A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Huiling Shao; Wing Chi Lawrence Chan; Heng Du; Xiangyan Fiona Chen; Qilin Ma; Zhiyu Shao

doi:10.1177/20552076221149528

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Digit Health. 2023 Jan 3:9:20552076221149528. doi: 10.1177/20552076221149528. eCollection 2023 Jan-Dec.

Authors

Huiling Shao¹, Wing Chi Lawrence Chan¹, Heng Du¹, Xiangyan Fiona Chen¹, Qilin Ma², Zhiyu Shao²

Affiliations

¹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
² Department of Neurology, The First Affiliated Hospital of Xiamen University, Xiamen, China.

Abstract

Background: Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice.

Methods: Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of $189$ patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC).

Results: In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, $p = 0.0192$ ).

Conclusions: Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.

Keywords: Ischemic stroke; decision support systems; machine learning; neuroimaging; translational medicine.