A shallow convolutional neural network (CNN), TextCNN, has become nearly ubiquitous for classification among clinical and medical text. This research presents a novel eXplainable-AI (X-AI) software, Red Flag/Blue Flag (RFBF), designed for classification with TextCNN. RFBF visualizes each convolutional filter's discriminative capability. This is a more informative approach than direct assessment of logit contribution, features that overfit to train set nuances on smaller datasets may indiscriminately activate large logits on validation samples from both classes. RFBF enables model diagnosis, term feature verification, and overfit prevention. We present 3 use cases of (1) filter consistency assessment; (2) predictive performance improvement; and (3) estimation of information leakage between train and holdout sets. The use cases derive from experiments on TextCNN for binary prediction of surgical misadventure outcomes from physician-authored operative notes. Due to TextCNN's prevalence, this X-AI can benefit clinical text research, and hence improve patient outcomes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841396PMC
http://dx.doi.org/10.1093/jamiaopen/ooac112DOI Listing

Publication Analysis

Top Keywords

red flag/blue
8
flag/blue flag
8
flag visualization
4
visualization common
4
common cnn
4
cnn text
4
text classification
4
classification shallow
4
shallow convolutional
4
convolutional neural
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!