Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection
This addresses feature selection challenges in clinical survey data analysis, but it is incremental as it applies an existing STS method to a new domain.
The paper tackled the problem of overfitting in machine learning models on clinical survey data with many features and few examples by using semantic textual similarity (STS) scores from feature names for feature selection, showing that STS-based selection can lead to higher model performance compared to traditional algorithms.
Survey data can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names and target names can be evaluated using language models (LMs) to produce semantic textual similarity (STS) scores, which can then be used to select features. We examine the performance using STS to select features directly and in the minimal-redundancy-maximal-relevance (mRMR) algorithm. The performance of STS as a feature selection metric is evaluated against preliminary survey data collected as a part of a clinical study on persistent post-surgical pain (PPSP). The results suggest that features selected with STS can result in higher performance models compared to traditional feature selection algorithms.