Explainable Analytics to Predict the Quality of Life in Patients with Prostate Cancer from Longitudinal Data
Bibliographic record
Abstract
Prostate cancer (PCa) is a complicated cancer with high level of unexplained variability that might affect the patient’s health-related quality of life (HRQoL). Using 2670 patients’ information with 433 measures per patient, our objective is to identify the minimal set of important variables which can predict 1-year follow-up HRQoL for PCa patients while adding interpretability to the proposed model. We address three problems of dimension reduction, prediction, and interpretability by first developing deep neural networks on top of a clustering algorithm to extract minimal set of important variables of baseline visit. Second, we build a model to predict a 1-year follow-up of HRQoL for PCa patients using the extracted important baseline variables. Third, we utilize Bayesian networks method to provide insights into the proposed model results to discover the relationship between patients’ baseline variables and their 1-year follow-up satisfaction. The results support the use of the proposed machine-learning technique as an essential tool in identifying potential baseline variables for predicting 1-year HRQoL. Furthermore, our approach to interpret the findings will help to establish guidelines for a better shared decision-making platform for PCa patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".