Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire

Abstract

Background: Primary headaches are widespread and costly public health problems. However, there are insufficient medical resources for their treatment in Japan due to two reasons. First, the numbers of headache specialists and clinics remain insufficient. Second, neurologists and neurosurgeons mainly treat headaches in Japan. However, they mainly work as general stroke neurologists, so they cannot focus on primary headache treatment. To solve these problems, we preliminarily developed a deep learning (DL)-based automated diagnosis model from patients’ Japanese unstructured sentences in the medical questionnaire using a DL framework. We hypothesized that the model would reduce the time and burden on both doctors and patients and improve their quality of life. Methods: We retrospectively investigated our primary headache database and developed a diagnosis model using the DL framework (Prediction One, Sony Network Communications Inc., Japan). We used age, sex, date, and embedding layer made by the medical questionnaire’s natural language processing (NLP). Results: Eight hundred and forty-eight primary headache patients (495 women and 353 men) are included. The median (interquartile range) age was 59 (40–74). Migraine accounted for 46%, tension-type headache for 47%, trigeminal autonomic cephalalgias for 5%, and other primary headache disorders for 2%. The accuracy, mean precision, mean recall, and mean F value of the developed diagnosis model were 0.7759, 0.8537, 0.6086, and 0.6353, which were satisfactory. Conclusion: The DL-based diagnosis model for primary headaches using the raw medical questionnaire’s Japanese NLP would be useful in performing efficient medical practice after ruling out the secondary headaches.

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.…”