arXiv subjects

Natthanaphop Isaradech

Publications and source records attributed to Natthanaphop Isaradech.

6 recordsLinked to original sources

arXiv2026

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data

Evidence remains limited on how missing-data strategies affect the stability of clinical prediction models across different predictor-outcome relationships and degrees of missingness. We conducted a simulation study using a fully observed real-world cardiac cohort of 8,245 patients, equally divided into development and external validation cohorts. Missing data were induced under a missing-at-random mechanism across 18 scenarios varying by variable type, predictor-outcome relationship, and missingness proportion. Five strategies were compared: complete case analysis, multiple imputation by chained equations with fully conditional specification, multiple imputation using predictive mean matching, missForest, and k-nearest neighbours. Logistic regression models were developed using backward stepwise elimination. Outcomes included optimism-corrected AUC, calibration slope, mean absolute prediction error, external validation performance, and computation time. When missingness involved isolated linear or categorical variables at 30%-60%, all methods maintained discrimination comparable to the complete-data model, with median AUCs of about 0.75. When missingness involved isolated non-linear variables or more complex patterns, predictive performance and calibration worsened as missingness increased, especially at 90%. In complex scenarios, multiple imputation showed greater prediction instability and overfitting, while missForest performed well internally but overfitted externally. k-nearest neighbours showed the most consistent performance, with stable predictions, better external validation results, and the shortest computation time. The optimal strategy may depend on the characteristics of variables with missing data. In sufficiently large development samples, k-nearest neighbours may provide a computationally efficient alternative.

stat.ME↗

arXiv2026

A repeated k-fold cross-validation approach for evaluating the instability of clinical prediction models: an empirical comparison to the bootstrap approach

Bootstrap-based methods have been recommended for assessing prediction instability in clinical prediction models, but their performance relative to cross-validation (CV) remains unclear. We propose a CV-based approach for assessing prediction instability and compare it with a bootstrap-based approach in logistic regression and random forest models. We conducted a resampling-based empirical experiment using a clinical cohort of 19,418 emergency department patients. Development samples were generated under events-per-variable (EPV) scenarios of 10, 30, and 50, and results were compared with those from the full dataset. Models were evaluated using bootstrap validation and repeated 5-fold CV; nested CV was used for random forest tuning. Predictive performance was assessed using AUC, calibration slope, and calibration-in-the-large. Prediction instability was quantified using mean absolute prediction error (MAPE). For logistic regression, bootstrap validation and repeated 5-fold CV produced broadly similar discrimination and calibration, especially at higher EPV values. For random forest, apparent performance consistently overestimated empirical discrimination. Bootstrap validation and repeated 5-fold CV gave comparable discrimination, but repeated 5-fold CV produced calibration slope estimates closer to the empirical value. Prediction stability improved as EPV increased for both modelling approaches. At EPV 30, bootstrap-derived MAPE was higher than CV-derived MAPE for both logistic regression (median, 0.042 versus 0.020) and random forest (median, 0.077 versus 0.027). A CV-based approach can assess prediction instability while also providing internally validated performance. These findings support CV-based instability assessment as a practical alternative to bootstrap-based assessment, particularly when comparing instability across multiple modelling algorithms.

stat.ME↗

arXiv2026

Class Imbalance Corrections Failed to Enhance Discrimination, Model Calibration, and Prediction Stability: An Empirical Simulation Study Based on Clinical Dataset

