Dr.-Ing. Uli Niemann
- Medical Data Mining
- Interpretable Machine Learning
- Temporal Aspects of Data Mining
Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI. Progress in brain research, (260):441—451, 2021. URL
Assessing the difficulty of annotating medical data in crowdworking with help of experiments. PLOS ONE, (16)7:1-26, Public Library of Science, July 2021. URL
Visual Analysis of Missing Values in Longitudinal Cohort Study Data. Computer Graphics Forum, (39)1:63-75, 2020. URL
Phenotyping chronic tinnitus patients using self-report questionnaire data: cluster analysis and visual comparison. Scientific Reports, (10)1:16411, 2020. URL
Gender-Specific Differences in Patients With Chronic Tinnitus—Baseline Characteristics and Treatment Effects. Frontiers in Neuroscience, (14):487, 2020. URL
Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms. International Journal of Computer Assisted Radiology and Surgery, (15)9:1525--1535, 2020. URL
Cardiac Cohort Classification based on Morphologic and Hemodynamic Parameters extracted from 4D PC-MRI Data. arXiv preprint arXiv:2010.05612, 2020.
Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables. PLOS ONE, (15)1:1-18, Public Library of Science, January 2020. URL
Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics. Scientific Reports, (10)1:4664, 2020. URL
Visual Analytics for Epidemiological Cohort Studies. Proc. of Eurographics Medical Price, 2019. URL
Exploiting Entity Information for Stream Classification over a Stream of Reviews. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 564-573, ACM, 2019. URL
Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity. International Journal of Data Science and Analytics, 2019. URL
Transformation of Temperature Timeseries into Features that Characterize Patients with Diabetic Autonomic Nerve Disorder. Proc. of the 31th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS18), 65-70, 2018.
Patient Empowerment Through Summarization of Discussion Threads on Treatments in a Patient Self-help Forum. In Nicos Maglaveras, Ioanna Chouvarda, and Paulo de Carvalho (Eds.), Precision Medicine Powered by pHealth and Connected Health: ICBHI 2017, Thessaloniki, Greece, 18-21 November 2017, 229-233, Springer Singapore, 2018. URL
Predicting Document Polarities on a Stream without Reading their Contents. Proceedings of the Symposium on Applied Computing (SAC), 2018.
A Framework for Expert-Driven Subpopulation Discovery and Evaluation Using Subspace Clustering for Epidemiological Data. Expert Systems with Applications, (113):147 - 160, 2018. URL
Rupture Status Classification of Intracranial Aneurysms Using Morphological Parameters. Proc. of the 31th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS18), 48-53, 2018.
Building a Bayesian Network to Understand the Interplay of Variables in an Epidemiological Population-Based Study. Proc. of the 31th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS18), 88-93, 2018.
Subpopulation Discovery and Validation in Epidemiological Data. In Michael Sedlmair, and Christian Tominski (Eds.), EuroVis Workshop on Visual Analytics (EuroVA), The Eurographics Association, 2017.
Combining Subgroup Discovery and Clustering to Identify Diverse Subpopulations in Cohort Study Data. Proc. of the 30th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS17), 582-587, 2017.
Classification of DCE-MRI Data for Breast Cancer Diagnosis Combining Contrast Agent Dynamics and Texture Features. Bildverarbeitung für die Medizin (BVM), 325-330, Springer Verlag, Heidelberg, 2017.
Visual Analytics of Missing Data in Epidemiological Cohort Studies. Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), 43-52, 2017.
ICE: Interactive Classification Rule Exploration on Epidemiological Data. Proc. of the 30th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS17), 606-611, Thessaloniki, Greece, 2017.
Learning Pressure Patterns for Patients with Diabetic Foot Syndrome. Proc. of the 29th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS16), IEEE, Dublin, Ireland and Belfast, Northern Ireland, June 2016.
Comparative Clustering of Plantar Pressure Distributions in Diabetics with Polyneuropathy May Be Applied to Reveal Inappropriate Biomechanical Stress. PLoS ONE, (11)8:1-12, Public Library of Science, August 2016. URL
Can we classify the participants of a longitudinal epidemiological study from their previous evolution?. Proc. of the 28th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS15), 121-126, IEEE, São Carlos and Ribeirão Preto, Brazil, June 2015. URL
3D Regression Heat Map Analysis of Population Study Data. IEEE Transactions on Visualization and Computer Graphics (TVCG), (22)1:81-90, 2015.
Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis. Expert Systems with Applications, (41)11:5405-5415, Elsevier BV, September 2014. URL
Subpopulation Discovery in Epidemiological Data with Subspace Clustering. Foundations of Computing and Decision Sciences (FCDS), (39)4:271-300, 2014. URL
Interactive Medical Miner: Interactively Exploring Subpopulations in Epidemiological Datasets. In Calders et al. (Eds.), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014) - DEMO TRACK, (8726):460-463, Springer Berlin Heidelberg, 2014. URL
Classification of Benign and Malignant DCE-MRI Breast Tumors by Analyzing the Most Suspect Region. In Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, and Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2013, 45-50, Springer Berlin Heidelberg, 2013. URL
Can we distinguish between benign and malignant breast tumors in DCE-MRI by studying a tumor's most suspect region only?. In Pedro Pereira Rodrigues, Mykola Pechenizkiy, João Gama, Ricardo Cruz-Correia, Jiming Liu, Agma J. M. Traina, Peter J. F. Lucas, and Paolo Soda (Eds.), CBMS, 77-82, IEEE, 2013. URL