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Analysis of machine learning prediction quality for automated subgroups within the MIMIC III dataset
(2023)
The motivation for this master’s thesis is to explore the potential of predictive data analytics in the field of medicine. For this, the MIMIC-III dataset offers an extensive foundation for the construction of prediction models, including Random Forest, XGBOOST, and deep learning networks. These models were implemented to forecast the mortality of 2,655 stroke patients.
The first part of the thesis involved conducting a comprehensive data analysis of the filtered MIMIC-III dataset.
Subsequently, the effectiveness and fairness of the predictive models were evaluated. Although the performance levels of the developed models did not match those reported in related research, their potential became evident. The results obtained demonstrated promising capabilities and highlighted the effectiveness of the applied methodologies. Moreover, the feature relevance within the XGBOOST model was examined to increase model explainability.
Finally, relevant subgroups were identified to perform a comparative analysis of the prediction performance across these subgroups. While this approach can be regarded as a valuable methodology, it was not possible to investigate underlying reasons for potential unfairness across clusters. Inside the test data, not enough instances remained per subgroup for further fairness or feature relevance analysis.
In conclusion, the implementation of an alternative use case with a higher patient count is recommended.
The code for this analysis is made available via a GitHub repository and includes a frontend to visualize the results.
Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips of visual events across ten human subjects. We use the videos’ extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.
We study threshold testing, an elementary probing model with the goal to choose a large value out of n i.i.d. random variables. An algorithm can test each variable X_i once for some threshold t_i, and the test returns binary feedback whether X_i ≥ t_i or not. Thresholds can be chosen adaptively or non-adaptively by the algorithm. Given the results for the tests of each variable, we then select the variable with highest conditional expectation. We compare the expected value obtained by the testing algorithm with expected maximum of the variables. Threshold testing is a semi-online variant of the gambler’s problem and prophet inequalities. Indeed, the optimal performance of non-adaptive algorithms for threshold testing is governed by the standard i.i.d. prophet inequality of approximately 0.745 + o(1) as n → ∞. We show how adaptive algorithms can significantly improve upon this ratio. Our adaptive testing strategy guarantees a competitive ratio of at least 0.869 - o(1). Moreover, we show that there are distributions that admit only a constant ratio c < 1, even when n → ∞. Finally, when each box can be tested multiple times (with n tests in total), we design an algorithm that achieves a ratio of 1 - o(1).
Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten. However, comparison of individual methods is nevertheless performed in isolation from the real world by monitoring accumulated benchmark test set performance. The closed world assumption remains predominant, i.e. models are evaluated on data that is guaranteed to originate from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown and corrupted instances. In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era. Hence, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Finally, the established synergies are supported empirically, showing joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, while exhibiting robust open world application.
A central concern in genetics is to identify mechanisms of transcriptional regulation. The aim is to unravel the mapping between the DNA sequence and gene expression. However, it turned out that this is extremely complex. Gene regulation is highly cell type-specific and even moderate changes in gene ex- pression can have functional consequences.
Important contributors to gene regulation are transcription factors (TFs), that are able to directly interact with the DNA. Often, a first step in understanding the effect of a TF on the gene’s regulation is to identify the genomic regions a TF binds to. Therefore, one needs to be aware of the TF’s binding preferences, which are commonly summarized in TF binding motifs. Although for many TFs the binding motif is experimentally validated, there is still a large number of TFs where no binding motif is known. There exist many tools that link TF binding motifs to TFs. We developed the method Massif that improves the performance of such tools by incorporating a domain score that uses the DNA binding domain of the studied TF as additional information.
TF binding sites are often enriched in regulatory elements (REMs) such as promoters or enhancers, where the latter can be located megabases away from its target gene. However, to understand the regulation of a gene it is crucial to know where the REMs of a gene are located. We introduced the EpiRegio webserver that holds REMs associated to target genes predicted across many cell types and tissues using STITCHIT, a previously established method. Our publicly available webserver enables to query for REMs associated to genes (gene query) and REMs overlapping genomic regions (region query). We illus- trated the usefulness of EpiRegio by pointing to a TF that occurs enriched in the REMs of differential expressed genes in circPLOD2 depleted pericytes. Further, we highlighted genes, which are affected by CRISPR-Cas induced mutations in non-coding genomic regions using EpiRegio’s region query. Non-coding genetic variants within REMs may alter gene expression by modifying TF binding sites, which can lead to various kinds of traits or diseases. To understand the underlying molecular mechanisms, one aims to evaluate the effect of such genetic variations on TF binding sites. We developed an accurate and fast statistical approach, that can assess whether a single nucleotide polymorphism (SNP) is regulatory. Further, we combined this approach with epigenetic data and additional analyses in our Sneep workflow. For instance, it enables to identify TFs whose binding preferences are affected by the analyzed SNPs, which is illustrated on eQTL datasets for different cell types. Additionally, we used our Sneep workflow to highlight cardiovascular disease genes using regulatory SNPs and REM-gene interactions.
Overall, the described results allow a better understanding of REM-gene interactions and their interplay with TFs on gene regulation.
