scholarly journals Identifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study (Preprint)

2019 ◽  
Author(s):  
Bora Kim ◽  
Younghoon Kim ◽  
C Hyung Keun Park ◽  
Sang Jin Rhee ◽  
Young Shin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Rating Scale ◽  
Suicide Attempts ◽  
Preventive Intervention ◽  
Learning Model ◽  
Psychiatric Evaluation ◽  
Medical Outcomes ◽  
Deep Learning Model

BACKGROUND Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. OBJECTIVE The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. METHODS This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). RESULTS Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method’s lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). CONCLUSIONS The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.

scholarly journals Identifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study

10.2196/14500 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e14500
Author(s):  
Bora Kim ◽  
Younghoon Kim ◽  
C Hyung Keun Park ◽  
Sang Jin Rhee ◽  
Young Shin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Rating Scale ◽  
Suicide Attempts ◽  
Preventive Intervention ◽  
Learning Model ◽  
Psychiatric Evaluation ◽  
Medical Outcomes ◽  
Deep Learning Model

Background Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. Objective The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. Methods This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). Results Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method’s lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). Conclusions The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.

scholarly journals An Earthquake Fatalities Assessment Method Based on Feature Importance with Deep Learning and Random Forest Models

2019 ◽  
Vol 11 (10) ◽  
pp. 2727 ◽  
Author(s):  
Hanxi Jia ◽  
Junqi Lin ◽  
Jinlong Liu
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Regression Tree ◽  
Learning Model ◽  
Assessment Method ◽  
Classification And Regression Tree ◽  
Model Classification ◽  
China Mainland ◽  
Deep Learning Model ◽  
Earthquake Fatalities

This study aims to analyze and compare the importance of feature affecting earthquake fatalities in China mainland and establish a deep learning model to assess the potential fatalities based on the selected factors. The random forest (RF) model, classification and regression tree (CART) model, and AdaBoost model were used to assess the importance of nine features and the analysis showed that the RF model was better than the other models. Furthermore, we compared the contributions of 43 different structure types to casualties based on the RF model. Finally, we proposed a model for estimating earthquake fatalities based on the seismic data from 1992 to 2017 in China mainland. These results indicate that the deep learning model produced in this study has good performance for predicting seismic fatalities. The method could be helpful to reduce casualties during emergencies and future building construction.

Prediction of the breakthrough cancer pain using deep learning model.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e24085-e24085
Author(s):  
Yeong Hak Bang ◽  
Yoon Ho Choi ◽  
Mincheol Park ◽  
Geon Hee Lee ◽  
Soo-yong Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Pain ◽  
Rating Scale ◽  
Numerical Rating Scale ◽  
Onset Time ◽  
Learning Model ◽  
Solid Cancer ◽  
Test Dataset ◽  
Breakthrough Cancer Pain ◽  
Deep Learning Model

e24085 Background: Breakthrough cancer pain (BTcP), a transitory flare of pain that occurs on a background of relatively well-controlled baseline pain, is a challenging clinical problem in managing cancer pain. We hypothesized that the BTcP could be predictable according to the patients’ previous observed patterns. In this study, we report on the development of a deep learning model that predicts hourly individual-level breakthrough pain for patients with cancer. Methods: We defined the BTcP as the pain with numerical rating scale (NRS) score 4 or above and developed models predicting the onset time of BTcP with the temporal resolution of 1 hour. The datasets which have more than 20 records of NRS score during hospitalization were included in our study. All the pain records were obtained from patients hospitalized on the wards of hematology-oncology in Samsung Medical Center between July 2016 to February 2020. The model used the time windows of 3 days to predict NRS scores over the next 24 hours. To capture irregular pain patterns, we created the sequence of average pain patterns over 24 hours from the previous 3 days and used it for normalization. We trained a Bi-directional long-short term memory (LSTM) based deep learning model. The model was validated using the holdout method with 20% of the datasets. Its performance was assessed with the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AURPC). Results: We included pain log data containing 2,905 admissions from 2,176 patients with solid cancer and 1,755 admissions from 1,082 patients with hematologic cancer in the analysis. The median age was 57 (interquartile range (IQR), 47-64), the most frequent type of cancer was lung cancer (18.0%), and most patients had stage 4 (60.7%). Among the 103,948 hours from patients in whole datasets, 1,091 (4.7%) hours were labeled as the period of BTcP. The patients have the records of NRS score with a median of 3 (IQR, 2.0-4.5) and BTcP with a median of 1.1 (IQR, 0.5-2.0) per day. We allocated approximately 20% of patients (653 patients with 932 admissions) to the holdout test dataset. Our model showed the AUROC 0.719 and AUPRC 0.680 for predicting the BTcP in the test dataset. Conclusions: Our study showed that cancer pain could be predictive by using a deep learning model. Though our exploratory study has a limitation of generalizability, future warranted subgroup analysis and verification research could make our model more applicable in a real-world setting.

A deep learning survival model for triple-negative breast cancer patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12594-e12594
Author(s):  
Yiyue Xu ◽  
Bing Zou ◽  
Bingjie Fan ◽  
Wanlong Li ◽  
Shijiang Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Deep Learning ◽  
Random Forest ◽  
Cox Regression ◽  
Learning Model ◽  
Survival Model ◽  
Cox Regression Analysis ◽  
Validation Set ◽  
Deep Learning Model

e12594 Background: Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis. There is no reliable model for survival prediction of TNBC patients. The traditional Cox regression analysis with poor prediction power cannot satisfy the clinical needs. The purpose was to establish a deep learning model and develop a new prognostic system for TNBC patients. Methods: This study collected data of TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) program between 2010 and 2016. 70% were used to develop the deep learning model, 15% were used as the validation set, and 15% as the independent testing set. Then the concordance-index (c-index) and Brier score (IBS) were calculated and compared with the Cox regression analysis and random forest. Finally, according to the classification of the deep survival model, an individualized prognosis system was established. Results: A total of 37,818 patients were enrolled in this study. In the validation set, the c-index of the deep learning was 0.799, which was better than the traditional Cox regression model (0.774) and random forest (0.763). The independent testing set further proved the robustness of the deep survival model (c-index 0.788). The new prognosis system based on the deep survival model reached an area under the curve (AUC) of 0.805, which was better than the Tumor, Node, Metastases (TNM) staging system (0.771). Conclusions: Deep learning model had better prediction power than the Cox regression analysis and the random forest. The established prognosis system can better predict prognosis and aid individual risk stratification for TNBC patients patients.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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