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Institute
In recent years, great progress has been made in the area of Artificial Intelligence (AI) due to the possibilities of Deep Learning which steadily yielded new state-of-the-art results especially in many image recognition tasks.
Currently, in some areas, human performance is achieved or already exceeded.
This great development already had an impact on the area of Optical Music Recognition (OMR) as several novel methods relying on Deep Learning succeeded in specific tasks.
Musicologists are interested in large-scale musical analysis and in publishing digital transcriptions in a collection enabling to develop tools for searching and data retrieving.
The application of OMR promises to simplify and thus speed-up the transcription process by either providing fully-automatic or semi-automatic approaches.
This thesis focuses on the automatic transcription of Medieval music with a focus on square notation which poses a challenging task due to complex layouts, highly varying handwritten notations, and degradation.
However, since handwritten music notations are quite complex to read, even for an experienced musicologist, it is to be expected that even with new techniques of OMR manual corrections are required to obtain the transcriptions.
This thesis presents several new approaches and open source software solutions for layout analysis and Automatic Text Recognition (ATR) for early documents and for OMR of Medieval manuscripts providing state-of-the-art technology.
Fully Convolutional Networks (FCN) are applied for the segmentation of historical manuscripts and early printed books, to detect staff lines, and to recognize neume notations.
The ATR engine Calamari is presented which allows for ATR of early prints and also the recognition of lyrics.
Configurable CNN/LSTM-network architectures which are trained with the segmentation-free CTC-loss are applied to the sequential recognition of text but also monophonic music.
Finally, a syllable-to-neume assignment algorithm is presented which represents the final step to obtain a complete transcription of the music.
The evaluations show that the performances of any algorithm is highly depending on the material at hand and the number of training instances.
The presented staff line detection correctly identifies staff lines and staves with an $F_1$-score of above $99.5\%$.
The symbol recognition yields a diplomatic Symbol Accuracy Rate (dSAR) of above $90\%$ by counting the number of correct predictions in the symbols sequence normalized by its length.
The ATR of lyrics achieved a Character Error Rate (CAR) (equivalently the number of correct predictions normalized by the sentence length) of above $93\%$ trained on 771 lyric lines of Medieval manuscripts and of 99.89\% when training on around 3.5 million lines of contemporary printed fonts.
The assignment of syllables and their corresponding neumes reached $F_1$-scores of up to $99.2\%$.
A direct comparison to previously published performances is difficult due to different materials and metrics.
However, estimations show that the reported values of this thesis exceed the state-of-the-art in the area of square notation.
A further goal of this thesis is to enable musicologists without technical background to apply the developed algorithms in a complete workflow by providing a user-friendly and comfortable Graphical User Interface (GUI) encapsulating the technical details.
For this purpose, this thesis presents the web-application OMMR4all.
Its fully-functional workflow includes the proposed state-of-the-art machine-learning algorithms and optionally allows for a manual intervention at any stage to correct the output preventing error propagation.
To simplify the manual (post-) correction, OMMR4all provides an overlay-editor that superimposes the annotations with a scan of the original manuscripts so that errors can easily be spotted.
The workflow is designed to be iteratively improvable by training better models as soon as new Ground Truth (GT) is available.
Die Extraktion von Metadaten aus historischen Dokumenten ist eine zeitintensive, komplexe und höchst fehleranfällige Tätigkeit, die üblicherweise vom menschlichen Experten übernommen werden muss. Sie ist jedoch notwendig, um Bezüge zwischen Dokumenten herzustellen, Suchanfragen zu historischen Ereignissen korrekt zu beantworten oder semantische Verknüpfungen aufzubauen. Um den manuellen Aufwand dieser Aufgabe reduzieren zu können, sollen Verfahren der Named Entity Recognition angewendet werden. Die Klassifikation von Termen in historischen Handschriften stellt jedoch eine große Herausforderung dar, da die Domäne eine hohe Schreibweisenvarianz durch unter anderem nur konventionell vereinbarte Orthographie mit sich bringt. Diese Arbeit stellt Verfahren vor, die auch in komplexen syntaktischen Umgebungen arbeiten können, indem sie auf Informationen aus dem Kontext der zu klassifizierenden Terme zurückgreifen und diese mit domänenspezifischen Heuristiken kombinieren. Weiterhin wird evaluiert, wie die so gewonnenen Metadaten genutzt werden können, um in Workflow-Systemen zur Digitalisierung historischer Handschriften Mehrwerte durch Heuristiken zur Produktionsfehlererkennung zu erzielen.
