On the intelligent management of sepsis in the intensive care unit

The management of the Intensive Care Unit (ICU) in a hospital has its own, very specific requirements that involve, amongst others, issues of risk-adjusted mortality and average length of stay; nurse turnover and communication with physicians; technical quality of care; the ability to meet patient's family needs; and avoid medical error due rapidly changing circumstances and work overload. In the end, good ICU management should lead to an improvement in patient outcomes. Decision making at the ICU environment is a real-time challenge that works according to very tight guidelines, which relate to often complex and sensitive research ethics issues. Clinicians in this context must act upon as much available information as possible, and could therefore, in general, benefit from at least partially automated computer-based decision support based on qualitative and quantitative information. Those taking executive decisions at ICUs will require methods that are not only reliable, but also, and this is a key issue, readily interpretable. Otherwise, any decision tool, regardless its sophistication and accuracy, risks being rendered useless. This thesis addresses this through the design and development of computer based decision making tools to assist clinicians at the ICU. It focuses on one of the main problems that they must face: the management of the Sepsis pathology. Sepsis is one of the main causes of death for non-coronary ICU patients. Its mortality rate can reach almost up to one out of two patients for septic shock, its most acute manifestation. It is a transversal condition affecting people of all ages. Surprisingly, its definition has only been standardized two decades ago as a systemic inflammatory response syndrome with confirmed infection. The research reported in this document deals with the problem of Sepsis data analysis in general and, more specifically, with the problem of survival prediction for patients affected with Severe Sepsis. The tools at the core of the investigated data analysis procedures stem from the fields of multivariate and algebraic statistics, algebraic geometry, machine learning and computational intelligence. Beyond data analysis itself, the current thesis makes contributions from a clinical point of view, as it provides substantial evidence to the debate about the impact of the preadmission use of statin drugs in the ICU outcome. It also sheds light into the dependence between Septic Shock and Multi Organic Dysfunction Syndrome. Moreover, it defines a latent set of Sepsis descriptors to be used as prognostic factors for the prediction of mortality and achieves an improvement on predictive capability over indicators currently in use.La gestió d'una Unitat de Cures Intensives (UCI) hospitalària presenta uns requisits força específics incloent, entre altres, la disminució de la taxa de mortalitat, la durada de l'ingrès, la rotació d'infermeres i la comunicació entre metges amb al finalitad de donar una atenció de qualitat atenent als requisits tant dels malalts com dels familiars. També és força important controlar i minimitzar els error mèdics deguts a canvis sobtats i a la presa ràpida de deicisions assistencials. Al cap i a la fi, la bona gestió de la UCI hauria de resultar en una reducció de la mortalitat i durada d'estada. La presa de decisions en un entorn de crítics suposa un repte de presa de decisions en temps real d'acord a unes guies clíniques molt restrictives i que, pel que fa a la recerca, poden resultar en problemes ètics força sensibles i complexos. Per tant, el personal sanitari que ha de prendre decisions sobre la gestió de malalts crítics no només requereix eines de suport a la decisió que siguin fiables sinó que, a més a més, han de ser interpretables. Altrament qualsevol eina de decisió que no presenti aquests trets no és considerarà d'utilitat clínica. Aquesta tesi doctoral adreça aquests requisits mitjançant el desenvolupament d'eines de suport a la decisió per als intensivistes i es focalitza en un dels principals problemes als que s'han denfrontar: el maneig del malalt sèptic. La Sèpsia és una de les principals causes de mortalitats a les UCIS no-coronàries i la seva taxa de mortalitat pot arribar fins a la meitat dels malalts amb xoc sèptic, la seva manifestació més severa. La Sèpsia és un síndrome transversal, que afecta a persones de totes les edats. Sorprenentment, la seva definició ha estat estandaritzada, fa només vint anys, com a la resposta inflamatòria sistèmica a una infecció corfimada. La recerca presentada en aquest document fa referència a l'anàlisi de dades de la Sèpsia en general i, de forma més específica, al problema de la predicció de la supervivència de malalts afectats amb Sèpsia Greu. Les eines i mètodes que formen la clau de bòveda d'aquest treball provenen de diversos camps com l'estadística multivariant i algebràica, geometria algebraica, aprenentatge automàtic i inteligència computacional. Més enllà de l'anàlisi per-se, aquesta tesi també presenta una contribució des de el punt de vista clínic atès que presenta evidència substancial en el debat sobre l'impacte de l'administració d'estatines previ a l'ingrès a la UCI en els malalts sèptics. També s'aclareix la forta dependència entre el xoc sèptic i el Síndrome de Disfunció Multiorgànica. Finalment, també es defineix un conjunt de descriptors latents de la Sèpsia com a factors de pronòstic per a la predicció de la mortalitat, que millora sobre els mètodes actualment més utilitzats en la UCI

