© MedizinFotoKöln
Ekaterina Kutafina
Dr., Dr. rer. medic.
CC BY 4.0 https://doi.org/10.1016/j.cmpb.2024.108448
Biosignale
Biosignale wie EKG, EEG oder Sensordaten von Wearables bieten einen einzigartigen Einblick in die menschliche Physiologie und Gesundheit. Wir entwickeln Methoden zur intelligenten Verarbeitung und Interpretation dieser Signale, wobei wir Signalverarbeitung, maschinelles Lernen und multimodale Integration kombinieren. Unser Ziel ist es, die Diagnose und Überwachung zu verbessern, personalisierte Interventionen zu ermöglichen und neue Wege zum Verständnis komplexer physiologischer Prozesse zu eröffnen – immer mit dem Fokus auf der klinischen Anwendbarkeit.
Tools & Services
Publikationen
Inter- and Transdisciplinarity Competencies in Medical Informatics and Digital Health Education – A Scoping Review
Introduction: Medical informatics and digital health are intrinsically transdisciplinary fields, requiring practitioners to integrate clinical knowledge, computational methods, regulatory understanding, and societal engagement. While inter- and transdisciplinary (ITD) competencies are widely considered essential in these domains, it remains unclear how explicitly they are addressed in existing training programs. Methods: To map the current landscape of interdisciplinary and transdisciplinary training initiatives relevant to medical informatics and digital health and to identify gaps and strategies in ITD competency development, a scoping review was conducted following PRISMA-ScR guidelines. PubMed was searched using a query combining terms for inter-/transdisciplinarity, education/training, and computational/informatics domains. After two-step title and abstract screening, 20 publications were included for full-text analysis. Publications were assessed for disciplinary coverage, ITD competency approaches, pedagogical frameworks, and relevance to ITD training in medical informatics. Results: The 20 included papers show that ITD competencies are predominantly addressed implicitly through project-based or collaborative learning rather than as explicit, assessable learning objectives. A clear gap exists between the acknowledged importance of ITD competencies and their formal integration into medical informatics and digital health curricula. Conclusions: Future work should develop explicit ITD learning objectives, structured curricula, and dedicated assessment methods, aligned with ITD meta-research and supported by institutional and network-level initiatives.
Distribution Shift Analysis in Generalizable Modelling: Intensive Care Time-Series Data
In this paper, we investigate how distribution shift affects model generalizability in medical time-series from intensive care. We define clinically relevant domain adaptation scenarios for predicting hypotension and hypoxemia using the MIMIC-III Matched Waveform Database v1.0. Distribution shift is quantified via the Kolmogorov–Smirnov test (KS) applied to an interpretable, lower-dimensional data representation. Our results highlight the challenges of measuring covariate, prior probability, and semantic shifts in non-stationary multivariate time series. We observed linear correlation (0.74 and 0.99) between KS(source train, target) - KS(source train, source test) and RMSE difference when comparing in-distribution and out-of-distribution model performance. We argue that developing and benchmarking data-driven models should explicitly account for modality-specific data shift characteristics.
Kurse
Einführung in die computergestützte medizinische Signal Analyse
Bachelor Studium
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Master Studium
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Vorklinik
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Der menschliche Körper sendet kontinuierlich Biosignale aus, die wertvolle Einblicke in physiologische Prozesse liefern. In der Medizin werden diese Signale sowohl zu Forschungszwecken genutzt als auch um Diagnose und Monitoring von Krankheiten und Patienten zu unterstützen. Diese Veranstaltung bietet eine praxisnahe Einführung in die computergestützte Biosignalanalyse. Nach einer kurzen theoretischen Einführung zu den Grundlagen der Signalverarbeitung, einschließlich Definition, Erfassung und Anwendungsmöglichkeiten, erfolgt eine praktische Einführung in die Datenanalyse. Anhand eines realistischen Beispiels aus dem Patientenmonitoring im Intensivmedizin-Setting werden essentielle Schritte vermittelt: Daten-Vorbereitung, Feature-Engineering und die Vorhersage des Signals mit modernen Machine Learning Methoden durchgeführt. Es wird von Teilnehmenden der Besuch der vorangegangenen Veranstaltung ¿Coding Basics¿ oder ein äquivalentes Vorwissen in der Programmierung in Python vorausgesetzt.
In KLIPS anzeigenMedical AI - From basics to pro: Heart Rate Variability & AI symbiosis in personalized medicine
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PostDoc
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Vorklinik
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WiSe
[Diese Lehrveranstaltung wird nur auf Englisch angeboten] Heart rate variability (HRV) is widely used in clinical settings as a non-invasive autonomic nervous system function marker. It helps assess cardiovascular health, stress levels, and overall well-being. Clinicians use HRV to monitor conditions like heart disease, hypertension, and diabetes, as well as to evaluate recovery in post-surgical and critically ill patients. HRV also plays a role in mental health, aiding in the diagnosis and management of anxiety, depression, or PTSD. Additionally, it is used in sports medicine and rehabilitation to track recovery and optimize training. Its broad applications make it a valuable tool in personalized medicine. The development of AI methods allows to make more complex predictions using multiple HRV parameters simultaneously. This complexity enabled successful decision support in the domains where HRV was not previously prominent for clinical use, such as epileptology. In this lecture block, we will discuss the technical aspects of HRV assessment, such as different sensors, data quality control or different HRV measures. We will review various types of clinical applications, but also the ¿citizen science¿ approach and sports coaching. For the practical part we take a dataset with precomputed R-to-R intervals and different labels (e.g. RR Interval Time Series Modeling: The PhysioNet/Computing in Cardiology Challenge 2002 v1.0.0 ). We will test different machine learning approaches to classify the data, e.g. to detect whether the data was recorded in a stressed or relaxed phase. This block lecture does not require any previous coding experience, we will use the graphical low-code platform KNIME. Students are required to bring their own laptop.
In KLIPS anzeigen