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arXiv 2020年

作者： Chen, Ziqi Min, Martin Renqiang Ning, Xia Computer Science and Engineering Department Ohio State University ColumbusOH United States Machine Learning Department NEC Labs America PrincetonNJ United States Biomedical Informatics Department Ohio State University ColumbusOH United States Translational Data Analytics Institute Ohio State University ColumbusOH United States

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network (CNN)based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles. © 2020, CC BY.

关键词： Peptides

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The 28th AIAI irish conference on artificial intelligence and cognitive science

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CEUR Workshop Proceedings 2020年 2771卷

作者： Longo, Luca Rizzo, Lucas Hunter, Elizabeth Pakrashi, Arjun Technological University Dublin School of Computer Science Ireland Machine Learning Labs Science Foundation Ireland Centre for Research Training Ireland D-Real Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality Ireland CeADAR Enterprise Ireland's Centre for Applied Artificial Intelligence Ireland University College Dublin School of Computer Science Ireland Insight Centre for Data Analytics Ireland

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Complementary App-Based Yoga Home Exercise Therapy for Patients With Axial Spondyloarthritis: Usability Study

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JMIR Formative Research 2024年 8卷 e57185页

作者： Grube, Lara Petit, Pascal Vuillerme, Nicolas Nitschke, Marlies Nwosu, Obioma Bertrand Knitza, Johannes Krusche, Martin Seifer, Ann-Kristin Eskofier, Bjoern Schett, Georg Morf, Harriet Department of Internal Medicine 3- Rheumatology & Immunology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Deutsches Zentrum Immuntherapie Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany AGEIS Université Grenoble Alpes Grenoble France Institut Universitaire de France Paris France LabCom Telecom4Health Orange Labs & Université Grenoble Alpes CNRS Inria Grenoble France Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute for Digital Medicine University Hospital of Giessen and Marburg Philipps-University Marburg Marburg Germany III. Department of Medicine University Medical Center Hamburg-Eppendorf Hamburg Germany Translational Digital Health Group Institute of AI for Health German Research Center for Environmental Health Helmholtz Zentrum München Neuherberg Germany

Background: Axial spondyloarthritis (AS) is a chronic inflammatory rheumatic disease characterized by potentially disabling inflammation of the spine and adjacent joints. Regular exercise is a cornerstone of treatment. However, patients with AS currently have little support. YogiTherapy (MaD Lab) is an app developed to support patients with AS by providing instructions for yoga-based home exercise therapy. Objective: This study aimed to evaluate the usability and acceptance of the newly designed YogiTherapy app for patients with AS. Methods: Patients completed the User Version of the Mobile Application Rating Scale (uMARS) and net promoter score (NPS) questionnaires after the app introduction. Wilcoxon Mann-Whitney rank sum test, chi-square test for count data, and correlation analysis were conducted to examine the usability of the app, acceptance, and patient characteristics. Results: A total of 65 patients with AS (33, 51% female;age: mean 43.3, SD 13.6 years) were included in the study from May 2022 to June 2023. Subsequently, the data were analyzed. Usability was rated moderate, with a mean uMARS of 3.35 (SD 0.47) points on a scale from 0 to 5. The highest-rated uMARS dimension was information (mean 3.88, SD 0.63), followed by functionality (mean 3.84, SD 0.87). Females reported a significantly higher uMARS total score than males (mean 3.47, SD 0.48 vs mean 3.23, SD 0.45;P=.03, Vargha and Delaney A [VDA] 0.66, 95% CI 0.53-0.77). The mean average of the NPS was 6.23 (SD 2.64) points (on a scale from 0 to 10), based on 43% (26/65 nonpromoters, 42% (25/65) indifferent, and 15% (9/65) promoters. A total of 7% (5/65) of those surveyed did not answer the question. When applying the NPS formula, the result is –26%. The NPS showed a positive correlation with the usage of mobile apps (r=0.39;P=.02). uMARS functionality was significantly higher rated by patients younger than 41 years (mean 4.17, SD 0.55 vs mean 3.54, SD 1;P<.001;VDA 0.69, 95% CI 0.56-0.80). Patients consi

关键词： adjacent joints ankylosing spondylitis app apps axial spondylarthritis chronic correlation analysis DHA digital health application digital therapeutics DTx eHealth exercise home exercise joint joints patient patient acceptance patients physical exercise rheumatic disease self-assessment spine spondyloarthritis therapy usability usability study Yoga YogiTherapy

