Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data

Abstract

Purpose With the aging population in Japan, the prediction of ambulance transports is needed to save the limited medical resources. Some meteorological factors were risks of ambulance transports, but it is difficult to predict in a classically statistical way because Japan has 4 seasons. We tried to make prediction models for ambulance transports using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), with the meteorological and calendarial variables. Materials and Methods We retrospectively investigated the daily ambulance transports and meteorological data between 2017 and 2019. First, to confirm their association, we performed classically statistical analysis. Second, to test the DL framework’s utility for ambulance transports prediction, we made 3 prediction models for daily ambulance transports (total daily ambulance transports more than 5 or not, cardiopulmonary arrest (CPA), and trauma) using meteorological and calendarial factors and evaluated their accuracies by internal cross-validation. Results During the 1095 days of 3 years, the total ambulance transports were 5948, including 240 CPAs and 337 traumas. Cardiogenic CPA accounted for 72.3%, according to the Utstein classification. The relation between ambulance transports and meteorological parameters by polynomial curves were statistically obtained, but their r 2 s were small. On the other hand, all DL-based prediction models obtained satisfactory accuracies in the internal cross-validation. The areas under the curves obtained from each model were all over 0.947. Conclusion We could statistically make polynomial curves between the meteorological variables and the number of ambulance transport. We also preliminarily made DL-based prediction models. The DL-based prediction for daily ambulance transports would be used in the future, leading to solving the lack of medical resources in Japan.

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.…”

Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

“…AI will surpass human wisdom, and the technology is gradually used in the neurosurgical fields [9,10,[23][24][25][26][27][28][29][30][31]. However, the majority of medical staff cannot treat AI technology.…”

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.…”