Robert Dürichen
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View article: ArcTEX—a novel clinical data enrichment pipeline to support real-world evidence oncology studies
ArcTEX—a novel clinical data enrichment pipeline to support real-world evidence oncology studies Open
Data stored within electronic health records (EHRs) offer a valuable source of information for real-world evidence (RWE) studies in oncology. However, many key clinical features are only available within unstructured notes. We present ArcT…
View article: Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data
Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data Open
Background: Large and deep electronic health record (EHR) datasets have the potential to increase understanding of real-world patient journeys, and to identify subgroups of patients currently grouped with a common disease label but differi…
View article: Enabling scalable clinical interpretation of ML-based phenotypes using real world data
Enabling scalable clinical interpretation of ML-based phenotypes using real world data Open
The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR dat…
View article: Similarity-based prediction of Ejection Fraction in Heart Failure\n Patients
Similarity-based prediction of Ejection Fraction in Heart Failure\n Patients Open
Biomedical research is increasingly employing real world evidence (RWE) to\nfoster discoveries of novel clinical phenotypes and to better characterize long\nterm effect of medical treatments. However, due to limitations inherent in the\nco…
View article: Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks
Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks Open
Electronic healthcare records are an important source of information which can be used in patient stratification to discover novel disease phenotypes. However, they can be challenging to work with as data is often sparse and irregularly sa…
View article: Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes
Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes Open
The increase in availability of longitudinal electronic health record (EHR) data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of clustering algorithms focus only on patient trajectories, …
View article: Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients Open
Determining phenotypes of diseases can have considerable benefits for in-hospital patient care and to drug development. The structure of high dimensional data sets such as electronic health records are often represented through an embeddin…
View article: Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of\n Heart Failure Patients
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of\n Heart Failure Patients Open
Determining phenotypes of diseases can have considerable benefits for\nin-hospital patient care and to drug development. The structure of high\ndimensional data sets such as electronic health records are often represented\nthrough an embed…
View article: Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units
Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units Open
In this work, we propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records (EHRs) at different time horizons. The proposed approach is compared to a standard …
View article: Early risk assessment for COVID-19 patients from emergency department data using machine learning
Early risk assessment for COVID-19 patients from emergency department data using machine learning Open
Background Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic, with more than 4.8 million reported cases and 310 000 deaths worldwide. While epidemiological and clinical…
View article: Wearable-Based Affect Recognition—A Review
Wearable-Based Affect Recognition—A Review Open
Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, fo…
View article: Binary Input Layer: Training of CNN models with binary input data
Binary Input Layer: Training of CNN models with binary input data Open
For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always …
View article: Binary Input Layer: Training of CNN models with binary input data
Binary Input Layer: Training of CNN models with binary input data Open
For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always …
View article: Wearable affect and stress recognition: A review
Wearable affect and stress recognition: A review Open
Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. provide reasoning for decision making or support mental wellbeing. Recently, besides approaches based on audio, visual or text informa…