Literature Review on Transfer Learning for Human Activity Recognition Using Mobile and Wearable Devices with Environmental Technology. Hernandez, Netzahualcoyotl; Lundström, Jens; Favela, Jesus; McChesney, Ian; Arnrich, Bert in SN Computer Science (2020). 1(2) 66.
AKI in Hospitalized Patients with COVID-19. Chan, Lili; Chaudhary, Kumardeep; Saha, Aparna; Chauhan, Kinsuk; Vaid, Akhil; Zhao, Shan; Paranjpe, Ishan; Somani, Sulaiman; Richter, Felix; Miotto, Riccardo; Lala, Anuradha; Kia, Arash; Timsina, Prem; Li, Li; Freeman, Robert; Chen, Rong; Narula, Jagat; Just, Allan C.; Horowitz, Carol; Fayad, Zahi; Cordon-Cardo, Carlos; Schadt, Eric; Levin, Matthew A.; Reich, David L.; Fuster, Valentin; Murphy, Barbara; He, John C.; Charney, Alexander W.; Bottinger, Erwin P.; Glicksberg, Benjamin S.; Coca, Steven G.; Nadkarni, Girish N. in Journal of the American Society of Nephrology (2020). 32(1) 151–160.
Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury. Chaudhary, Kumardeep; Vaid, Akhil; Duffy, Áine; Paranjpe, Ishan; Jaladanki, Suraj; Paranjpe, Manish; Johnson, Kipp; Gokhale, Avantee; Pattharanitima, Pattharawin; Chauhan, Kinsuk; O’Hagan, Ross; Vleck, Tielman Van; Coca, Steven G.; Cooper, Richard; Glicksberg, Benjamin; Bottinger, Erwin P.; Chan, Lili; Nadkarni, Girish N. in Clinical Journal of the American Society of Nephrology (2020). CJN.09330819.
Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Van Hout, Cristopher V; Tachmazidou, Ioanna; Backman, Joshua D; Hoffman, Joshua D; Liu, Daren; Pandey, Ashutosh K; Gonzaga-Jauregui, Claudia; Khalid, Shareef; Ye, Bin; Banerjee, Nilanjana; Li, Alexander H; O’Dushlaine, Colm; Marcketta, Anthony; Staples, Jeffrey; Schurmann, Claudia; Hawes, Alicia; Maxwell, Evan; Barnard, Leland; Lopez, Alexander; Penn, John; Habegger, Lukas; Blumenfeld, Andrew L; Bai, Xiaodong; O’Keeffe, Sean; Yadav, Ashish; Praveen, Kavita; Jones, Marcus; Salerno, William J; Chung, Wendy K; Surakka, Ida; Willer, Cristen J; Hveem, Kristian; Leader, Joseph B; Carey, David J; Ledbetter, David H; Cardon, Lon; Yancopoulos, George D; Economides, Aris; Coppola, Giovanni; Shuldiner, Alan R; Balasubramanian, Suganthi; Cantor, Michael; Nelson, Matthew R; Whittaker, John; Reid, Jeffrey G; Marchini, Jonathan; Overton, John D; Scott, Robert A; Abecasis, Gonçalo R; Yerges-Armstrong, Laura; Baras, Aris in Nature (2020).
The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world(1). Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6\% have a frequency of less than 1\%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97\%) had at least one carrier with a LOF variant, and most genes (more than 69\%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.
Economic impact of clinical decision support interventions based on electronic health records. Lewkowicz, Daniel; Wohlbrandt, Attila; Boettinger, Erwin in BMC Health Services Research (2020). 20, 871(1)
Proteomic analysis reveals upregulation of ACE2, the putative SARS-CoV-2 receptor in pressure- but not volume-overloaded human hearts. Stegbauer, Johannes Stegbauer; Kraus, Milena; Nordmeyer, Sarah; Kirchner, Marieluise; Ziehm, Matthias Ziehm; Dommisch, Henrik; Kelle, Sebastian; Kelm, Marcus; Baczko, Istvan; Landmesser, Ulf; Tschope, Carsten; Knosalla, Christoph; Falcke, Martin; Schapranow, Matthieu-P.; Regitz-Zagrosek, Vera; Mertins, Philipp; Kühne, Titus in Hypertension (2020).
Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations. Zhou, Lin; Tunca, Can; Fischer, Eric; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert (2020).
Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life,individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.
Tangle Ledger for Decentralized Learning. Schmid, R.; Pfitzner, B.; Beilharz, J.; Arnrich, B.; Polze, A. (2020). 852–859.
Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing nodes.Previous approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack surface.We propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.
The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare. Maas, Lientje; Geurtsen, Mathan; Nouwt, Florian; Schouten, Stefan F; Van De Water, Robin; Van Dulmen, Sandra; Dalpiaz, Fabiano; Van Deemter, Kees; Brinkkemper, Sjaak in Information Technology in Healthcare: IT Architectures and Implementations in Healthcare Environments (2020).
Documenting patient medical information in the electronic medical record is a time-consuming task at the expense of direct patient care. We propose an integrated solution to automate the process of medical reporting. This vision is enabled through the integration of speech and action recognition technology with semantic interpretation based on knowledge graphs. This paper presents our dialogue summarization pipeline that transforms speech into a medical report via transcription and formal representation. We discuss the functional and technical architecture of our Care2Report system along with an initial system evaluation with data of real consultation sessions.
Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records. Freitas, Jessica K. De; Johnson, Kipp W.; Golden, Eddye; Nadkarni, Girish N.; Dudley, Joel T.; Bottinger, Erwin P.; Glicksberg, Benjamin S.; Miotto, Riccardo (2020).
Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry. Wenric, Stephane; Jeff, Janina M.; Joseph, Thomas; Yee, Muh-Ching; Belbin, Gillian M.; Obeng, Aniwaa Owusu; Ellis, Stephen B.; Bottinger, Erwin P.; Gottesman, Omri; Levin, Matthew A.; Kenny, Eimear E. in The Pharmacogenomics Journal (2020). 21(2) 174–189.
Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19. Chan, Lili; Jaladanki, Suraj K.; Somani, Sulaiman; Paranjpe, Ishan; Kumar, Arvind; Zhao, Shan; Kaufman, Lewis; Leisman, Staci; Sharma, Shuchita; He, John Cijiang; Murphy, Barbara; Fayad, Zahi A.; Levin, Matthew A.; Bottinger, Erwin P.; Charney, Alexander W.; Glicksberg, Benjamin S.; Coca, Steven G.; Nadkarni, Girish N. in Clinical Journal of the American Society of Nephrology (2020). 16(3) 452–455.
The effect of LRRK2 loss-of-function variants in humans. Whiffin, Nicola; Armean, and Irina M.; Kleinman, Aaron; Marshall, Jamie L.; Minikel, Eric V.; Goodrich, Julia K.; Quaife, Nicholas M.; Cole, Joanne B.; Wang, Qingbo; Karczewski, Konrad J.; Cummings, Beryl B.; Francioli, Laurent; Laricchia, Kristen; Guan, Anna; Alipanahi, Babak; Morrison, Peter; Baptista, Marco A. S.; Merchant, Kalpana M.; Ware, James S.; Havulinna, Aki S.; Iliadou, Bozenna; Lee, Jung-Jin; Nadkarni, Girish N.; Whiteman, Cole; Daly, Mark; Esko, T~onu; Hultman, Christina; Loos, Ruth J. F.; Milani, Lili; Palotie, Aarno; Pato, Carlos; Pato, Michele; Saleheen, Danish; Sullivan, Patrick F.; Alföldi, Jessica; Cannon, Paul; MacArthur, Daniel G.; and in Nature Medicine (2020). 26(6) 869–877.
