TY  - JOUR
A1  - Freitas da Cruz, Harry
A1  - Pfahringer, Boris
A1  - Martensen, Tom
A1  - Schneider, Frederic
A1  - Meyer, Alexander
A1  - Böttinger, Erwin
A1  - Schapranow, Matthieu-Patrick
T1  - Using interpretability approaches to update "black-box" clinical prediction models
BT  - an external validation study in nephrology
JF  - Artificial intelligence in medicine : AIM
N2  - Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.
KW  - Clinical predictive modeling
KW  - Nephrology
KW  - Validation
KW  - Interpretability
KW  - methods
Y1  - 2021
U6  - https://doi.org/10.1016/j.artmed.2020.101982
SN  - 0933-3657
SN  - 1873-2860
VL  - 111
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - THES
A1  - Grum, Marcus
T1  - Construction of a concept of neuronal modeling
N2  - The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
N2  - Die vorliegende Arbeit addressiert das Geschäftsproblem von ineffizienten Prozessen, unpräzisen Prozessanalysen und -simulationen sowie untransparenten künstlichen neuronalen Netzwerken, indem ein Modellierungskonzept zum Neuronalen Modellieren konstruiert wird. Dieses neuartige Konzept des Neuronalen Modellierens (CoNM) fungiert als flexibler und effizienter Ansatz zum Modellieren, Simulieren und Optimieren von Prozessen mit Hilfe von neuronalen Netzwerken und wird mittels einer Modellierungssprache, dessen mathematischen Formalisierung und technischen Substanziierung sowie einer Sammlung von neuartigen Subartefakten beschrieben. In der Verwendung derer Implementierung als CoNM-Werkzeuge können somit neue Arten einer Neuronalen-Prozess-Modellierung (NPM), Neuronalen-Prozess-Simulation (NPS) sowie Neuronalen-Prozess-Optimierung (NPO) realisiert werden. Die Wirksamkeit der erstellten Artefakte wurde anhand von sechs Experimenten demonstriert sowie in einem Simulator in realen Produktionsprozessen gezeigt.
T2  - Konzept des Neuronalen Modellierens
KW  - Deep Learning
KW  - Artificial Neuronal Network
KW  - Explainability
KW  - Interpretability
KW  - Business Process
KW  - Simulation
KW  - Optimization
KW  - Knowledge Management
KW  - Process Management
KW  - Modeling
KW  - Process
KW  - Knowledge
KW  - Learning
KW  - Enterprise Architecture
KW  - Industry 4.0
KW  - Künstliche Neuronale Netzwerke
KW  - Erklärbarkeit
KW  - Interpretierbarkeit
KW  - Geschäftsprozess
KW  - Simulation
KW  - Optimierung
KW  - Wissensmanagement
KW  - Prozessmanagement
KW  - Modellierung
KW  - Prozess
KW  - Wissen
KW  - Lernen
KW  - Enterprise Architecture
KW  - Industrie 4.0
Y1  - 2021
ER  -