Neuropathies are a group of potentially treatable diseases with an often disabling and restricting course. Amyotrophic lateral sclerosis (ALS) is a lethal disease without causal treatment possibilities. The objective of this study was to examine the diagnostic utility of HRUS for the differentiation of subtypes of axonal and demyelinating neuropathies and to investigate its utility for the sonological differentiation of ALS.
The hypothetical statement that neuropathy causes enlargement of peripheral nerves compared to healthy controls proved to be right, but the adjunctive assumption that ALS does not cause enlargement of peripheral nerves proved to be wrong – in patients with ALS slight enlargement of peripheral nerves was visible as well. The statement that nerve enlargement can be detected by measurement of the cross-sectional area (CSA) and the longitudinal diameter (LD) with comparable results proved to be right, but the enlargement was slightly less present by measurement of the LD. The statement that axonal and demyelinating neuropathies show distinct patterns of nerve enlargement must be answered differentiated: The comparison between axonal and demyelinating neuropathies showed a stronger nerve enlargement in patients with demyelinating neuropathies than in patients with axonal neuropathies at proximal nerve segments of upper extremities. In the comparison of diagnose-defined subgroups of inflammatory demyelinating neuropathies a respective specific pattern of nerve enlargement was visible. However, remarkable in this context was the strong nerve enlargement found in patients with NSVN, which is classified as an axonal neuropathy. Stratification for specific findings in nerve biopsy did not lead to constructive differences in comparison between the different groups.
To sum up, HRUS showed to provide a useful contribution in the diagnostic process of neuropathies and ALS but needs to be integrated in a multimodal diagnostic approach.
Context-specific Consistencies in Information Extraction: Rule-based and Probabilistic Approaches
(2015)
Large amounts of communication, documentation as well as knowledge and information are stored in textual documents. Most often, these texts like webpages, books, tweets or reports are only available in an unstructured representation since they are created and interpreted by humans. In order to take advantage of this huge amount of concealed information and to include it in analytic processes, it needs to be transformed into a structured representation. Information extraction considers exactly this task. It tries to identify well-defined entities and relations in unstructured data and especially in textual documents.
Interesting entities are often consistently structured within a certain context, especially in semi-structured texts. However, their actual composition varies and is possibly inconsistent among different contexts. Information extraction models stay behind their potential and return inferior results if they do not consider these consistencies during processing. This work presents a selection of practical and novel approaches for exploiting these context-specific consistencies in information extraction tasks. The approaches direct their attention not only to one technique, but are based on handcrafted rules as well as probabilistic models.
A new rule-based system called UIMA Ruta has been developed in order to provide optimal conditions for rule engineers. This system consists of a compact rule language with a high expressiveness and strong development support. Both elements facilitate rapid development of information extraction applications and improve the general engineering experience, which reduces the necessary efforts and costs when specifying rules.
The advantages and applicability of UIMA Ruta for exploiting context-specific consistencies are illustrated in three case studies. They utilize different engineering approaches for including the consistencies in the information extraction task. Either the recall is increased by finding additional entities with similar composition, or the precision is improved by filtering inconsistent entities. Furthermore, another case study highlights how transformation-based approaches are able to correct preliminary entities using the knowledge about the occurring consistencies.
The approaches of this work based on machine learning rely on Conditional Random Fields, popular probabilistic graphical models for sequence labeling. They take advantage of a consistency model, which is automatically induced during processing the document. The approach based on stacked graphical models utilizes the learnt descriptions as feature functions that have a static meaning for the model, but change their actual function for each document. The other two models extend the graph structure with additional factors dependent on the learnt model of consistency. They include feature functions for consistent and inconsistent entities as well as for additional positions that fulfill the consistencies.
The presented approaches are evaluated in three real-world domains: segmentation of scientific references, template extraction in curricula vitae, and identification and categorization of sections in clinical discharge letters. They are able to achieve remarkable results and provide an error reduction of up to 30% compared to usually applied techniques.
Today knowledge base authoring for the engineering of intelligent systems is performed mainly by using tools with graphical user interfaces. An alternative human-computer interaction para- digm is the maintenance and manipulation of electronic documents, which provides several ad- vantages with respect to the social aspects of knowledge acquisition. Until today it hardly has found any attention as a method for knowledge engineering.