Schöne neue Ärzte-Welt: Kümmern sich kluge Computer bald um unsere Gesundheitsprobleme?. Schapranow, Matthieu-P. in F.A.Z. Sonderbeliage Gesundheit (2020). (48) B2.
Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City. Sigel, Keith; Swartz, Talia; Golden, Eddye; Paranjpe, Ishan; Somani, Sulaiman; Richter, Felix; Freitas, Jessica K De; Miotto, Riccardo; Zhao, Shan; Polak, Paz; Mutetwa, Tinaye; Factor, Stephanie; Mehandru, Saurabh; Mullen, Michael; Cossarini, Francesca; Bottinger, Erwin; Fayad, Zahi; Merad, Miriam; Gnjatic, Sacha; Aberg, Judith; Charney, Alexander; Nadkarni, Girish; Glicksberg, Benjamin S in Clinical Infectious Diseases (2020).
Good news: How data science helps us to better understand the Coronavirus pandemic. Schapranow, Matthieu-P. in Portal Wissen: The research magazine of the University of Potsdam (2020). 2(9) 14–19.
ALPS: A Web Platform for Analysing Multimodal Sensor Data in the Context of Digital Health. Musmann, F.; Sasso, A.; Arnrich, B. (2020). 1–12.
Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis: Observational Study (Preprint). Hirten, Robert P; Danieletto, Matteo; Tomalin, Lewis; Choi, Katie Hyewon; Zweig, Micol; Golden, Eddye; Kaur, Sparshdeep; Helmus, Drew; Biello, Anthony; Pyzik, Renata; Charney, Alexander; Miotto, Riccardo; Glicksberg, Benjamin S; Levin, Matthew; Nabeel, Ismail; Aberg, Judith; Reich, David; Charney, Dennis; Bottinger, Erwin P; Keefer, Laurie; Suarez-Farinas, Mayte; Nadkarni, Girish N; Fayad, Zahi A in Journal of Medical Internet Research (2020).
HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. Morassi Sasso, Ariane; Datta, Suparno; Jeitler, Michael; Steckhan, Nico; Kessler, Christian S.; Michalsen, Andreas; Arnrich, Bert; Böttinger, Erwin M. Michalowski, R. Moskovitch (eds.) (2020). (Vol. 12299)
The state of the art for monitoring hypertension relies on measuring blood pressure (BP) using uncomfortable cuff-based devices. Hence, for increased adherence in monitoring, a better way of measuring BP is needed. That could be achieved through comfortable wearables that contain photoplethysmography (PPG) sensors. There have been several studies showing the possibility of statistically estimating systolic and diastolic BP (SBP/DBP) from PPG signals. However, they are either based on measurements of healthy subjects or on patients on (ICUs). Thus, there is a lack of studies with patients out of the normal range of BP and with daily life monitoring out of the ICUs. To address this, we created a dataset (HYPE) composed of data from hypertensive subjects that executed a stress test and had 24-h monitoring. We then trained and compared machine learning (ML) models to predict BP. We evaluated handcrafted feature extraction approaches vs image representation ones and compared different ML algorithms for both. Moreover, in order to evaluate the models in a different scenario, we used an openly available set from a stress test with healthy subjects (EVAL). The best results for our HYPE dataset were in the stress test and had a mean absolute error (MAE) in mmHg of 8.79 (±3.17) for SBP and 6.37 (±2.62) for DBP; for our EVAL dataset it was 14.74 (±4.06) and 7.12 (±2.32) respectively. Although having tested a range of signal processing and ML techniques, we were not able to reproduce the small error ranges claimed in the literature. The mixed results suggest a need for more comparative studies with subjects out of the intensive care and across all ranges of blood pressure. Until then, the clinical relevance of PPG-based predictions in daily life should remain an open question.
IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition. Konak, Orhan; Wegner, Pit; Arnrich, Bert in Sensors (Switzerland) (2020). 20(24) 1–15.
