Sang Min Sung1, Yoon Jung Kang2, Han Jin Cho2, Nae Ri Kim2, Suk Min Lee2, Byung Kwan Choi3, Giphil Cho4. 1. Stroke Center, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea; Department of Neurology, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea; Biomedical Research Institute, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea. Electronic address: aminoff@hanmail.net. 2. Stroke Center, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea; Department of Neurology, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea. 3. Stroke Center, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea; Department of Neurosurgery, Pusan National University Hospital, School of Medicine, Busan, Republic of Korea. 4. Finance Fishery Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan, Republic of Korea.
Abstract
OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (END). The purpose of this study was to evaluate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. PATIENTS AND METHODS: We collected clinical and neuroimaging information from patients with acute minor stroke with NIHSS score of ≤ 3. Early neurological deterioration was defined as any worsening of NIHSS score within 3 days after admission. Unfavorable functional outcome was defined as a modified Rankin Scale score of ≥ 2. We also compared clinical and neuroimaging information between patients with and without END. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep neural network, and Logistic Regression, were selected and trained by our dataset to predict early neurological deterioration RESULTS: A total of 739 patients were included in this study. 78 patients (10.6%) experienced END. Among 78 patients with END, 61 (78.2%) had unfavorable functional outcome at 90 days after stroke onset. On multivariate analysis, the initial NIHSS score (P = 0.003), hemorrhagic transformation (P = 0.010), and stenosis (P = 0.014) or occlusion (P = 0.004) of a relevant artery were independently associated with END. Of the four machine learning algorithms, Boosted trees, Deep neural network, and Logistic Regression can be used to predict END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and area under the curve = 0.934, Deep neural network :0.966, 0.8, and 0. 904, and Logistic Regression : 0.966, 0.8, and 0.885). CONCLUSIONS: This study suggests that machine learning algorithms that integrate clinical and neuroimaging information can be used to predict END in patients with acute minor stroke. Further studies based on larger, multicenter datasets are needed to predict END accurately for designing treatment strategies and obtaining favorable functional outcome.
OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (END). The purpose of this study was to evaluate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. PATIENTS AND METHODS: We collected clinical and neuroimaging information from patients with acute minor stroke with NIHSS score of ≤ 3. Early neurological deterioration was defined as any worsening of NIHSS score within 3 days after admission. Unfavorable functional outcome was defined as a modified Rankin Scale score of ≥ 2. We also compared clinical and neuroimaging information between patients with and without END. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep neural network, and Logistic Regression, were selected and trained by our dataset to predict early neurological deterioration RESULTS: A total of 739 patients were included in this study. 78 patients (10.6%) experienced END. Among 78 patients with END, 61 (78.2%) had unfavorable functional outcome at 90 days after stroke onset. On multivariate analysis, the initial NIHSS score (P = 0.003), hemorrhagic transformation (P = 0.010), and stenosis (P = 0.014) or occlusion (P = 0.004) of a relevant artery were independently associated with END. Of the four machine learning algorithms, Boosted trees, Deep neural network, and Logistic Regression can be used to predict END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and area under the curve = 0.934, Deep neural network :0.966, 0.8, and 0. 904, and Logistic Regression : 0.966, 0.8, and 0.885). CONCLUSIONS: This study suggests that machine learning algorithms that integrate clinical and neuroimaging information can be used to predict END in patients with acute minor stroke. Further studies based on larger, multicenter datasets are needed to predict END accurately for designing treatment strategies and obtaining favorable functional outcome.