Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

Background:  Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary.

Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention.

Objective: In the fields of medical care and research as well as hospital management, time series are an important part of the overall data basis. To ensure high quality standards and enable suitable decisions, tools for precise and generic imputations and forecasts that integrate the temporal dynamics are of great importance. Since forecasting and imputation tasks involve an inherent uncertainty, the focus of our work lay on a probabilistic multivariate generative approach that samples infillings or forecasts from an analysable distribution rather than producing deterministic results.

Using machine learning to estimate the calendar age based on autonomic cardiovascular function.

Introduction: Aging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic cardiovascular indices in order to estimate participants' age.

Methods: We analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers.

Generative adversarial networks for biomedical time series forecasting and imputation.

In the present systematic review we identified and summarised current research activities in the field of time series forecasting and imputation with the help of generative adversarial networks (GANs). We differentiate between imputation which describes the filling of missing values at intermediate steps and forecasting defining the prediction of future values. Especially the utilisation of such methods in the biomedical domain was to be investigated.

Semantic Anomaly Detection in Medical Time Series.

The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based on recurrent neural networks with gated recurrent units were used for the semantic encoding of such time frames. A subsequent cluster analysis conducted in the code space served as the decision mechanism labelling samples as anomalies or normal intervals, respectively.

GAN-Based Prediction of Time Series.

The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series data. Our experiment with blood pressure series showed that a generative recurrent autoencoder exhibits significant individual learning progress but needs further tuning to benefit from joint training.

Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation.

Background: Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure.

Objective: In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way.

Methods: The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets).

A Visualisation and Extraction Tool for Time Series in the MIMIC III Database.

We developed a tool based on the KNIME analytics platform for the extraction and visualisation of medical time series stored in the Medical Information Mart for Intensive Care III (MIMIC III) and the related MIMIC-III Waveform Database Matched Subset. The large number of data points and the free accessibility make these data sets an attractive source for data-driven projects in the medical domain. The problem that we tackled with our tool was the lack of an easy and extensible way of selecting, reading, and visualising stored time series.

Word Sense Disambiguation of Medical Terms via Recurrent Convolutional Neural Networks.

Background: Tagging text data with codes representing biomedical concepts plays an important role in medical data management and analysis. A problem occurs if there are ambiguous words linked to several concepts.

Objectives And Methods: This study aims at investigating word sense disambiguation based on word embedding and recurrent convolutional neural networks.