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
Spotlight on Women in Tech: Fostering an Inclusive Workforce when Exploring and Exploiting Digital Innovation Potentials. Schmitt, Franziska; Sundermeier, Janina; Bohn, Nicolai; Morassi Sasso, Ariane (2020). (Vol. 6)
StudyU: a platform for designing and conducting innovative digital N-of-1 trials Konigorski, Stefan; Wernicke, Sarah; Slosarek, Tamara; Zenner, Alexander M.; Strelow, Nils; Ruether, Ferenc D.; Henschel, Florian; Manaswini, Manisha; Pottbäcker, Fabian; Edelman, Jonathan A.; Owoyele, Babajide; Danieletto, Matteo; Golden, Eddye; Zweig, Micol; Nadkarni, Girish; Böttinger, Erwin (2020).
IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition. Konak, Orhan; Wegner, Pit; Arnrich, Bert in Sensors (2020). 20(24) 7179.
Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia. Hernandez, Netzahualcoyotl; Garcia-Constantino, Matias; Beltran, Jessica; Hecker, Pascal; Favela, Jesus; Cleland, Ian; Lopez, Hussein; Arnrich, Bert; McChesney, Ian in EAI Endorsed Transactions on Pervasive Health and Technology (2020). 5(19)
INTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving that affects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxiety and remain for extended periods, which affects their quality of life. OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during the manifestation of anxiety in patients with dementia. METHODS: We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structured interviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, we developed the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio to represent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety in patients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patients during episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network model trained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the two groups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved from publicly available datasets. RESULTS: The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitative results from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network and Support Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classification accuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%. CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre, qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of a methodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude by signalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.
Position Matters: Sensor Placement for SittingPosture Classification. Kappattanavar, A. M.; da Cruz, H. F.; Arnrich, B.; Böttinger, E. (2020).
Prolonged sitting behavior and postures that cause strain on the spine and muscles have been reported to increase the probability of low back pain. To address this issue, many commercially available sensors already provide feedback about whether a person is 'slouching' or 'not slouching'. However, they do not provide information on a person's posture, which would give insights into the strain caused by a specific posture. Hence, in this pilot study, we attempt to find the optimum number of inertial measurement unit sensors required and the best locations to place them using six mock postures. Data is collected from these sensors and features are extracted. The number of features are reduced and the best features are selected using the Recursive Feature Elimination method with Cross-Validation. The reduced number of features is then trained and tested on Logistic Regression, Support Vector Machine and Hierarchical Model. Among the three models, the Support Vector Machine algorithm had the highest accuracy of 93.68%, obtained for the thoracic, hip and sacral region sensor combinations. While these findings will be validated in a larger study in an uncontrolled environment, this pilot study quantitatively highlights the importance of sensor placement in shaping discriminative performance in sitting posture classification tasks.
Will You Be My Quarantine: A Computer Vision and Inertial Sensor Based Home Exercise System. Albert, Justin; Zhou, Lin; Gloeckner, Pawel; Trautmann, Justin; Ihde, Lisa; Eilers, Justus; Kamal, Mohammed; Arnrich, Bert (2020). (Vol. 14)
The quarantine situation inflicted by the COVID-19 pandemic has left many people around the world isolated at home. Despite the large variety of mobile device-based self exercise tools for training plans, activity recognition or repetition counts, it remains challenging for an inexperienced person to perform fitness workouts or learn a new sport with the correct movements at home. As a proof of concept, a home exercise system has been developed in this contribution. The system takes computer vision and inertial sensor data recorded for the same type of exercise as two independent inputs, and processes the data from both sources into the same representations on the levels of raw inertial measurement unit (IMU) data and 3D movement trajectories. Moreover, a Key Performance Indicator (KPI) dashboard was developed for data import and visualization. The usability of the system was investigated with an example use case where the learner equipped with IMUs performed a kick movement and was able to compare it to that from a coach in the video.
