Data-Driven Prediction of Athletes’ Performance based on their Social Media Presence

We investigated whether proxies of athlete social media activity are useful features for a machine learning model to predict athletes’ performance in subsequent competitions. We extracted millions of tweets that NBA basketball players posted themselves or were tagged in and derived features reflecting players’ mood, social media behaviour, and sleep quality before games. Using these and other non-social media-related features, we performed statistical tests to examine whether the features significantly improve the accuracy of a random forest model for predicting players’ BPM scores in upcoming games.

Classification of cardiac cohorts based on morphological and hemodynamic features derived from 4D PC-MRI data

We investigate the potential of morphological and hemodynamic features extracted from measured blood flow data in the aorta to classify hear-thealthy volunteers (HHV) and patients with bicuspid aortic valve (BAV). Furthermore, we determine features that distinguish male vs. female patients and elderly HHV vs. BAV patients. We propose a data analysis pipeline for cardiac status classification, encompassing feature selection, model training, and hyperparameter tuning.

Gender-Specific Differences in Patients With Chronic Tinnitus - Baseline Characteristics and Treatment Effects

Study examining (1) how male and female tinnitus patients differ, (2) the extent to which gender is related to (tinnitus) questionnaire responses, and (3) which baseline questionnaire items are related to tinnitus distress, depression, and treatment response, only one gender.

Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics

Study predicting depression severity in tinnitus patients after treatment based on self-report questionnaire data acquired at baseline and ranking of features w.r.t. their attribution towards model prediction.

Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables

A machine learning workflow to predict tinnitus distress after treatment based on self-report questionnaire data acquired at baseline and extraction of statistics and visualizations representing feature importance on a population-, subpopulation- and individual level.