Blood pressure assessment with differential pulse transit time and deep learning: a proof of concept

Modern clinical environments are laden with technology devices continuously gathering physiological data from patients. This is especially true in critical care environments, where life-saving decisions may have to be made on the basis of signals from monitoring devices. Hemodynamic monitoring is essential in dialysis, surgery, and in critically ill patients. For the most severe patients, blood pressure is normally assessed through a catheter, which is an invasive procedure that may result in adverse effects. Blood pressure can also be monitored noninvasively through different methods and these data can be used for the continuous assessment of pressure using machine learning methods. Previous studies have found pulse transit time to be related to blood pressure. In this short paper, we propose to study the feasibility of implementing a data-driven model based on restricted Boltzmann machine artificial neural networks, delivering a first proof of concept for the validity and viability of a method for blood pressure prediction based on these models.Peer ReviewedPostprint (author's final draft

Enabling interpretation of the outcome of a human obesity prediction machine learning analysis from genomic data

In this brief paper, we address the medical problem of human obesity prediction from genomic data. Genomic datasets may contain a huge number of features and they often have to be analyzed within the realm of Big Data technologies. As a medical problem, obesity prediction would welcome interpretables outcomes. Therefore, the analyst would benefit from appraches in which the problem of very high data dimensionality could be eased as much as possible. Feature selection can be an essential part of such approaches. In this context, though, traditional machine learning methods may struggle. Here, we propose a pipeline to address this problem using partitioning strategies: both vertical, by dividing the data based on gender, and horizontal, by splitting each of the analyzed chromosomes into 5,000-instances subsets. For each, Minimum Redundancy and Maximum Relevance feature selection is used to find rankings of the single nucleotide polymorphisms most relevant for classification in the medical dataset.Preprin

HDL: A molecular view of the "classic" antiatherogenic lipoprotein

L'estudi del metabolisme de les HDL (lipoproteïnes de densitat alta) i la seva relació amb l'arteriosclerosi és un tema complex, en el qual, recentment, s'han fet avenços importants. Primer, s'ha demostrat que les HDL tenen una acció antiaterogènica. Segon, s'ha establert que aquesta acció és deguda a fraccions que tenen apoA-I però no apoA-II. Tercer, s'han definit alguns mecanismes clau en l'acció antiaterogènica de les HDL: transport invers de colesterol i prevenció de la modificació oxidativa de les LDL (lipoproteïnes de densitat baixa). És previsible que aquests avenços permetran el desenvolupament d'estratègies efectives, que complementaran les existents per al tractament i la prevenció de les malalties cardiovasculars aterotrombòtiques.HDL metabolism and its relationship to atherosclerosis is a complex topic, though notable advances have been made. First, HDL clearly has an anti-atherogenic action. Second, this action is due to some fraction/s containing apoA-I but not apoA-II. Third, some of the molecular mechanisms involved in two key anti-atherogenic functions of HDL have been defined: reverse cholesterol transport and prevention of LDL oxidative modification. These advances may permit the development of effective strategies, complementing those already available, for treating and preventing atherothrombotic cardiovascular diseases

Proyecto piloto de establecimiento de parcela de resinación de Pinus halepensis con objetivo múltiple en Sant Josep de Sa Talaia, Eivissa (Illes Balears)