Tackling the SHL Challenge 2020 with Person-Specific Classifiers and Semi-Supervised Learning. Kalabakov, Stefan; Stankoski, Simon; Rescic, Nina; Kiprijanovska, Ivana; Andova, Andrejaana; Picard, Clement; Janko, Vito; Gjoreski, Martin; Lustrek, Mitja in UbiComp-ISWC ’20 (2020). 323–328.
The SHL recognition challenge 2020 was an open competition in activity recognition where the participants were tasked with recognizing eight different modes of locomotion and transportation with smartphone sensors. The main challenges were that the training data was recorded by a different person than the validation and test data, and that the smartphone location in the test data was unknown to the participants. We, team "Third time's a charm", tackled the first challenge by attempting to identify the persons with clustering, and then performed cluster/person-specific feature selection to build a separate classifier for each person. The smartphone location appears not to make much difference. We also used semi-supervised learning to classify the test data. Internal tests using this methodology yielded an accuracy of 81.01%.
Hand in Hand: Wie KI und Ärzte in der Onkologie zusammenarbeiten. Schapranow, Matthieu-P. in Konkrete Anwendungsfälle von KI & Big-Data in der Industrie (2020). 69–74.
Using CEF Digital Service Infrastructures in the Smart4Health Project for the Exchange of Electronic Health Records. Slosarek, Tamara; Wohlbrandt, Attila; Böttinger, Erwin in arXiv preprint arXiv:2001.01477 (2020).
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Vaid, Akhil; Somani, Sulaiman; Russak, Adam J; Freitas, Jessica K De; Chaudhry, Fayzan F; Paranjpe, Ishan; Johnson, Kipp W; Lee, Samuel J; Miotto, Riccardo; Richter, Felix; Zhao, Shan; Beckmann, Noam D; Naik, Nidhi; Kia, Arash; Timsina, Prem; Lala, Anuradha; Paranjpe, Manish; Golden, Eddye; Danieletto, Matteo; Singh, Manbir; Meyer, Dara; OtextquotesingleReilly, Paul F; Huckins, Laura; Kovatch, Patricia; Finkelstein, Joseph; Freeman, Robert M.; Argulian, Edgar; Kasarskis, Andrew; Percha, Bethany; Aberg, Judith A; Bagiella, Emilia; Horowitz, Carol R; Murphy, Barbara; Nestler, Eric J; Schadt, Eric E; Cho, Judy H; Cordon-Cardo, Carlos; Fuster, Valentin; Charney, Dennis S; Reich, David L; Bottinger, Erwin P; Levin, Matthew A; Narula, Jagat; Fayad, Zahi A; Just, Allan C; Charney, Alexander W; Nadkarni, Girish N; Glicksberg, Benjamin S in Journal of Medical Internet Research (2020). 22(11) e24018.
Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study. Albert, Justin; Owolabi, Victor; Gebel, Arnd; Brahms, Markus Clemens; Granacher, Urs; Arnrich, Bert in MDPI Sensors (2020). 20(18)
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking usinggold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
Powerful rare variant association testing in a copula-based joint analysis of multiple traits. Konigorski, Stefan; Yilmaz, Yildiz E.; Janke, Jürgen; Bergmann, Manuela M.; Boeing, Heiner; Pischon, Tobias in Genetic Epidemiology (2020). 44(1) 26–40.
The SARS-CoV-2 effective reproduction rate has a high correlation with a contact index derived from large-scale individual location data using GPS-enabled mobile phones in Germany Rüdiger, S; Konigorski, S; Edelman, J; Zernick, D; Lippert, C; Thieme, A (2020).
