.79 for mortality, and 0.88 for mortality and morbidity. By comparison, for mortality, the TOPICC logistic regression had AUROC of 0.90 [0.84, 0.93], but substantially inferior AP of 0.49 [0.35, 0.56] and PPV of 0.60 at specificity 0.995. Feature importance analysis showed that pupillary non-reactivity, Glasgow Coma Scale, and temperature were the most contributory vital signs, and acidosis and coagulopathy the most important laboratory values. The FHIR application provided a simulated demonstration of real-time health record query and model deployment.ConclusionsPICU patients with suspected acute neurological injury have higher mortality and morbidity. Our machine learning approach independently identified previously-known causes of secondary brain injury. Advanced modeling achieves improved positive predictive value in this important population compared to published models, providing a stepping stone in the path to deploying explainable models as interoperable bedside decision-support tools

GENERALIZABLE MODELS FOR PREDICTION OF PHYSIOLOGICAL DECOMPENSATION FROM MULTIVARIATE AND MULTISCALE PHYSIOLOGICAL TIME SERIES USING DEEP LEARNING AND TRANSFER LEARNING TECHNIQUES

The goal of this thesis is to develop generalizable machine learning models for early prediction of physiological decomposition from multivariate and multiscale physiological time series data. A combination of recent advances in machine learning and the increased availability of more granular physiological time series data (due to increased adoption of electronic medical records in US hospitals) has encouraged the development of more accurate prediction models for the critically ill patients. One such physiological decompensation prediction task we consider in our work is the early prediction of onset of sepsis. Sepsis is a syndromic, life-threatening condition that arises when the body's response to infection injures its own internal organs. While there are effective protocols for treating sepsis (e.g. administration of broad-spectrum antibiotics, Intravenous fluids, and vasopressors) once it has been diagnosed, there still exists challenges in reliably identifying septic patients early in their course. The purpose of this work is to explore the feasibility of utilizing low-resolution electronic medical record data and high-resolution physiological time series data to develop accurate prediction models for onset of sepsis in critically ill patients. To achieve this objective - We first investigate the connection between heart rate (HR) and blood pressure (MAP) time series - as captured through quantification of the structure of their corresponding network representation - for early signs of sepsis. We will then explore the utility of recurrent neural network models for accurate prediction of onset of sepsis. Finally, we combine ideas from adversarial domain adaptation, representation learning and conformal prediction to develop a generalizable prediction model that can adapt well to new target populations (without the requirement of obtaining gold-standard labels).Ph.D

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe