Deep learning for precision medicine

As a result of the recent trend towards digitization, an increasing amount of information is recorded in clinics and hospitals, and this increasingly overwhelms the human decision maker. This issue is one of the main reasons why Machine Learning (ML) is gaining attention in the medical domain, since ML algorithms can make use of all the available information to predict the most likely future events that will occur to each individual patient. Physicians can include these predictions in their decision processes which can lead to improved outcomes. Eventually ML can also be the basis for a decision support system that provides personalized recommendations for each individual patient. It is also worth noticing that medical datasets are becoming both longer (i.e. we have more samples collected through time) and wider (i.e. we store more variables). There- fore we need to use ML algorithms capable of modelling complex relationships among a big number of time-evolving variables. A kind of models that can capture very complex relationships are Deep Neural Networks, which have proven to be successful in other areas of ML, like for example Language Modelling, which is a use case that has some some similarities with the medical use case. However, the medical domain has a set of characteristics that make it an almost unique scenario: multiple events can occur at the same time, there are multiple sequences (i.e. multiple patients), each sequence has an associated set of static variables, both inputs and outputs can be a combination of different data types, etc. For these reasons we need to develop approaches specifically designed for the medical use case. In this work we design and develop different kind of models based on Neural Networks that are suitable for modelling medical datasets. Besides, we tackle different medical tasks and datasets, showing which models work best in each case. The first dataset we use is one collected from patients that suffered from kidney failure. The data was collected in the Charité hospital in Berlin and it is the largest data collection of its kind in Europe. Once the kidney has failed, patients face a lifelong treatment and periodic visits to the clinic for the rest of their lives. Until the hospital finds a new kidney for the patient, he or she must attend to the clinic multiple times per week in order to receive dialysis, which is a treatment that replaces many of the functions of the kidney. After the transplant has been performed, the patient receives immunosuppressive therapy to avoid the rejection of the transplanted kidney. Patients must be periodically controlled to check the status of the kidney, adjust the treatment and take care of associated diseases, such as those that arise due to the immunosuppressive therapy. This dataset started being recorded more than 30 years ago and it is composed of more than 4000 patients that underwent a renal transplantation or are waiting for it. The database has been the basis for many studies in the past. Our first goal with the nephrology dataset is to develop a system to predict the next events that will be recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. Specifically, we model three aspects of the patient evolution: medication prescriptions, laboratory tests ordered and laboratory test results. Besides, there are a set of endpoints that can happen after a transplantation and it would be very valuable for the physicians to be able to know beforehand when one of these is going to happen. Specifically, we also predict whether the patient will die, the transplant will be rejected, or the transplant will be lost. For each visit that a patient makes to the clinic, we anticipate which of those three events (if any) will occur both within 6 months and 12 months after the visit. The second dataset that we use in this thesis is the one collected by the MEmind Wellness Tracker, which contains information related to psychiatric patients. Suicide is the second leading cause of death in the 15-29 years age group, and its prevention is one of the top public health priorities. Traditionally, psychiatric patients have been assessed by self-reports, but these su↵er from recall bias. To improve data quantity and quality, the MEmind Wellness Tracker provides a mobile application that enables patients to send daily reports about their status. Thus, this application enables physicians to get information about patients in their natural environments. Therefore this dataset contains sequential information generated by the MEmind application, sequential information generated during medical visits and static information of each patient. Our goal with this dataset is to predict the suicidal ideation value that each patient will report next. In order to model both datasets, we have developed a set of predictive Machine Learning models based on Neural Networks capable of integrating multiple sequences of data withthe background information of each patient. We compare the performance achieved by these approaches with the ones obtained with classical ML algorithms. For the task of predicting the next events that will be observed in the nephrology dataset, we obtained the best performance with a Feedforward Neural Network containing a representation layer. On the other hand, for the tasks of endpoint prediction in nephrology patients and the task of suicidal ideation prediction, we obtained the best performance with a model that combines a Feedforward Neural Network with one or multiple Recurrent Neural Networks (RNNs) using Gated Recurrent Units. We hypothesize that this kind of models that include RNNs provide the best performance when the dataset contains long-term dependencies. To our knowledge, our work is the first one that develops these kind of deep networks that combine both static and several sources of dynamic information. These models can be useful in many other medical datasets and even in datasets within other domains. We show some examples where our approach is successfully applied to non-medical datasets that also present multiple variables evolving in time. Besides, we installed the endpoints prediction model as a standalone system in the Charit ́e hospital in Berlin. For this purpose, we developed a web based user interface that the physicians can use, and an API interface that can be used to connect our predictive system with other IT systems in the hospital. These systems can be seen as a recommender system, however they do not necessarily generate valid prescriptions. For example, for certain patient, a system can predict very high probabilities for all antibiotics in the dataset. Obviously, this patient should not take all antibiotics, but only one of them. Therefore, we need a human decision maker on top of our recommender system. In order to model this decision process, we used an architecture based on a Generative Adversarial Network (GAN). GANs are systems based on Neural Networks that make better generative models than regular Neural Networks. Thus we trained one GAN that works on top of a regular Neural Network and show how the quality of the prescriptions gets improved. We run this experiment with a synthetic dataset that we created for this purpose. The architectures that we developed, are specially designed for modelling medical data, but they can be also useful in other use cases. We run experiments showing how we train them for modelling the readings of a sensor network and also to train a movie recommendation engine

Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks

In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events. We work with a database collected in the Charit\'{e} Hospital in Berlin that contains complete information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, with a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task

Impacto del cambio en la actividad física en diferentes indicadores de resultados en pacientes con enfermedad pulmonar obstructiva crónica (EPOC)

123 p.Los objetivos generales de la tesis se centran en tres aspectos: 1º Determinar el impacto del cambio en el nivel de actividad física en la calidad de vida relacionada con la salud en una corte de pacientes con EPOC. 2º Determinar el impacto del cambio de la actividad física en la frecuencia de hospitalizaciones por exacerbación de la EPOC en una cohorte de pacientes con EPOC. 3º Determinar el impacto de la actividad física en la mortalidad en el curso de una exacerbación moderada severa en pacientes con EPOC.Para ello hemos utilizado diferentes cohortes de pacientes con EPOC. Dos de ellas con pacientes en fase de estabilidad clínica y una en fase de agudización de la enfermedad.Los resultados han sido extraídos de tres trabajos publicados en revistas de neumología.Las conclusiones de estos estudios demuestran una asociación entre el cambio en el nivel de actividad física en pacientes con EPOC en fase de estabilidad clínica y dos resultados de salud importantes en la EPOC como son la calidad de vida relacionada con las salud y las hospitalizaciones. También se demostró asociación entre el cambio del nivel de actividad física durante un episodio de exacerbación moderada-severa de la EPOC y la mortalidad al cabo de un año

Impacto del cambio en la actividad física en diferentes indicadores de resultados en pacientes con enfermedad pulmonar obstructiva crónica (EPOC)

123 p.Los objetivos generales de la tesis se centran en tres aspectos: 1º Determinar el impacto del cambio en el nivel de actividad física en la calidad de vida relacionada con la salud en una corte de pacientes con EPOC. 2º Determinar el impacto del cambio de la actividad física en la frecuencia de hospitalizaciones por exacerbación de la EPOC en una cohorte de pacientes con EPOC. 3º Determinar el impacto de la actividad física en la mortalidad en el curso de una exacerbación moderada severa en pacientes con EPOC.Para ello hemos utilizado diferentes cohortes de pacientes con EPOC. Dos de ellas con pacientes en fase de estabilidad clínica y una en fase de agudización de la enfermedad.Los resultados han sido extraídos de tres trabajos publicados en revistas de neumología.Las conclusiones de estos estudios demuestran una asociación entre el cambio en el nivel de actividad física en pacientes con EPOC en fase de estabilidad clínica y dos resultados de salud importantes en la EPOC como son la calidad de vida relacionada con las salud y las hospitalizaciones. También se demostró asociación entre el cambio del nivel de actividad física durante un episodio de exacerbación moderada-severa de la EPOC y la mortalidad al cabo de un año

Microplastics in Aquatic Environments and Their Toxicological Implications for Fish