Data Science für Digitale Medizin. Fehr, J; Konigorski, S; Lippert, C D. Matusiewicz, M. Henningsen, J. Ehlers (eds.) (2020). (Vol. Digitale Medizin – Kompendium für Studium und Praxis)
Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19. Chan, Lili; Jaladanki, Suraj K.; Somani, Sulaiman; Paranjpe, Ishan; Kumar, Arvind; Zhao, Shan; Kaufman, Lewis; Leisman, Staci; Sharma, Shuchita; He, John Cijiang; Murphy, Barbara; Fayad, Zahi A.; Levin, Matthew A.; Bottinger, Erwin P.; Charney, Alexander W.; Glicksberg, Benjamin S.; Coca, Steven G.; Nadkarni, Girish N. in Clinical Journal of the American Society of Nephrology (2020). CJN.12360720.
Using Interpretability Approaches to Update Black-Box Clinical Prediction Models: an External Validation Study in Nephrology. da Cruz, Harry Freitas; Pfahringer, Boris; Martensen, Tom; Schneider, Frederic; Meyer, Alexander; Bottinger, Erwin; Schapranow, Matthieu-P. in Artificial Intelligence in Medicine (2020). 101982.
Constrained expectation maximisation algorithm for estimating ARMA models in state space representation. Galka, Andreas; Moontaha, Sidratul; SIniatchkin, Siniatchkin in EURASIP Journal on Advances in Signal Processing 2020.1 (2020). 1–37.
GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines. Borchert, Florian; Lohr, Christina; Modersohn, Luise; Langer, Thomas; Follmann, Markus; Sachs, Jan Philipp; Hahn, Udo; Schapranow, Matthieu-P. (2020). 38–48.
The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.
#nCoVStats: Wie Data Science hilft die Coronavirus-Pandemie zu verstehen. Schapranow, Matthieu-P. in gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland (2020). 13 34–37.
"Herr Doktor, verstehen Sie mich?“: Wie lernende Systeme helfen medizinische Fachsprache zu verstehen und welche Rolle klinische Leitlinien dabei spielen. Borchert, Florian; Lohr, Christina; Modersohn, Luise; Hahn, Udo; Langer, Thomas; Wenzel, Gregor; Follmann, Markus; Schapranow, Matthieu-P. in gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland (2020). 13 19–22.
Latente Tuberkulose bei medizinischem Personal in Deutschland nach Auslandseinsatz. Meier, I; Schablon, A; Nienhaus, A; Konigorski, S in Pneumologie (2020). 74 1–7.
Good News: Wie Data Science dabei hilft, die Corona-Pandemie besser zu verstehen. Schapranow, Matthieu-P. in Portal Wissen: Das Forschungsmagazin der Universität Potsdam (2020). 9(2) 14–19.
Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study. Moontaha, Sidratul; Steckhan, Nico; Kappattanavar, Arpita; Surges, Rainer; Arnrich, Bert (2020). (Vol. 14)
Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.
Fast kernel-based rare-variant association tests integrating variant annotations from deep learning. Konigorski, S; Monti, R; Rautenstrauch, P; Lippert, C (2020). (Vol. 44) 495.
Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19. Somani, Sulaiman S.; Richter, and Felix; Fuster, Valentin; Freitas, Jessica K. De; Naik, Nidhi; Sigel, Keith; Bottinger, Erwin P; Levin, Matthew A.; Fayad, Zahi; Just, Allan C.; Charney, Alexander W.; Zhao, Shan; Glicksberg, Benjamin S.; Lala, Anuradha; Nadkarni, Girish N. in Journal of General Internal Medicine (2020). 35(10) 2838–2844.
Federated Learning in a Medical Context: A Systematic Literature Review. Pfitzner, Bjarne; Steckhan, Nico; Arnrich, Bert in ACM Transactions on Internet Technology (TOIT) Special Issue on Security and Privacy of Medical Data for Smart Healthcare (2020).
Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. On the other hand, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling. Piccininni, Marco; Konigorski, Stefan; Rohmann, Jessica L; Kurth, Tobias in BMC Medical Research Methodology (2020). 20 179.
