Project Title
Classification and Comparative Analysis of In-Hospital Suicidal Behaviors of Patients Using Neural Networks And NLP Techniques
Academic department under which the project should be listed
CCSE - Computer Science
Research Mentor Name
Md Abdullah Al Hafiz Khan
Abstract (300 words maximum)
Suicide, an alarming public health, is one of the top 20 problems in the United States that leaves a lasting impact on families and communities. After a two years decline, a total of 47,000 people committed suicide last year. According to the US CDC report, a person expires every 11 minutes due to attempting suicide. Suicidal behavior information in the health records will help to understand the mental health situation of a patient. By identifying the patients who are ideating and are anticipating attempting suicide using the growing technology, physicians can help the patients' lives by keeping close monitoring. As part of the Phase-I of this project, we built a simple LSTM model on the extracted ScAN [1] (Suicide Attempt and Ideation Events Dataset) data, achieving 94.83% accuracy in predicting the Suicide Attempt (SA) and Suicide Ideation (SI) classes. In the next phase(s), we will apply complex and state-of-the-art model architectures, such as Capsule Neural Networks and EXAM-a three-layer architecture model, for classifying the suicide annotated data.
Disciplines
Artificial Intelligence and Robotics | Data Science | Psychiatry and Psychology | Psychology | Public Health
Classification and Comparative Analysis of In-Hospital Suicidal Behaviors of Patients Using Neural Networks And NLP Techniques
Suicide, an alarming public health, is one of the top 20 problems in the United States that leaves a lasting impact on families and communities. After a two years decline, a total of 47,000 people committed suicide last year. According to the US CDC report, a person expires every 11 minutes due to attempting suicide. Suicidal behavior information in the health records will help to understand the mental health situation of a patient. By identifying the patients who are ideating and are anticipating attempting suicide using the growing technology, physicians can help the patients' lives by keeping close monitoring. As part of the Phase-I of this project, we built a simple LSTM model on the extracted ScAN [1] (Suicide Attempt and Ideation Events Dataset) data, achieving 94.83% accuracy in predicting the Suicide Attempt (SA) and Suicide Ideation (SI) classes. In the next phase(s), we will apply complex and state-of-the-art model architectures, such as Capsule Neural Networks and EXAM-a three-layer architecture model, for classifying the suicide annotated data.