arXiv subjects

Tobias Geyer

Publications and source records attributed to Tobias Geyer.

7 recordsLinked to original sources

arXiv2026

Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment

The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Predictive Business Process Monitoring, like Next-activity-prediction (NAP) can be used as a promising technique to support physicians in treatment planning, by proposing a possible next treatment step. Existing patient data, often in the form of electronic health records, can be analyzed to recommend the next suitable step in the treatment process. However, the use of patient data poses many challenges due to its knowledge-intensive character, high variability and scarcity of medical data. To overcome these challenges, this article examines the use of the knowledge encoded in taxonomies to improve and explain the prediction of the next activity in the treatment process. This study proposes the TS4NAP approach, which uses medical taxonomies (ICD-10-CM and ICD-10-PCS) in combination with graph matching to assess the similarities of medical codes to predict the next treatment step. The effectiveness of the proposed approach will be evaluated using event logs that are derived from the MIMIC-IV dataset. The results highlight the potential of using domain-specific knowledge held in taxonomies to improve the prediction of the next activity, and thus can improve treatment planning and decision-making by making the predictions more explainable.

cs.LG↗

arXiv2025

Bounding the Minimal Current Harmonic Distortion in Optimal Modulation of Single-Phase Power Converters

Optimal pulse patterns (OPPs) are a modulation technique in which a switching signal is computed offline through an optimization process that accounts for selected performance criteria, such as current harmonic distortion. The optimization determines both the switching angles (i.e., switching times) and the pattern structure (i.e., the sequence of voltage levels). This optimization task is a challenging mixed-integer nonconvex problem, involving integer-valued voltage levels and trigono metric nonlinearities in both the objective and the constraints. We address this challenge by reinterpreting OPP design as a periodic mode-selecting optimal control problem of a hybrid system, where selecting angles and levels corresponds to choosing jump times in a transition graph. This time-domain formulation enables the direct use of convex-relaxation techniques from optimal control, producing a hierarchy of semidefinite programs that lower-bound the minimal achievable harmonic distortion and scale subquadratically with the number of converter levels and switching angles. Numerical results demonstrate the effectiveness of the proposed approachs

eess.SY↗

arXiv2025

AI-Driven Decision Support in Oncology: Evaluating Data Readiness for Skin Cancer Treatment

This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital Münster, delves into the essential role of data quality, availability, and extractability in implementing effective AI applications in oncology. By employing a multifaceted methodology, including literature review, data readiness assessment, and expert workshops, the study addresses the challenges of integrating AI into clinical decision-making. The research identifies crucial data points for skin cancer treatment decisions, evaluates their presence and quality in various information systems, and highlights the difficulties in extracting information from unstructured data. The findings underline the significance of high-quality, accessible data for the success of AI-driven CDSS in medical settings, particularly in the complex field of oncology.

cs.AI↗

arXiv2024

On the SDP Relaxation of Direct Torque Finite Control Set Model Predictive Control

This paper formulates a semidefinite programming relaxation for a long horizon direct-torque finite-control-set model predictive control problem. In parallel with this relaxation, a conventional branch-and-bound algorithm tailored for the original problem, but with an iteration limit to restrict its computational burden, is also solved. An input sequence candidate is extracted from the solution of the semidefinite program in the lifted space. This sequence is then compared with the so-called early-stopping branch-and-bound solution, and the best of the two is applied in a receding horizon fashion. In simulated case studies, the proposed approach exhibits significant improvements in torque transients, as the branch-and-bound alone struggles to find a meaningful solution due to the imposed limit.

math.OC↗

arXiv2024

Switching Frequency Limitation with Finite Control Set Model Predictive Control via Slack Variables

Past work proposed an extension to finite control set model predictive control to track both a current reference and a switching frequency reference, simultaneously. Such an objective can jeopardize the current tracking performance, and this can potentially be alleviated by instead limiting the switching frequency. To this end, we propose to limit the switching frequency in finite control set model predictive control. The switching frequency is captured with an infinite impulse response filter and bounded by an inequality constraint; its corresponding slack variable is penalized in the cost function. To solve the resulting problem efficiently, a sphere decoder with a computational speed-up is presented.

math.OC↗

arXiv2022

Declarative Guideline Conformance Checking of Clinical Treatments: A Case Study

Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another challenge are alignments, which usually do not add any value from a medical point of view. For this reason, we investigate in a case study the usability of the HL7 standard Arden Syntax for declarative, rule-based conformance checking and the use of manually modeled alignments. Using the approach, it was possible to check the conformance of treatment cases and create medically meaningful alignments for large parts of a medical guideline.

cs.AI↗

arXiv2016

High-Speed Finite Control Set Model Predictive Control for Power Electronics

Common approaches for direct model predictive control (MPC) for current reference tracking in power electronics suffer from the high computational complexity encountered when solving integer optimal control problems over long prediction horizons. We propose an efficient alternative method based on approximate dynamic programming, greatly reducing the computational burden and enabling sampling times below 25 $μ$s. Our approach is based on the offline estimation of an infinite horizon value function which is then utilized as the tail cost of an MPC problem. This allows us to reduce the controller horizon to a very small number of stages while simultaneously improving the overall controller performance. Our proposed algorithm was implemented on a small size FPGA and validated on a variable speed drive system with a three-level voltage source converter. Time measurements showed that our algorithm requires only 5.76 $μ$s for horizon N = 1 and 17.27 $μ$s for N = 2, in both cases outperforming state of the art approaches with much longer horizons in terms of currents distortion and switching frequency. To the authors' knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real-time and achieving comparable performance to formulations with long prediction horizons.

math.OC↗