Predicting Postoperative Cochlear Implant Performance Using Supervised Machine Learning
Bibliographic record
Abstract
OBJECTIVES: To predict postoperative cochlear implant performance with heterogeneous text and numerical variables using supervised machine learning techniques. STUDY DESIGN: A supervised machine learning approach comprising neural networks and decision tree-based ensemble algorithms were used to predict 1-year postoperative cochlear implant performance based on retrospective data. SETTING: Tertiary referral center. PATIENTS: One thousand six hundred four adults who received one cochlear implant from 1989 to 2019. Two hundred eighty two text and numerical objective demographic, audiometric, and patient-reported outcome survey instrument variables were included. OUTCOME MEASURES: Outcomes for postoperative cochlear implant performance were discrete Hearing in Noise Test (HINT; %) performance and binned HINT performance classification ("High," "Mid," and "Low" performers). Algorithm performance was assessed using hold-out validation datasets and were compared using root mean square error (RMSE) in the units of the target variable and classification accuracy. RESULTS: The neural network 1-year HINT prediction RMSE and classification accuracy were 0.57 and 95.4%, respectively, with only numerical variable inputs. Using both text and numerical variables, neural networks predicted postoperative HINT with a RMSE of 25.0%, and classification accuracy of 73.3%. When applied to numerical variables only, the XGBoost algorithm produced a 1-year HINT score prediction performance RMSE of 25.3%. We identified over 20 influential variables including preoperative sentence-test performance, age at surgery, as well as specific tinnitus handicap inventory (THI), Short Form 36 (SF-36), and health utilities index (HUI) question responses as the highest influencers of postoperative HINT. CONCLUSION: Our results suggest that supervised machine learning can predict postoperative cochlear implant performance and identify preoperative factors that significantly influence that performance. These algorithms can help improve the understanding of the diverse factors that impact functional performance from heterogeneous data sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".