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A Digital Twin to overcome long-time challenges in Photovoltaics

arXiv

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arXiv 2023年

作者： Lüer, Larry Peters, Marius Smith, Ana Sunčana Dorschky, Eva Eskofier, Bjoern M. Liers, Frauke Franke, Jörg Sjarov, Martin Brossog, Matthias Guldi, Dirk Maier, Andreas Brabec, Christoph J. Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstrasse 7 Erlangen91058 Germany Immerwahrstraße 2 Erlangen91058 Germany PULS Group Department of Physics Friedrich-Alexander-Universität Erlangen-Nürnberg IZNF Erlangen Germany Division of Physical Chemistry Ruđer Bošković Institute Bijenička cesta 54 Zagreb Croatia Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Carl-Thiersch-Straße 2b Bayern Erlangen91052 Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Cauerstr. 11 Erlangen91058 Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 7 Erlangen91058 Germany Department of Chemistry and Pharmacy Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 3 Erlangen91058 Germany Informatics Department Friedrich-Alexander-Universität Erlangen-Nürnberg Martenstr. 3 Erlangen91058 Germany

The recent successes of emerging photovoltaics (PV) such as organic and perovskite solar cells are largely driven by innovations in material science. However, closing the gap to commercialization still requires significant innovation to match contradicting requirements such as performance, longevity and recyclability. The rate of innovation, as of today, is limited by a lack of design principles linking chemical motifs to functional microscopic structures, and by an incapacity to experimentally access microscopic structures from investigating macroscopic device properties. Both limitations in turn are caused by an individualist approach to learning, not being able to produce consistent datasets large enough to find patterns and physical laws leading us to breakthrough innovations. In this work, we envision a layout of a Digital Twin for PV materials aimed at removing both limitations. The layout combines machine learning approaches, as performed in materials acceleration platforms (MAPs), with mathematical models derived from the underlying physics and digital twin concepts from the engineering world. This layout will allow using high-throughput (HT) experimentation in MAPs to improve the parametrization of quantum chemical and solid-state models. In turn, the improved and generalized models can be used to obtain the crucial structural parameters from HT data. HT experimentation will thus yield a detailed understanding of generally valid structure-property relationships, enabling inverse molecular design, that is, predicting the optimal chemical structure and process conditions to build PV devices satisfying a multitude of requirements at the same time. After motivating our proposed layout of the digital twin with causal relationships in material science, we discuss the current state of the enabling technologies such as model-based inference of hidden parameters, already being able to yield insight from HT data today. However, we identify the multiscale nature of PV

关键词： machine learning

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Unsupervised harmonic frequency-based gait sequence detection for Parkinson's disease

Unsupervised harmonic frequency-based gait sequence detectio...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Martin Ullrich Julius Hannink Heiko Gaßner Jochen Klucken Bjoern M. Eskofier Felix Kluge Machine Learning and Data Analytics Lab University of Erlangen-Nürnberg (FAU) Erlangen Germany Portabiles HealthCare Technologies GmbH Erlangen Germany Department of Molecular Neurology University Hospital Erlangen Erlangen Germany

Sensor-based gait analysis is a valuable tool in diagnosis and assessment of Parkinson's disease. Especially for large data sets, efficient analysis pipelines are required. Pre-segmentation of long time series into chunks of interest is a possible approach to increase efficiency. Therefore, we developed an unsupervised algorithm for the detection of gait sequences from continuous sensor signals. In the proposed method, gyroscope signals representing the angular rate of the feet are analyzed in the frequency domain using moving windows. A gait sequence was detected, if the frequency spectrum of a given window contained harmonic frequencies. The approach was tested on two data sets that differed in the ratio of clinical gait and cyclic movement tests. Sensitivity in both data sets was higher than 99% in a stride-to-stride comparison with ground truth. The specificity was measured with 76.1% (data set 1) and 94.5% (data set 2) for tests against sequences of other cyclic movements. In conclusion, the algorithm offers a reliable and efficient approach for the detection of gait sequences in time series data and is also promising for the application in long-term home-monitoring scenarios.

