Data visualization and predictive data mining to identify high risk suicidal patients with psychiatric disorder
Bibliographic record
Abstract
Suicide is a global health issue that involves the biological, social, cultural, spiritual, and psychological state of an individual in addition to many other factors which interact and lead a person to Suicidal Ideation (SI) and Suicide Attempt (SA). Over the last decade, with the advent of large medical databases, there has been a tremendous rise in the use of Business Intelligence (BI) in the healthcare sector. Healthcare uses BI tools to transform raw data into meaningful information to extract the potential value of historical data. Timely diagnoses of mental health problems can assist experts to address it at an early stage and enhance patient’s quality of life. There is a critical need to examine the fundamental psychological well-being issues among the worldwide population, which may develop into more complex issues, if not considered at an early period. This research focuses on two main components: Data visualization and Predictive model. First, a mental health dashboard is created using an end to end approach in which mental health data is pre-processed, integrated, and visualized in the form of several reports. These reports display the aggregated results in visually appealing formats (i.e., graphs, tables, pie charts, and line graphs) and allow navigation to finer granularity reports via drill down and drill through reports. Second, a predictive model is built to forecasts Suicide Attempts (SA). Ontario Mental Health Reporting System (OMHRS) database obtained from CIHI (Canadian Institute for Health Information) is used to train and test the predictive model. This model uses advanced data mining algorithms, including Artificial Neural Networks, Decision trees, and regression. The outcomes of different data mining algorithms are compared with actual values to determine the accuracy of the model. In addition, a web form is created, which takes input from the user and calculates the probability of SA for a given patient. The objective of this research is to provide a better understanding of trends, outliers, and patterns to enable healthcare providers to make more informed decisions and decrease mortality rate due to suicide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".