This thesis provides a comprehensive discussion of document-centered knowledge acquisition with knowledge markup languages. There, electronic documents are edited by the knowledge authors and the executable knowledge base entities are captured by markup language expressions within the documents. The analysis of this approach reveals significant advantages as well as new challenges when compared to the use of traditional GUI-based tools.
Some advantages of the approach are the low barriers for domain expert participation, the simple integration of informal descriptions, and the possibility of incremental knowledge for- malization. It therefore provides good conditions for building up a knowledge acquisition pro- cess based on the mixed-initiative strategy, being a flexible combination of direct and indirect knowledge acquisition. Further it turns out that document-centered knowledge acquisition with knowledge markup languages provides high potential for creating customized knowledge au- thoring environments, tailored to the needs of the current knowledge engineering project and its participants. The thesis derives a process model to optimally exploit this customization po- tential, evolving a project specific authoring environment by an agile process on the meta level. This meta-engineering process continuously refines the three aspects of the document space: The employed markup languages, the scope of the informal knowledge, and the structuring and organization of the documents. The evolution of the first aspect, the markup languages, plays a key role, implying the design of project specific markup languages that are easily understood by the knowledge authors and that are suitable to capture the required formal knowledge precisely. The goal of the meta-engineering process is to create a knowledge authoring environment, where structure and presentation of the domain knowledge comply well to the users’ mental model of the domain. In that way, the approach can help to ease major issues of knowledge-based system development, such as high initial development costs and long-term maintenance problems.
In practice, the application of the meta-engineering approach for document-centered knowl- edge acquisition poses several technical challenges that need to be coped with by appropriate tool support. In this thesis KnowWE, an extensible document-centered knowledge acquisition environment is presented. The system is designed to support the technical tasks implied by the meta-engineering approach, as for instance design and implementation of new markup lan- guages, content refactoring, and authoring support. It is used to evaluate the approach in several real-world case-studies from different domains, such as medicine or engineering for instance.
We end the thesis by a summary and point out further interesting research questions consid- ering the document-centered knowledge acquisition approach.
Bei Lernprozessen spielt das Anwenden der zu erlernenden Tätigkeit eine wichtige Rolle. Im Kontext der Ausbildung an Schulen und Hochschulen bedeutet dies, dass es wichtig ist, Schülern und Studierenden ausreichend viele Übungsmöglichkeiten anzubieten. Die von Lehrpersonal bei einer "Korrektur" erstellte Rückmeldung, auch Feedback genannt, ist jedoch teuer, da der zeitliche Aufwand je nach Art der Aufgabe beträchtlich ist.
Eine Lösung dieser Problematik stellen E-Learning-Systeme dar. Geeignete Systeme können nicht nur Lernstoff präsentieren, sondern auch Übungsaufgaben anbieten und nach deren Bearbeitung quasi unmittelbar entsprechendes Feedback generieren. Es ist jedoch im Allgemeinen nicht einfach, maschinelle Verfahren zu implementieren, die Bearbeitungen von Übungsaufgaben korrigieren und entsprechendes Feedback erstellen. Für einige Aufgabentypen, wie beispielsweise Multiple-Choice-Aufgaben, ist dies zwar trivial, doch sind diese vor allem dazu gut geeignet, sogenanntes Faktenwissen abzuprüfen. Das Einüben von Lernzielen im Bereich der Anwendung ist damit kaum möglich.
Die Behandlung dieser nach gängigen Taxonomien höheren kognitiven Lernziele erlauben sogenannte offene Aufgabentypen, deren Bearbeitung meist durch die Erstellung eines Freitexts in natürlicher Sprache erfolgt. Die Information bzw. das Wissen, das Lernende eingeben, liegt hier also in sogenannter „unstrukturierter“ Form vor. Dieses unstrukturierte Wissen ist maschinell nur schwer verwertbar, sodass sich Trainingssysteme, die Aufgaben dieser Art stellen und entsprechende Rückmeldung geben, bisher nicht durchgesetzt haben. Es existieren jedoch auch offene Aufgabentypen, bei denen Lernende das Wissen in strukturierter Form eingeben, so dass es maschinell leichter zu verwerten ist. Für Aufgaben dieser Art lassen sich somit Trainingssysteme erstellen, die eine gute Möglichkeit darstellen, Schülern und Studierenden auch für praxisnahe Anwendungen viele Übungsmöglichkeiten zur Verfügung zu stellen, ohne das Lehrpersonal zusätzlich zu belasten.