关键词： Harmonic analysis Sensitivity Foot Legged locomotion Frequency measurement Parkinson's disease Hospitals

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Increasing the robustness of the automatic IMU calibration for lower Extremity Motion Analysis

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Current Directions in Biomedical Engineering 2018年第1期4卷 439-442页

作者： Küderle, Arne Becker, Sebastian Disselhorst-Klug, Catherine Machine Learning and Data Analytics Lab. University of Erlangen-Nürnberg (FAU) Carl-Thiersch-Strasse 2b Erlangen Germany Department of Rehabilitation and Prevention Engineering Institute of Applied Medical Engineering RWTH Aachen University Aachen Germany

With the advances of IMU-based human motion tracking, joint angle tracking in an home environment has become a realistic goal. Achieving it, could enable novel applications in rehabilitation and sports medicine. However, in existing systems the process of aligning the mounted sensors with the body coordinate system is either not robust enough or to complicated for fully unsupervised usage. In this publication the performance of a promising existing algorithm is evaluated with a range of different calibration motions. Further, an extension to this implementation is proposed, aiming to improve its stability when only non-ideal calibration data is available. It could be validated that the modification of the algorithm can increase the stability and reduce the dependency of the calibration on specific calibration motions. Based on these results, we recommend the proposed extension of the algorithm as a drop-in replacement for the existing implementation. © 2018 Arne Küderle et al.

关键词： Calibration

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Correction: contactless radar-based heart rate Estimation in palliative care- a feasibility study and possible use in symptom management

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BMC palliative care 2025年第1期24卷 113页

作者： Stefan G Grießhammer Anke Malessa Hui Lu Julia Yip Julie Leuschner Florian Christgau Nils C Albrecht Marie Oesten Thanh Truc Tran Robert Richer Maria Heckel Bjoern M Eskofier Alexander Koelpin Tobias Steigleder Christoph Ostgathe Department of Palliative Medicine Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. stefan.griesshammer@uk-erlangen.de. Department of Palliative Medicine Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. Institute of High-Frequency Technology Hamburg University of Technology (TUHH) Hamburg Germany. Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus-Senftenberg Cottbus Germany. Machine Learning and Data Analytics Lab Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU) Erlangen Germany.

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Harnessing multimodal approaches for depression detection using large language models and facial expressions

npj Mental Health Research

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npj Mental Health Research 2024年第1期3卷 1-14页

作者： Sadeghi, Misha Richer, Robert Egger, Bernhard Schindler-Gmelch, Lena Rupp, Lydia Helene Rahimi, Farnaz Berking, Matthias Eskofier, Bjoern M. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany

Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis. © The Author(s) 2024.

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Simple unsupervised keyphrase extraction using sentence embeddings

arXiv

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arXiv 2018年

作者： Bennani-Smires, Kamil Musat, Claudiu Hossmann, Andreaa Baeriswyl, Michael Jaggi, Martin Data Analytics & AI Swisscom AG Machine Learning and Optimization Laboratory EPFL

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domain of the training data. At the same time, unsupervised systems have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extraction from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suitable for real-time processing of large amounts of Web data. With EmbedRank, we also explicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrases’ semantic overlap leads to no gains in F-score, our high diversity selection is preferred by humans. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Artificial Intelligence for Imaging Diagnostics in Neurosurgery

Artificial Intelligence for Imaging Diagnostics in Neurosurg...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Andrey Yu. Letyagin Sergey K. Golushko Jamil A. Rzaev Mihail E. Amelin Evgeniy N. Pavlovsky Bair N. Tuchinov Evgeniya V. Amelina Galina I. Moisak Ekaterina G. Bulgakova Liana O. Degtyareva Novosibirsk State University Novosibirsk Russia Federal State Budget Institution Federal Center of Neurosurgery Novosibirsk Russia FSBI “Federal Neurosurgical Center” Novosibirsk Russia Stread Data Analytics & Machine Learning lab. Novosibirsk State University Novosibirsk Russia

Improving the accuracy and timeliness of medical imaging in everyday clinical neurosurgery practice is an urgent problem for the health care of all countries. This problem will be solved with the help of artificial intelligence (AI). By performing the search and contouring of “suspicious” pathological objects, making automatic measurements on 3D medical images (MRI, CT), formalizing quantitative indicators and compiling medical statistical reports, AI can solve diagnostic problems in radiology and medical imaging. The effect of introducing AI will be at least a more accurate and quick interpretation of images, implemented through a set of decision-making tools for medical specialists.

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