Association of APOL1 Risk Genotype and Air Pollution for Kidney Disease. Paranjpe, Ishan; Chaudhary, Kumardeep; Paranjpe, Manish; O’Hagan, Ross; Manna, Sayan; Jaladanki, Suraj; Kapoor, Arjun; Horowitz, Carol; DeFelice, Nicholas; Cooper, Richard; Glicksberg, Benjamin; Bottinger, Erwin P.; Just, Allan C.; Nadkarni, Girish N. in Clinical Journal of the American Society of Nephrology (2020). 15(3) 401–403.
The Influence of Reward on Facial Mimicry: No Evidence for a Significant Effect of Oxytocin. Trilla, Irene; Drimalla, Hanna; Bajbouj, Malek; Dziobek, Isabel in Frontiers in Behavioural Neuroscience (2020).
Event Log Generation in a Health System: A Case Study. Remy, Simon; Pufahl, Luise; Sachs, Jan Philipp; Böttinger, Erwin; Weske, Mathias in Lecture Notes in Computer Science (2020). 505–522.
AKI in Hospitalized Patients with COVID-19. Chan, Lili; Chaudhary, Kumardeep; Saha, Aparna; Chauhan, Kinsuk; Vaid, Akhil; Zhao, Shan; Paranjpe, Ishan; Somani, Sulaiman; Richter, Felix; Miotto, Riccardo; Lala, Anuradha; Kia, Arash; Timsina, Prem; Li, Li; Freeman, Robert; Chen, Rong; Narula, Jagat; Just, Allan C.; Horowitz, Carol; Fayad, Zahi; Cordon-Cardo, Carlos; Schadt, Eric; Levin, Matthew A.; Reich, David L.; Fuster, Valentin; Murphy, Barbara; He, John C.; Charney, Alexander W.; Böttinger, Erwin P.; Glicksberg, Benjamin S.; Coca, Steven G.; Nadkarni, Girish N.; Li, Li in Journal of the American Society of Nephrology (2020). ASN.2020050615.
Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity. Yaghootkar, Hanieh; Zhang, Yiying; Spracklen, Cassandra N; Karaderi, Tugce; Huang, Lam Opal; Bradfield, Jonathan; Schurmann, Claudia; Fine, Rebecca S; Preuss, Michael H; Kutalik, Zoltan; Wittemans, Laura Bl; Lu, Yingchang; Metz, Sophia; Willems, Sara M; Li-Gao, Ruifang; Grarup, Niels; Wang, Shuai; Molnos, Sophie; Sandoval-Zárate, América A; Nalls, Mike A; Lange, Leslie A; Haesser, Jeffrey; Guo, Xiuqing; Lyytikäinen, Leo-Pekka; Feitosa, Mary F; Sitlani, Colleen M; Venturini, Cristina; Mahajan, Anubha; Kacprowski, Tim; Wang, Carol A; Chasman, Daniel I; Amin, Najaf; Broer, Linda; Robertson, Neil; Young, Kristin L; Allison, Matthew; Auer, Paul L; Blüher, Matthias; Borja, Judith B; Bork-Jensen, Jette; Carrasquilla, Germán D; Christofidou, Paraskevi; Demirkan, Ayse; Doege, Claudia A; Garcia, Melissa E; Graff, Mariaelisa; Guo, Kaiying; Hakonarson, Hakon; Hong, Jaeyoung; Ida Chen, Yii-Der; Jackson, Rebecca; Jakupović, Hermina; Jousilahti, Pekka; Justice, Anne E; Kähönen, Mika; Kizer, Jorge R; Kriebel, Jennifer; LeDuc, Charles A; Li, Jin; Lind, Lars; Luan, Jian’an; Mackey, David; Mangino, Massimo; Männistö, Satu; Martin Carli, Jayne F; Medina-Gomez, Carolina; Mook-Kanamori, Dennis O; Morris, Andrew P; de Mutsert, Renée; Nauck, Matthias; Nedeljkovic, Ivana; Pennell, Craig E; Pradhan, Arund D; Psaty, Bruce M; Raitakari, Olli T; Scott, Robert A; Skaaby, Tea; Strauch, Konstantin; Taylor, Kent D; Teumer, Alexander; Uitterlinden, Andre