In dieser Arbeit wird beschrieben, wie bestimmte Eigenschaften von Aufgaben ausgenutzt werden, um entsprechende Trainingssysteme konzipieren und implementieren zu können. Es handelt sich dabei um Aufgaben, deren Lösungen strukturiert und maschinell interpretierbar sind.
Im Hauptteil der Arbeit werden vier Trainingssysteme bzw. deren Komponenten beschrieben und es wird von den Erfahrungen mit deren Einsatz in der Praxis berichtet: Eine Komponente des Trainingssystems „CaseTrain“ kann Feedback zu UML Klassendiagrammen erzeugen. Das neuartige Trainingssystem „WARP“ generiert zu UML Aktivitätsdiagrammen Feedback in mehreren Ebenen, u.a. indem es das durch Aktivitätsdiagramme definierte Verhalten von Robotern in virtuellen Umgebungen visualisiert. Mit „ÜPS“ steht ein Trainingssystem zur Verfügung, mit welchem die Eingabe von SQL-Anfragen eingeübt werden kann. Eine weitere in „CaseTrain“ implementierte Komponente für Bildmarkierungsaufgaben ermöglicht eine unmittelbare, automatische Bewertung entsprechender Aufgaben.
Die Systeme wurden im Zeitraum zwischen 2011 und 2014 an der Universität Würzburg in Vorlesungen mit bis zu 300 Studierenden eingesetzt und evaluiert. Die Evaluierung ergab eine hohe Nutzung und eine gute Bewertung der Studierenden der eingesetzten Konzepte, womit belegt wurde, dass elektronische Trainingssysteme für offene Aufgaben in der Praxis eingesetzt werden können.
Knowledge-based systems (KBS) face an ever-increasing interest in various disciplines and contexts. Yet, the former aim to construct the ’perfect intelligent software’ continuously shifts to user-centered, participative solutions. Such systems enable users to contribute their personal knowledge to the problem solving process for increased efficiency and an ameliorated user experience. More precisely, we define non-functional key requirements of participative KBS as: Transparency (encompassing KBS status mediation), configurability (user adaptability, degree of user control/exploration), quality of the KB and UI, and evolvability (enabling the KBS to grow mature with their users). Many of those requirements depend on the respective target users, thus calling for a more user-centered development. Often, also highly expertise domains are targeted — inducing highly complex KBs — which requires a more careful and considerate UI/interaction design. Still, current KBS engineering (KBSE) approaches mostly focus on knowledge acquisition (KA) This often leads to non-optimal, little reusable, and non/little evaluated KBS front-end solutions.
In this thesis we propose a more encompassing KBSE approach. Due to the strong mutual influences between KB and UI, we suggest a novel form of intertwined UI and KB development. We base the approach on three core components for encompassing KBSE:
(1) Extensible prototyping, a tailored form of evolutionary prototyping; this builds on mature UI prototypes and offers two extension steps for the anytime creation of core KBS prototypes (KB + core UI) and fully productive KBS (core KBS prototype + common framing functionality). (2) KBS UI patterns, that define reusable solutions for the core KBS UI/interaction; we provide a basic collection of such patterns in this work. (3) Suitable usability instruments for the assessment of the KBS artifacts. Therewith, we do not strive for ’yet another’ self-contained KBS engineering methodology. Rather, we motivate to extend existing approaches by the proposed key components. We demonstrate this based on an agile KBSE model.
For practical support, we introduce the tailored KBSE tool ProKEt. ProKEt offers a basic selection of KBS core UI patterns and corresponding configuration options out of the box; their further adaption/extension is possible on various levels of expertise. For practical usability support, ProKEt offers facilities for quantitative and qualitative data collection. ProKEt explicitly fosters the suggested, intertwined development of UI and KB. For seamlessly integrating KA activities, it provides extension points for two selected external KA tools: For KnowOF, a standard office based KA environment. And for KnowWE, a semantic wiki for collaborative KA. Therewith, ProKEt offers powerful support for encompassing, user-centered KBSE.
Finally, based on the approach and the tool, we also developed a novel KBS type: Clarification KBS as a mashup of consultation and justification KBS modules. Those denote a specifically suitable realization for participative KBS in highly expertise contexts and consequently require a specific design. In this thesis, apart from more common UI solutions, we particularly also introduce KBS UI patterns especially tailored towards Clarification KBS.
Large volumes of data are collected today in many domains. Often, there is so much data available, that it is difficult to identify the relevant pieces of information. Knowledge discovery seeks to obtain novel, interesting and useful information from large datasets.
