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.

Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital

“…The clinical characteristics of the 559 stroke patients (421 CI, 98 ICH, 40 SAH; 224 women and 335 men) are summarized in table 1. The median (interquartile range) age was 77 (69-83), and LOS in the acute care ward 15 (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Regarding discharge destinations, 205 patients were discharged home, 122 patients were transferred to KRW, 81 nursing facilities, 79 long-term hospitals, and 72 patients died.…”

“…We hypothesized that we could make a good prediction model for our hospital using the DL framework, even with a small dataset. Therefore, we herein produced the prediction model using DL framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan, https://predictionone.sony.biz/) [19][20][21]24,25] with our dataset and compared the utility of the DLbased model to previously-reported multiple regression models. We also investigated our patients' characteristics admitted to KRW because our hospital is unique in that KRW is annexed to an acute care hospital.…”

“…Recently, deep learning (DL), contained in artificial intelligence, is attractive. DL is gradually starting to be used in neurosurgical diseases in decision making for spinal canal stenosis [ 17 ], predicting outcomes after stroke [ 18 , 19 ], predicting the occurrence of stroke from meteorological information [ 20 ], automated diagnosis of primary headaches from a Japanese medical questionnaire [ 21 ], pathological diagnosis [ 22 ], or radiomics studies of brain tumours [ 23 ]. However, there are no reports on the DL-based prediction model regarding the total FIM score after KRW admission.…”

Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage

“…Deep learning (DL), one of the machine learning, is recently attractive. DL is starting to be used in the neurosurgical situations in decision-making for spinal canal stenosis,[ 1 ] predicting outcomes after subarachnoid hemorrhage,[ 21 ] automated diagnosis of primary headache,[ 26 ] predicting the occurrence of stroke[ 25 ] and ambulance transport,[ 51 ] pathological diagnosis[ 33 ] or radiomics studies of brain tumors. [ 4 , 34 ] However, there are no reports on the DL-based outcome prediction of ICH, though studies using other machine learning methods, such as decision tree, random forest, support vector machine, and XGBoost, have been reported.…”

Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, “I Think I Have Influenza,” Contributes to the Positivity Rate

“…To reduce unnecessary RIDTs and prepare for the telemedicine era under the COVID-19 pandemic [4], we herein re-examined the medical interview's importance and its relationship to the positivity of RIDTs. Then we preliminarily built a prediction model for the positive rate of RIDTs using detail medical interview results and one of the automated artificial intelligence (AI) frameworks, Prediction One (Sony Network Communications Inc., Tokyo, Japan) [5][6][7][8][9][10][11].…”