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  - JOUR
A1  - Datta, Suparno
A1  - Sachs, Jan Philipp
A1  - Freitas da Cruz, Harry
A1  - Martensen, Tom
A1  - Bode, Philipp
A1  - Morassi Sasso, Ariane
A1  - Glicksberg, Benjamin S.
A1  - Böttinger, Erwin
T1  - FIBER
BT  - enabling flexible retrieval of electronic health records data for clinical predictive modeling
JF  - JAMIA open
N2  - Objectives: 
The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. 

Materials and Methods: 
FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. 

Results:
Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case.

Conclusion: 
FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.
KW  - databases
KW  - factual
KW  - electronic health records
KW  - information storage and
KW  - retrieval
KW  - workflow
KW  - software/instrumentation
Y1  - 2021
U6  - https://doi.org/10.1093/jamiaopen/ooab048
SN  - 2574-2531
VL  - 4
IS  - 3
PB  - Oxford Univ. Press
CY  - Oxford
ER  -