One key technique for that purpose is subgroup discovery. It aims at identifying descriptions for subsets of the data, which have an interesting distribution with respect to a predefined target concept. This work improves the efficiency and effectiveness of subgroup discovery in different directions.
For efficient exhaustive subgroup discovery, algorithmic improvements are proposed for three important variations of the standard setting: First, novel optimistic estimate bounds are derived for subgroup discovery with numeric target concepts. These allow for skipping the evaluation of large parts of the search space without influencing the results. Additionally, necessary adaptations to data structures for this setting are discussed. Second, for exceptional model mining, that is, subgroup discovery with a model over multiple attributes as target concept, a generic extension of the well-known FP-tree data structure is introduced. The modified data structure stores intermediate condensed data representations, which depend on the chosen model class, in the nodes of the trees. This allows the application for many popular model classes. Third, subgroup discovery with generalization-aware measures is investigated.
These interestingness measures compare the target share or mean value in the subgroup with the respective maximum value in all its generalizations. For this setting, a novel method for deriving optimistic estimates is proposed. In contrast to previous approaches, the novel measures are not exclusively based on the anti-monotonicity of instance coverage, but also takes the difference of coverage between the subgroup and its generalizations into account. In all three areas, the advances lead to runtime improvements of more than an order of magnitude.
The second part of the contributions focuses on the \emph{effectiveness} of subgroup discovery. These improvements aim to identify more interesting subgroups in practical applications. For that purpose, the concept of expectation-driven subgroup discovery is introduced as a new family of interestingness measures. It computes the score of a subgroup based on the difference between the actual target share and the target share that could be expected given the statistics for the separate influence factors that are combined to describe the subgroup.
In doing so, previously undetected interesting subgroups are discovered, while other, partially redundant findings are suppressed.
Furthermore, this work also approaches practical issues of subgroup discovery: In that direction, the VIKAMINE II tool is presented, which extends its predecessor with a rebuild user interface, novel algorithms for automatic discovery, new interactive mining techniques, as well novel options for result presentation and introspection. Finally, some real-world applications are described that utilized the presented techniques. These include the identification of influence factors on the success and satisfaction of university students and the description of locations using tagging data of geo-referenced images.
Die nächtliche (24-stündige) Überwachung des intraokularen Drucks (IOD) bei stationären Glaukompatienten wird in Europa seit mehr als 100 Jahren eingesetzt, um Spitzenwerte zu erkennen, die während der regulären Sprechstundenzeiten übersehen werden. Daten, die diese Praxis unterstützen, fehlen, zum Teil weil es schwierig ist, manuell erstellte IOD-Kurven mit dem objektiven Verlauf des Glaukoms zu korrelieren. Um dieses Problem zu beheben, haben wir automatisierte IOD-Datenextraktionswerkzeuge eingesetzt und auf eine Korrelation mit einem fortschreitenden Verlust der retinalen Nervenfaserschicht auf der optischen Kohärenztomographie im Spektralbereich (SDOCT) getestet.
In dieser Arbeit wurde ein Kollektiv chronisch herzinsuffizienter Patienten aus der niedergelassenen kardiologischen Betreuung in Bayern analysiert und auf die Umsetzung der zum Zeitpunkt der HF-Bavaria Studie gültigen Leitlinien untersucht. Dabei wurde das Patientenkollektiv nach dem Geschlecht und zusätzlich auch nach den neu definierten Herzinsuffizienz-Klassen der aktuell gültigen Leitlinien eingeteilt, um Unterschiede und Gemeinsamkeiten innerhalb dieser Differenzierungen darstellen zu können und einen Vergleich zu den Studien der jüngeren Vergangenheit zu ermöglichen.
Die Patienten der HF-Bavaria Studie waren zu 65,9 % männlich (n = 3569) und zu 34,1 % weiblich (n = 1848). Die Frauen litten häufiger unter HFpEF, waren seit kürzerer Zeit herzinsuffizient und waren in der Vergangenheit seltener zur Therapieintensivierung oder Intervention hospitalisiert. Die Patientinnen berichteten dabei weniger häufig Komorbiditäten. So fanden sich bei den Frauen seltener KHK, Niereninsuffizienz oder Diabetes mellitus, hingegen häufiger Herzklappenerkrankungen und Vorhofflimmern. Weiterhin wurden die Patientinnen weniger häufig mit ACE-Hemmer, Betablocker und MRA, dagegen häufiger mit ARB und Digitalis behandelt.
