scholarly journals Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Le Zheng ◽  
Oliver Wang ◽  
Shiying Hao ◽  
Chengyin Ye ◽  
Modi Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Suicide Attempt ◽  
Electronic Health Records ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Health Records ◽  
High Risk Patients ◽  
Risk Patients

Development and Implementation of an Early Warning System for High Risk Patients for Suicide Attempt Using Deep Learning and Electronic Health Records (Preprint)

2018 ◽  
Author(s):  
Le Zheng ◽  
Oliver Wang ◽  
Modi Liu ◽  
Chengyin Ye ◽  
Minjie Xia ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
High Risk ◽  
Suicide Attempt ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Health Records ◽  
Prospective Cohorts ◽  
One Year

BACKGROUND Suicide is the 10th leading cause of death in the US. Over the past 15 years, the total suicide rate has increased 24% from 10.5 to 13.0 per 100,000 people. In Massachusetts the rate of death by suicide is three times the rate of homicide deaths. Approximately 60% of suicides die on the first attempt. Of the remaining 40% who survive the index attempt and receive emergency or hospital level of care, rates of subsequent completed suicide are exceptionally high, ranging from 2.3% to 4%. A recent study determined that risk factors for repeat suicide attempt and suicide differed, with alcohol use, younger age and cluster B personality disorders among the attempters and older age and alcohol use among the suicide completers. This data is from a small sample in Catalonia, Spain and whether it is generalizable to populations in the USA is yet to be determined. An early warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and the contribution of repeated attempts to the risk of eventual death by suicide. OBJECTIVE In this study we sought to develop an early warning system (EWS) for high risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Importantly, this EWS was designed to support case managers, primary care and mental health care practitioners participating in accountable care programs. The continuous use of the system in this program will help assess the ongoing EWS effectiveness. METHODS Data from individual patient electronic health records (EHRs) from the Berkshire Health System located in Pittsfield, MA. Advanced machine-learning algorithms and Deep Neural Networks were utilized in the process of feature selection and model building. A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following one-year time period. Risk scores were subjected to individual level analysis in order to aid in the interpretation of the results for health care providers managing the at-risk cohorts. RESULTS The one-year suicide attempt risk model attained an area under the curve (AUC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the “very high risk” category was 60 times greater than the population baseline when tested in the prospective cohorts, 10 times greater in the “high risk” group, and 5 times greater in the “medium risk” bin. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socio-economic determinants were recognized as significant features associated with incident suicide attempt. CONCLUSIONS Utilizing a single EHR dataset, an EWS for the determination of risk for suicide attempt was successfully developed and prospectively validated using deep learning modeling techniques.

scholarly journals Best Paper Selection

2021 ◽  
Vol 30 (01) ◽  
pp. 282-282
Keyword(s):  
Primary Care ◽  
Deep Learning ◽  
Disease Surveillance ◽  
Early Warning System ◽  
Communicable Disease ◽  
Warning System ◽  
Surveillance Systems ◽  
Health Records ◽  
Community Perspectives ◽  
Risk Patients

Zheng L, Wang O, Hao S, Ye C, Liu M, Xia M, Sabo AL, Markovic L, Stearns F, Kanov L, Sylvester KL, Widen R, McElhinney DB, Zhang W, Liao J, Ling XB. Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033212/ Roope LSJ, Tonkin-Crine S, Herd N, Michie S, Pouwels KB, Castro-Sanchez E, Sallis A, Hopkins S, Robotham JV, Crook DW, Peto T. Peters M, Butler CC, Walker AS, Wordsworth S. Reducing expectations for antibiotics in primary care: a randomised experiment to test the response to fear-based messages about antimicrobial resistance. https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01553-6 Degeling C, Carter SM, van Oijen AM McAnulty J, Sintchenko V, Braunack-Mayer A, Yarwood T, Johnson J, Gilbert GL. Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: a report on four community juries. https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-020-00474-6

scholarly journals Introduce a Tool to Assess the Understanding of the Early Warning System in High-Risk Organizations

2020 ◽  
Author(s):  
Hamidreza Mehri ◽  
Faeze Sepahi Zoeram ◽  
Fatemeh Majidpour ◽  
Zainab Anbari Nogyni ◽  
Reza Jafari Nodoushan
Keyword(s):  
High Risk ◽  
Early Warning ◽  
Content Validity ◽  
Early Warning System ◽  
Good Condition ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Validity And Reliability ◽  
Spearman Test

Background: Although early warning system processes follow precise models and scenarios, the human part is not fully understood. Most people before and during crises, act according to their interpretive plans, sometimes when the situation may not be dangerous, but can lead to dangerous reactions. The purpose of this study was to provide an indicator that can be used to assess people's understanding of early warning systems. Methods: This study is a descriptive-analytical study that was conducted in 2019 in a gas refinery in Iran. In the first step, the Perception Index questionnaire was translated into Persian with the help of English language experts. In the next step, the validity and reliability of the questionnaire were assessed. The questionnaires were distributed and completed among 168 refinery personnel. The collected data were analyzed using SPSS software version 24, and Pearson and Spearman correlation coefficients were determined by statistical tests. Results: The content validity index was 0.8, and the content validity ratio was 0.66. The general index of perception of the rapid warning system in this industry was 71.74 percent. Pearson correlation test did not show a significant correlation between age and perception index (r = 0.060), and also this test showed a positive correlation between perception index and work experience (r = 0.691). Spearman test was used to examine the relationship between two variables of education level and perception index. The results showed that there was a strong correlation between these two variables (rho = 0.746). Conclusion: The results showed that the perception index in this questionnaire has high validity and reliability and can be used in high-risk industries. The general perception index gained in this industry was in good condition, which means that people are more likely to be well aware at the time of an accident and will behave appropriately. However, it is suggested that the managers of the industry understudy hold training classes related to the early warning systems, hold emergency maneuvers, and familiarize the personnel with different scenarios.  

Routine Bedside Use of Obstetric Early Warning System in the Postnatal Ward to Identify Maternal Morbidity Among High-Risk Women

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Sanna Hoppu ◽  
Katja Hannola ◽  
Susanna Mennander ◽  
Heini Huhtala ◽  
Maria Rissanen ◽  
...  
Keyword(s):  
High Risk ◽  
Early Warning ◽  
Early Warning System ◽  
Maternal Morbidity ◽  
Warning System ◽  
Postnatal Ward ◽  
High Risk Women

scholarly journals Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Rongxia Wang ◽  
Malik Bader Alazzam ◽  
Fawaz Alassery ◽  
Ahmed Almulihi ◽  
Marvin White
Keyword(s):  
Deep Learning ◽  
False Alarm ◽  
False Alarm Rate ◽  
Early Warning ◽  
Collision Detection ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Trajectory Prediction ◽  
Activation Area

Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.

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