[ES] El aprovechamiento de los terrenos forestales en la isla de Ibiza fue importante en el pasado. Hoy en día, debido a una fuerte transformación hacia el sector terciario con la llegada del turismo, dicha actividad ha sufrido un importante abandono. La consecuencia directa de ello es la pérdida del acervo cultural etnológico vinculado al mundo rural ibicenco, además del aumento del riesgo de incendios forestales, poniendo en riesgo al medio ambiente y a las personas. El presente proyecto (con memoria y anejos, planos, pliego de condiciones y presupuesto) propone el establecimiento de una parcela que combine la recuperación y muestra de tradiciones rurales con la prevención de incendios. Para ello, se ha localizado y analizado una zona de pinar de 7 ha y se han propuesto los tratamientos selvícolas adecuados (desbroce y clara) en base a su previa caracterización. Con la ejecución de los trabajos selvícolas proyectados se pretende convertir la masa en un punto de anclaje o zona segura en la defensa contra incendios forestales, además de servir de parcela piloto para la resinación del pino carrasco (Pinus halepensis Mill.) tanto por metodología tradicional ibicenca como por métodos más modernos, y así tener una primera estimación de rendimientos resineros en esta especie de pino. Además, para la recuperación de las tradiciones se proyecta un recorrido etnológico en el que a lo largo de sus 360 metros mostrará, mediante 8 paneles, la tradición resinera ibicenca y la construcción de pedra en sec de los márgenes de bancales, cuya restauración está también contemplada en el proyecto mediante la reposición de 585 m2 de muro. El presupuesto es de 59.227,08 € y de financiación privada.[EN] In the past, forests in Ibiza played a relevant role. However, in recent times, due to a huge increase of the third sector, especially the tourism industry, the management of these forests and rural areas has importantly decreased. Consequently, traditions and rural knowledge attached to them have gradually been lost, followed by an increasing risk of forest fires; which endangers not only the environment but also the population of the island. This project, which consists in memory and supplements, plans, conditions document and budget, proposes the establishment of a plot of land that combines the recovery of different antique rural traditions in Ibiza with wild forest fires prevention. To do so, a 7 ha Aleppo pine (Pinus halepensis Mill.) zone has been localised and studied. Based on its characterization, o proposal of the silvicultural treatments required to prepare the wood to become a safety zone in forest fires defence (brushing out and thinning) has been suggested. Furthermore, these treatments will prepare the forest tor the extraction of resin from its pines both in the traditional ibicenco way, and at the same time allowing modern extraction methods in order to have a first estimation of resin production in Aleppo pine. To fulfil the objective of recovering the cultural heritage in Ibiza, a 360 meters ethological path has been projected, equipped with 8 panels aimed at explaining these traditions. In addition, 585 m2 oftypical terrace wallsthat exist in the zone will be restored. The cost is expected to be 59.227,08 € and would be covered by private funding.Ribas Costa, VA. (2019). Proyecto piloto de establecimiento de parcela de resinación de Pinus halepensis con objetivo múltiple en Sant Josep de Sa Talaia, Eivissa (Illes Balears). http://hdl.handle.net/10251/123642TFG

Class imbalance impact on the prediction of complications during home hospitalization: a comparative study.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksHome hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving patient's quality of life, the lack of continuous observation at home may lead to complications in some patients. Since blood tests have been proven to provide relevant prognosis information in many diseases, this paper analyzes the impact of different sampling methods on the prediction of HH outcomes. After a first exploratory analysis, some variables extracted from routine blood tests performed at the moment of HH admission, such as hemoglobin, lymphocytes or creatinine, were found to unmask statistically significant differences between patients undergoing successful and unsucessful HH stays. Then, predictive models were built with these data, in order to identify unsuccessful cases eventually needing hospital facilities. However, since these hospital admissions during HH programs are rare, their identification through conventional machine-learning approaches is challenging. Thus, several sampling strategies designed to face class imbalance were herein overviewed and compared. Among the analyzed approaches, over-sampling strategies, such as ROSE (Random Over-Sampling Examples) and conventional random over-sampling, showed the best performances. Nevertheless, further improvements should be proposed in the future so as to better identify those patients not benefiting from HHPeer ReviewedPostprint (author's final draft

Assessment of electrocardiograms with pretraining and shallow networks

Objective: Clinical Decision Support Systems normally resort to annotated signals for the automatic assessment of ECG signals. In this paper we put forward a new method for the assessment of normal/abnormal heart function from raw ECG signals (i.e. signals without annotation) based on shallow neural networks with pretraining. Methodology: this paper resorts to a prospective clinical study that took place at Hospital Cll´inic in Barcelona, Spain. This study took place in 2010-2012 and recruited 1390 patients. For each patient we recorded a 12-lead ECG and diagnosis was conducted by the Cardiology service at the same hospital. Two datasets were produced, the first contained the automatically annotated version of all input signals and the second contained the raw signals obtained from the ECG. Results: The new method was tested through crossvalidation with a cohort of 200 test patients. Performance was compared for both annotated and raw datasets. For the annotated dataset and a shallow network with pretraining we obtained an accuracy of 0.8639, a sensitivity of 0.9560 and specificity of 0.7143. The raw dataset yielded an accuracy of 0.8426, a sensitivity of 0.8977 and a specificity of 0.7785. Conclusion: Shallow networks with pretraining automatically obtain a representation of the input data without resorting to any annotation and thus simplify the process of assessing normality of ECG signals. Despite the fact that sensitivity has decreased, accuracy is not much lower than that obtained with standard methods. Specificity is improved with the new method. These results open up a promising line of research for the automatic assessment of ECG signals.Peer ReviewedPostprint (published version

A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients

In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a common clinical syndrome, often treated at the Intensive Care Unit (ICU) in a time-critical context. Mortality prediction results with Support Vector Machines using QBK compare favorably with those obtained using alternative kernels and standard clinical procedures.Postprint (published version