Im Patientenkollektiv der HF-Bavaria Studie hatten 29,0 % eine HFrEF (n = 1581), 28,9 % eine HFmrEF (n = 1577) und 42,0 % eine HFpEF (n = 2291). Patienten mit HFrEF waren überwiegend männlich, zum größten Teil seit mehr als 5 Jahren herzinsuffizient und im Vergleich zu den anderen Herzinsuffizienz-Klassen häufiger in den NYHA-Stadien III und IV eingestuft. HFrEF Patienten hatten den größten Anteil an bereits erfolgten Interventionen und Device-Therapien und die durchschnittlich höchste Anzahl an Komorbiditäten. Das Komorbiditätenspektrum bei Patienten mit HFmrEF lag prozentual in den meisten Kategorien zwischen den beiden anderen Herzinsuffizienz-Klassen. Patienten mit HFpEF waren überThe ewiegend weiblich, wiesen vergleichsweise am häufigsten eine komorbide Hypertonie oder ein Vorhofflimmern auf, während eine KHK deutlich seltener vorlag, als es in den anderen Herzinsuffizienz-Klassen der Fall war.
Die Prüfung der leitliniengerechten Pharmakotherapie bei HFrEF-Patienten ergab eine insgesamt gleichwertige Verschreibungshäufigkeit im geschlechtsspezifischen Vergleich der nach NYHA-Stadium indizierten Medikamentenklassen und Kombinationstherapien. Lediglich im NYHA-Stadium III konnte gezeigt werden, dass Männer signifikant häufiger mit einem Betablocker therapiert wurden. Weiterhin zeigte sich, bis auf wenige Ausnahmen, eine auch im nationalen und internationalen Vergleich hohe prozentuale Einnahme der stadienabhängig indizierten Medikamente. Die Einnahmerate von MRAs war vergleichsweise noch geringer als zu erwarten wäre, jedoch konnte das begleitende Vorliegen relevanter Kontraindikationen nicht zuverlässig genug erfasst werden, um die tatsächliche Versorgungslücke zu quantifizieren.
Die Analyse der Pharmakotherapie von HFmrEF- und HFpEF-Patienten zeigte, trotz bisher fehlender wissenschaftlicher Erkenntnisse zur optimalen medikamentösen Therapie dieser Patientengruppen, sehr ähnliche Einnahmehäufigkeiten der verschiedenen Substanzklassen im Vergleich zu den HFrEF-Patienten.
Die Therapie mit Devices war im Patientenkollektiv der HF-Bavaria Studie vergleichsweise selten und dabei häufiger bei männlichen Patienten vorzufinden. Die Analyse der leitliniengetreuen Indikationen von ICDs, CRTs und CRT-ICDs zu den tatsächlich implantierten Devices ergab Hinweise auf eine Unterversorgung vermittels apparativer Therapiemöglichkeiten.
Die Auswertung der HF-Bavaria Studie bestätigte die von uns erwartete Heterogenität und Komplexität der herzinsuffizienten Patienten in der niedergelassenen kardiologischen Betreuung. In dieser Arbeit konnte gezeigt werden, dass bedeutsame Unterschiede im Hinblick auf das Profil, den Verlauf und die Therapie von männlichen und weiblichen herzinsuffizienten Patienten bestehen. Die Therapieempfehlungen der Leitlinien richten sich trotz dieser Unterschiede vorrangig nach der Herzinsuffizienz-Klasse der Patienten. Bisher existierten in den Leitlinien vorrangig Therapieempfehlungen für Patienten mit einer HFrEF (und LVEF <40 %). Im Patientenkollektiv fanden sich jedoch zu 71 % Patienten mit einer LVEF ≥40 %. Dies bedeutet, dass für den Großteil der Patienten in unserer Studie bisher keine evidenzbasierten Behandlungsalgorithmen existieren, insbesondere zur Pharmakotherapie. Künftig sollte die Forschung vermehrt auf diese Evidenzlücken eingehen und idealerweise eine personalisierte Therapie ermöglichen.
Abschließend lässt sich feststellen, dass die leitliniengerechte Therapie der herzinsuffizienten Patienten in der niedergelassenen kardiologischen Versorgung in Bayern eine im nationalen und internationalen Kontext fortgeschrittene Qualität besitzt. Dennoch wurden erwartungsgemäß Möglichkeiten zur Qualitätsverbesserung im vorliegenden Projekt identifiziert.