Abstract 177: Predictors Of All-cause Mortality In Young Onset Stroke: An Artificial Neural Network Analysis Using A Nationwide Cohort
Bibliographic record
Abstract
Background: It is crucial for providers to screen high risk patients with limited data being available on young onset stroke (YOS). We aimed to determine predictors of all-cause mortality in this population using the Artificial Neural Network (ANN) model in a national cohort. Methods: We identified young adult (18-44 yrs) YOS hospitalizations from the National Inpatient Sample (2018). ANN’s predictive factors were selected for all-cause mortality. YOS admissions were randomly split between training (70%) & testing datasets (30%). Training data was used to calibrate ANN while testing data was used to evaluate accuracy of the algorithm. We compared the frequency of incorrect prediction between training and testing data and measured area under the Receiver Operating Curve (AUC) to determine ANN’s efficacy in predicting in-hospital mortality in YOS. Results: The 2018 YOS cohort consisted of 39,040 admissions with a mean age of 36 ± 6 years (50.1% male, 51.1% white, 26.2% black, 15.5% Hispanic, 3.2% Asian or Pacific islanders) patients). The all-cause-in-hospital mortality was 5.3%. Training data showed an improved lower predictions in testing model vs testing (5.0% vs 5.2% error rate) , thereby depicting better accuracy. Normalized predictors are displayed in Figure 1a. The AUC was 0.82 (Fig 1b) which shows an excellent ANN model for inpatient mortality in YOS patients. Conclusion: The ANN model successfully revealed the order of prevalent predictors for all-cause mortality that can eventually be utilized to improve survival in high-risk patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".