G; Wu, Ying; Yao, Jie; Walker, Mark; North, Kari E; Kovacs, Peter; Ikram, M Arfan; van Duijn, Cornelia M; Ridker, Paul M; Lye, Stephen; Homuth, Georg; Ingelsson, Erik; Spector, Tim D; McKnight, Barbara; Province, Michael A; Lehtimäki, Terho; Adair, Linda S; Rotter, Jerome I; Reiner, Alexander P; Wilson, James G; Harris, Tamara B; Ripatti, Samuli; Grallert, Harald; Meigs, James B; Salomaa, Veikko; Hansen, Torben; Willems van Dijk, Ko; Wareham, Nicholas J; Grant, Struan Fa; Langenberg, Claudia; Frayling, Timothy M; Lindgren, Cecilia M; Mohlke, Karen L; Leibel, Rudolph L; Loos, Ruth Jf; Kilpeläinen, Tuomas O in Diabetes (2020).
HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. Sasso, Ariane Morassi; Datta, Suparno; Jeitler, Michael; Steckhan, Nico; Kessler, Christian S; Michalsen, Andreas; Arnrich, Bert; Boettinger, Erwin (2020). 325–335.
Eatomics: Shiny exploration of quantitative proteomics data. Kraus, Milena; Mathew Stephen, Mariet; Schapranow, Matthieu-P in Journal of Proteome Research, (S. Weintraub, ed.) (2020).
Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). Drimalla, Hanna; Scheffer, Tobias; Landwehr, Niels; Baskow, Irina; Roepke, Stefan; Behnia, Behnoush; Dziobek, Isabel in npj digital medicine (2020). 3(25)
Clinical Characteristics of Hospitalized Covid-19 Patients in New York City. Paranjpe, Ishan; Russak, Adam; Freitas, Jessica K De; Lala, Anuradha; Miotto, Riccardo; Vaid, Akhil; Johnson, Kipp W; Danieletto, Matteo; Golden, Eddye; Meyer, Dara; Singh, Manbir; Somani, Sulaiman; Manna, Sayan; Nangia, Udit; Kapoor, Arjun; OtextquotesingleHagan, Ross; OtextquotesingleReilly, Paul F; Huckins, Laura M; Glowe, Patricia; Kia, Arash; Timsina, Prem; Freeman, Robert M; Levin, Matthew A; Jhang, Jeffrey; Firpo, Adolfo; Kovatch, Patricia; Finkelstein, Joseph; Aberg, Judith A; Bagiella, Emilia; Horowitz, Carol R; Murphy, Barbara; Fayad, Zahi A; Narula, Jagat; Nestler, Eric J; Fuster, Valentin; Cordon-Cardo, Carlos; Charney, Dennis S; Reich, David L; Just, Allan C; Bottinger, Erwin P; Charney, Alexander W; Glicksberg, Benjamin S; Nadkarni, Girish in medRxiv (2020). (I) Version 1 (April 23, 2020 – 03:18).
ABSTRACT Background: The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. Methods Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. Results A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. Conclusions This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.
It is time to reality check the promises of machine learning-powered precision medicine. Wilkinson, Jack; Arnold, Kellyn F; Murray, Eleanor J; van Smeden, Maarten; Carr, Kareem; Sippy, Rachel; de Kamps, Marc; Beam, Andrew; Konigorski, Stefan; Lippert, Christoph; Gilthorpe, Mark S; Tennant, Peter WG in The Lancet Digital Health (2020).
How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications. Zhou, Lin; Fischer, Eric; Tunca, Can; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert in Sensors (2020).