Preliminary development of a prediction model for daily stroke occurrences based on meteorological and calendar information using deep learning framework (Prediction One; Sony Network Communications Inc., Japan)

Abstract

Background: Chronologically meteorological and calendar factors were risks of stroke occurrence. However, the prediction of stroke occurrences is difficult depending on only meteorological and calendar factors. We tried to make prediction models for stroke occurrences using deep learning (DL) software, Prediction One (Sony Network Communications Inc., Tokyo, Japan), with those variables.

Methods: We retrospectively investigated the daily stroke occurrences between 2017 and 2019. We used Prediction One software to make the prediction models for daily stroke occurrences (present or absent) using 221 chronologically meteorological and calendar factors. We made a prediction models from the 3-year dataset and evaluated their accuracies using the internal cross-validation. Areas under the curves (AUCs) of receiver operating characteristic curves were used as accuracies.

Results: The 371 cerebral infarction (CI), 184 intracerebral hemorrhage (ICH), and 53 subarachnoid hemorrhage patients were included in the study. The AUCs of the several DL-based prediction models for all stroke occurrences were 0.532-0.757. Those for CI were 0.600-0.782. Those for ICH were 0.714-0.988.

Conclusion: Our preliminary results suggested a probability of the DL-based prediction models for stroke occurrence only by meteorological and calendar factors. In the future, by synchronizing a variety of medical information among the electronic medical records and personal smartphones as well as integrating the physical activities or meteorological conditions in real time, the prediction of stroke occurrence could be performed with high accuracy, to save medical resources, to have patients care for themselves, and to perform efficient medicine.

Keywords: Artificial intelligence; Calendar factors; Deep learning; Meteorological factors; Stroke.

Figure 1:

(a) Miyagi prefecture is located in the northeastern part of Japan. (b) Map of Miyagi prefecture. The Ishinomaki, Tome, and Kesennuma medical area, with background populations of about 350,000 people, is inside the thick line and colored with light green. The blue square is Japanese Red Cross Ishinomaki Hospital (HP), where both intravenous (IV) rt-PA and thrombectomy capable in this medical area. The blue triangle (Senseki HP) and the red triangle (Kesennuma City HP, where this study was performed) are IV rt-PA capable hospitals.

Figure 2:

Against the stroke occurrence (present or not), the area under the curves of predicted numbers of stroke occurrences, probability of each number of stroke patients as 0, 1, 2, 3, and 4, and the expected value were 0.693, 0.243, 0.717, 0.589, 0.532, 0.580, and 0.693, respectively.

Figure 3:

Against the cerebral infarction (CI) occurrence (present or not), the area under the curves of predicted numbers of CI occurrence, probability of each number of CI patients from 0, 1, 2, to 3, and the expected value were 0.688, 0.218, 0.773, 0.609, 0.600, and 0.768, respectively.

Figure 4:

Against the intracerebral hemorrhage (ICH) occurrence (present or not), the area under the curves of predicted numbers of ICH occurrences, probability of each number of ICH patients from 0, 1, 2, to 3, and the expected value were 0.988, 0.262, 0.737, 0.726, 0.714, and 0.731, respectively.