Deep Learning Applications for Biomedical Data and Natural Language Processing

The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. Development of deep learning is essentially a quest to mimic how the human brain, at least partially, operates.In this thesis, I will use machine learning techniques to tackle two different domain of problems. The first is a problem in natural language processing. We improved classification of relations within images, using text associated with the pictures. The second domain is regarding heart transplant. We created models for pre- and post-transplant survival and simulated a whole transplantation queue, to be able to asses the impact of different allocation policies. We used deep learning models to solve these problems.As introduction to these problems, I will present the basic concepts of machine learning, how to represent data, how to evaluate prediction results, and how to create different models to predict values from data. Following that, I will also introduce the field of heart transplant and some information about simulation

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Towards Robust Machine Learning for Health Applications

Methoden des maschinellen Lernens haben über die letzten Jahrzehnte beeindruckende technologische Fortschritte ermöglicht und haben das Potenzial, viele Aspekte unseres Lebens nachhaltig zu verändern. Besonders vielversprechend ist maschinelles Lernen im Gesundheitsbereich. Hier kann es unser Verständnis immer komplexerer Gesundheitsdaten vertiefen, Prozesse wie Diagnostik und Risikoeinschätzung beschleunigen sowie deren Objektivität erhöhen, und eine personalisiertere medizinische Versorgung ermöglichen. Zugleich steht maschinelles Lernen im Gesundheitsbereich vor besonderen Herausforderungen. Gesundheitsdaten sind häufig zeitabhängig und heterogen, über mehrere Institutionen verteilt und nur in begrenztem Umfang für spezifische Modellierungsanwendungen zugänglich. Infolgedessen erfordert das maschinelle Lernen für den Gesundheitsbereich grundsätzlich robuste Methoden, die für heterogene und im Umfang begrenzte Daten geeignet sind, sowie besonders auf die jeweilige Anwendung zugeschnittene Modelle. Diese Dissertation umfasst Beiträge zu beiden dieser Aspekte. Sie enthält neue Methoden zur unüberwachten Domänenadaptation, die speziell für hochdimensionale molekulare Gesundheitsdaten entwickelt wurden und eine genauere Vorhersage über heterogene Datensätze hinweg ermöglichen. Als konkretes Anwendungsbeispiel wurden diese Methoden auf das Problem der Altersvorhersage basierend auf DNA-Methylierungsdaten über Gewebe hinweg angewandt. Im Vergleich zu einem nicht-adaptiven Referenzmodell verbesserten sie hierbei die Vorhersage auf einem Gewebe, das nicht zum Trainieren der Modelle verwendet wurde. Zusätzlich enthält diese Dissertation robuste Modelle zur Analyse von Daten einer frühen klinischen Studie, die die Verwendung von breitneutralisierenden Antikörpern zur Behandlung von HIV untersuchte. Hier wurden Modelle und Methoden gewählt, die trotz des begrenzten Stichprobenumfangs Heterogenität zwischen Patientengruppen berücksichtigen konnten. Ein weiterer anwendungsspezifischer Beitrag war die Entwicklung robuster Modelle zur zeitabhängigen Vorhersage der Mortalität sowie einer Cytomegalievirus-Reaktivierung nach hämatopoetischer Stammzelltransplantation. Diese Modelle wurden in einer prospektiven, nicht-interventionellen klinischen Studie validiert und generierten in einem Pilot-Vergleich eine ähnliche genaue Vorhersage wie die Einschätzung erfahrener Kliniker. Zusätzlich unterstützte diese Dissertation die Entwicklung der XplOit-Plattform, einer Software-Plattform, die robustes maschinelles Lernen für den Gesundheitsbereich durch die semantische Integration heterogener Daten erleichtert.Machine learning has enabled striking technological advances over the last decades and has the potential to transform many aspects of our lives. Its application is especially promising in the health domain, where it can improve our understanding of increasingly complex health data, accelerate processes such as diagnosis or risk assessment while also making them more objective, and enable a more personalized approach to medicine. At the same time, machine learning for health faces particular challenges. Health data is often temporal and heterogeneous, distributed across many institutions, and accessible only in modest amounts for a specific machine learning application. Consequently, machine learning for health requires generally robust methods capable of handling heterogeneous and limited data and models that are well-tailored to the task at hand. This thesis contributes to both of these aspects. It includes new methods for unsupervised domain adaptation, which were designed for high-dimensional molecular health data and improved prediction across heterogeneous datasets. As a concrete application example, these methods were applied to the problem of age prediction from DNA methylation data across tissues, where they improved age prediction on a tissue not used for model training compared to a non-adaptive reference model. In addition, this thesis includes robust models for the analysis of data from an early clinical trial evaluating the use of broadly neutralizing antibodies for the treatment of HIV, which were suitable to account for heterogeneity between patient groups despite a limited sample size. Another application-specific contribution was the development of robust models for the time-dependent prediction of mortality and early cytomegalovirus reactivation after hematopoietic cell transplantation. These models were validated in a prospective non-interventional clinical trial and demonstrated similar performance as experienced physicians in a pilot comparison. Finally, this thesis supported the development of the XplOit platform, a software platform that facilitates robust machine learning for health by semantically integrating heterogeneous datasets

Integrative approaches to high-throughput data in lymphoid leukemias (on transcriptomes, the whole-genome mutational landscape, flow cytometry and gene copy-number alterations)

Within this thesis I developed a new approach for the analysis and integration of heterogeneous leukemic data sets applicable to any high-throughput analysis including basic research. All layers are stored in a semantic graph which facilitates modifications by just adding edges (relationships/attributes) and nodes (values/results) as well as calculating biological consensus and clinical correlation. The front-end is accessible through a GUI (graphical user interface) on a Java-based Semantic Web server. I used this framework to describe the genomic landscape of T-PLL (T-cell prolymphocytic leukemia), which is a rare (~0.6/million) mature T-cell malignancy with aggressive clinical course, notorious treatment resistance, and generally low overall survival. We have conducted gene expression and copy-number profiling as well as NGS (next-generation sequencing) analyses on a cohort comprising 94 T-PLL cases. TCL1A (T-cell leukemia/lymphoma 1A) overexpression and ATM (Ataxia Telangiectasia Mutated) impairment represent central hallmarks of T-PLL, predictive for patient survival, T-cell function and proper DNA damage responses. We identified new chromosomal lesions, including a gain of AGO2 (Argonaute 2, RISC Catalytic Component; 57.14% of cases), which is decisive for the chromosome 8q lesion. While we found significant enrichments of truncating mutations in ATM mut/no del (p=0.01365), as well as FAT (FAT Atypical Cadherin) domain mutations in ATM mut/del (p=0.01156), JAK3 (Janus Kinase 3) mut/ATM del cases may represent another tumor lineage. Using whole-transcriptome sequencing, we identified novel structural variants affecting chromosome 14 that lead to the expression of a TCL1A-TCR (T-cell receptor) fusion transcript and a likely degradated TCL1A protein. Two clustering approaches of normal T-cell subsets vs. leukemia gene expression profiles, as well as immunophenotyping-based agglomerative clustering and TCR repertoire reconstruction further revealed a restricted, memory-like T-cell phenotype. This is to date the most comprehensive, multi-level, integrative study on T-PLL and it led to an evolutionary disease model and a histone deacetylase-inhibiting / double strand break-inducing treatment that performs better than the current standard of chemoimmunotherapy in preclinical testing

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

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process