Metahumans ist ein innovatives Framework für die Unreal Engine, das hochgradig realistische digitale Charaktere zur Verfügung stellt. Metahumans zeichnen sich durch eine vollständige Control Rig aus, die es Entwicklern ermöglicht, vorgefertigte Animationen zu nutzen und sie nach Bedarf anzupassen und zu erweitern.
Im Rahmen dieser wissenschaftlichen Arbeit wird die Anwendung von Metahumans in der virtuellen Umgebung der Unreal Engine 5 untersucht. Das Hauptziel besteht darin, die Fähigkeit eines Metahumans zu untersuchen, mittels eines herkömmlichen Virtual Reality Headsets mithilfe von Motion Tracking gesteuert und animiert zu werden. Dabei wird speziell auf die Verwendung von Inverse Kinematics als Methode zur Erzeugung möglichst natürlicher Bewegungsabläufe eingegangen. Zusätzlich wird angestrebt, die Interaktion zwischen verschiedenen Metahuman-Avataren in einer Online-Sitzung zu ermöglichen.
Um den Einfluss auf das Immersionserlebnis der Benutzerinnen und Benutzer zu analysieren, werden Probandinnen und Probanden eingeladen, ihre Nutzererfahrungen zu evaluieren. Zu diesem Zweck werden zwei vergleichbare Level erstellt: eines in der Unreal Engine mit Metahumans und das andere in Unity mit den Meta Avataren von Oculus.
Diese wissenschaftliche Untersuchung zielt darauf ab, ein umfassendes Verständnis für die Leistungsfähigkeit von Metahumans zu erlangen, insbesondere im Vergleich zu anderen Avatar-Systemen.
In this contribution we present algorithms for model checking of analog circuits enabling the specification of time constraints. Furthermore, a methodology for defining time-based specifications is introduced. An already known method for model checking of integrated analog circuits has been extended to take into account time constraints. The method will be presented using three industrial circuits. The results of model checking will be compared to verification by simulation.
With the rise of digitalization and ubiquity of media use, both opportunities and challenges emerge for academic learning. One prevalent challenge is media multitasking, which can become distracting and hinder learning success. This thesis investigates two facets of this issue: the enhancement of data tracking, and the exploration of digital interventions that support self-control.
The first paper focuses on digital tracking of media use, as a comprehensive understanding of digital distractions requires careful data collection to avoid misinterpretations. The paper presents a tracking system where media use is linked to learning activities. An annotation dashboard enabled the enrichment of the log data with self-reports. The efficacy of this system was evaluated in a 14-day online course taken by 177 students, with results confirming the initial assumptions about media tracking.
The second paper tackles the recognition of whether a text was thoroughly read, an issue brought on by the tendency of students to skip lengthy and demanding texts. A method utilizing scroll data and time series classification algorithms is presented and tested, showing promising results for early recognition and intervention.
The third paper presents the results of a systematic literature review on the effectiveness of digital self-control tools in academic learning. The paper identifies gaps in existing research and outlines a roadmap for further research on self-control tools.
The fourth paper shares findings from a survey of 273 students, exploring the practical use and perceived helpfulness of DSCTs. The study highlights the challenge of balancing between too restrictive and too lenient DSCTs, particularly for platforms offering both learning content and entertainment. The results also show a special role of media use that is highly habitual.
The fifth paper of this work investigates facets of app-based habit building. In a study over 27 days, 106 school-aged children used the specially developed PROMPT-app. The children carried out one of three digital activities each day, each of which was supposed to promote a deeper or more superficial processing of plans. Significant differences regarding the processing of plans emerged between the three activities, and the results suggest that a child-friendly planning application needs to be personalized to be effective.
Overall, this work offers a comprehensive insight into the complexity and potentials of dealing with distracting media usage and shows ways for future research and interventions in this fascinating and ever more important field.
Large language models have become widely available to the general public, especially due to ChatGPT's release. Consequently, the AI community has invested much effort into recreating language models of the same caliber as ChatGPT, since the latter is still a technical blackbox. This thesis aims to contribute to that cause by proposing R.O.B.E.R.T., a Robotic Operating Buddy for Efficiency, Research and Teaching. In doing so, it presents a first implementation of a lightweight environment which produces tailor-made, instruction-following language models with a heavy focus on conversational capabilities that instruct themselves into a given domain-context. Within this environment, the generation of datasets, the fine-tuning process and finally the inference of a unique R.O.B.E.R.T. instance are all carried out as part of an automated pipeline.
We present a hierarchy of polynomial time lattice basis reduction algorithms that stretch from Lenstra, Lenstra, Lovász reduction to Korkine–Zolotareff reduction. Let λ(L) be the length of a shortest nonzero element of a lattice L. We present an algorithm which for k∈N finds a nonzero lattice vector b so that |b|2⩽(6k2)nkλ(L)2. This algorithm uses O(n2(kk+o(k))+n2)log B) arithmetic operations on O(n log B)-bit integers. This holds provided that the given basis vectors b1,…,bn∈Zn are integral and have the length bound B. This algorithm successively applies Korkine–Zolotareff reduction to blocks of length k of the lattice basis. We also improve Kannan's algorithm for Korkine-Zolotareff reduction.