The intensive use of plastics and derivatives during the last century has increased the contamination of animal habitats. The breakdown of these primary plastics in the environment results in microplastics (MP), small fragments of plastic typically <1–5 mm in size. Apart from the potential negative effects of the MPs per se, it is generally assumed that microplastics may increase the exposure of marine aquatic organisms to chemicals associated with the plastics. In addition, to enhance the performance of plastics, additives are added during manufacture. Furthermore, they are active in absorbing other contaminants and be used as vectors of highly and well‐documented persistent contaminants. Finally, these small MPs are easily ingested by animals and affect their physiology and behaviour. Thus, aquatic living organisms are continuously exposed to these MPs, and associated contaminants, and could suffer from its contamination but also introduce them into the food chain

Assessment of the Performance of Imputation Techniques in Observational Studies with Two Measurements

: In observational studies with two measurements when the measured outcome pertains to a health related quality of life (HRQoL) variable, one motivation of the research may be to determine the potential predictors of the mean change of the outcome of interest. It is very common in such studies for data to be missing, which can bias the results. Different imputation techniques have been proposed to cope with missing data in outcome variables. We compared five analysis approaches (Complete Case, Available Case, K- Nearest Neighbour, Propensity Score, and a Markov Chain Monte Carlo algorithm) to assess their performance when handling missing data at different missingness rates and mechanisms (MCAR, MAR and MNAR). These strategies were applied to a pre-post study of patients with Chronic Obstructive Pulmonary Disease. We analyzed the relationship of the changes in subjects HRQoL over one year with clinical and socio-demographic characteristics. A simulation study was also performed to illustrate the performance of the imputation methods. Relative and standardized bias was assessed on each scenario. For all missingness mechanisms, not imputing and using MCMC method, both combined with mixed-model analysis, showed lowest standardized bias. Conversely, Propensity Score showed worst bias values. When missingness pattern is MCAR or MAR and rate small, we recommend using mixed models. Nevertheless, when missingness percentage is high, in order to gain sample size and statistical power, MCMC is preferred, although there are no bias differences compared with the mixed models without imputation. For a MNAR scenario, a further sensitivity analysis should be made

Open Educational Resources in virtual teaching communities

Los modelos formativos ligados a la teoría del conectivismo son cada vez más flexibles, abiertos y participativos. Bajo esta tendencia, se han expandido ideas como las comunidades virtuales docentes (CVD) y los recursos educativos abiertos (REA), que plantean oportunidades educativas en línea. El presente trabajo buscó conocer y analizar los usos y las potencialidades que tienen los recursos educativos de libre acceso en una CVD; además, se examinó el significado que los miembros de esa comunidad dan a los REA. Para esto, se recurrió a un procedimiento cualitativo de investigación que permitió la elaboración y la validación interjueces de dos instrumentos de recolección de datos: una entrevista y una guía de indicadores de análisis que, mediante la observación participante, permitió evaluar y analizar los REA compartidos en una CVD, que se caracteriza por los procesos de intercambio y trabajo colaborativo entre docentes. Dentro de los principales hallazgos se observó que las CVD tienen mayor presencia en la formación del profesorado, donde se constató el impulso y la expansión de los REA. Se evidencia la importancia general de investigar el ámbito de la educación flexible y abierta, y de forma específica el potencial empleo de CVD, donde los REA tienen una especial relevancia en la formación docente. Formative models related to the theory of connectivism are increasingly flexible, open and participatory. Under this trend, ideas such as virtual teaching communities (VCT) or Open Educational Resources (OER) have been widespread, which lead us to talk about online educational opportunities. Accordingly, this work seeks and analyzes the uses and potentialities of 'educational resources of free access' in a VCT, while examining the meaning that members of that community give to the OER. In addition, a qualitative research procedure endorsed the development and validation of data-collection instruments, such as an interview and an indicator guide to analyze and evaluate the OER shared in a VCT, through participant observation, which is characterized by exchange processes and collaborative work among teachers. Among the main findings, it is observed that virtual communities have a greater presence in teacher training, where the impulse and expansion of OER is verified. These facts highlight the importance of the research field of flexible and open education with technology and particularly, the potential use of VCT, where OER have a special relevance in teacher trainin