Class imbalance is common when developing clinical prediction models (CPMs) and is often assumed to lead to poor predictive performance. Several methods have been proposed to correct data imbalance during CPM development. However, it remains unclear whether correcting class imbalance improves or harms CPM performance. This study investigated how imbalance correction affects classification performance and prediction stability. We simulated the development and internal validation of CPMs using penalised logistic regression under different imbalance-correction strategies, including algorithm-level rebalancing, data-level rebalancing by oversampling, and combined over- and under-sampling. The simulation dataset was derived from the GUSTO-I trial, which included 40,830 patients and 2,851 events. All imbalance-correction strategies were evaluated across sample-size scenarios ranging from 500 to 40,830. Model performance and prediction stability were assessed using 200 bootstrap resamples, including discrimination, calibration, calibration stability, mean absolute prediction error (MAPE), and classification instability index (CII). Class imbalance correction did not meaningfully improve model discrimination. Both data-level and algorithm-level correction led to miscalibration, risk overestimation, and increased prediction instability, as shown by prediction stability, MAPE, and CII plots, compared with models developed without correction. These findings suggest that class imbalance correction does not necessarily improve CPM performance and may compromise calibration and prediction stability. Class imbalance should not be treated as a pathology that automatically requires correction. In clinical prediction modelling, routine imbalance correction by default is generally not advisable.

stat.ME↗

arXiv2026

Influence of continuous predictor modelling methods on prediction stability in clinical prediction model development: an empirical comparison using real clinical data

Background and objective: Prediction stability is increasingly recognised as important for reliable clinical prediction model development, but the effect of continuous predictor modelling choices is unclear. This study examined how approaches to modelling continuous predictors influence prediction stability. Methods: We used a real clinical dataset of 19,418 emergency department patients to create five sample size scenarios ranging from 437 to 8,739 patients. Six methods were compared: dichotomisation at the median (DIC), tertile categorisation (TER), linear terms (LIN), quadratic terms (QUA), multivariable fractional polynomials (MFP), and extreme gradient boosting (XGB). Prediction stability was evaluated using a bootstrap-based framework. Optimism-corrected AUC and calibration were estimated through internal validation. A method was considered stable when at least 90% of individual predictions had a mean absolute prediction error (MAPE) <=5%. Results: Stability increased with sample size and varied by method. At n = 437, no method met the stability criterion; LIN was the most stable, followed by DIC. At n = 874, DIC and LIN achieved stable predictions with similar calibration, although DIC had lower AUC. At n = 1,748, QUA achieved stability, whereas MFP and XGB did not. At n = 3,496 and n = 8,739, all methods achieved stability. LIN, QUA, MFP, and XGB generally had higher AUCs than DIC and TER, while XGB showed the highest AUC but persistent miscalibration. Conclusion: Continuous predictor modelling methods appeared to influence prediction stability. LIN achieved stable predictions from the base sample size onwards, whereas QUA, MFP, and XGB required larger samples. Although XGB showed high discrimination, calibration concerns persisted. These findings suggest that, in smaller datasets, simpler approaches, particularly LIN, may provide more stable predictions.

stat.ME↗

arXiv2024

Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions using ChatGPT

Introduction: Medication prescriptions are often in free text and include a mix of two languages, local brand names, and a wide range of idiosyncratic formats and abbreviations. Large language models (LLMs) have shown promising ability to generate text in response to input prompts. We use ChatGPT 3.5 to automatically structure and expand medication statements in discharge summaries and thus make them easier to interpret for people and machines. Methods: Named-entity Recognition (NER) and Text Expansion (EX) are used in a zero- and few-shot setting with different prompt strategies. 100 medication statements were manually annotated and curated. NER performance was measured by using strict and partial matching. For the task EX, two experts interpreted the results by assessing semantic equivalence between original and expanded statements. The model performance was measured by precision, recall, and F1 score. Results: For NER, the best-performing prompt reached an average F1 score of 0.94 in the test set. For EX, the few-shot prompt showed superior performance among other prompts, with an average F1 score of 0.87. Conclusion: Our study demonstrates good performance for NER and EX tasks in free-text medication statements using ChatGPT. Compared to a zero-shot baseline, a few-shot approach prevented the system from hallucinating, which would be unacceptable when processing safety-relevant medication data.

cs.CL↗

arXiv2023

Developing A Visual-Interactive Interface for Electronic Health Record Labeling: An Explainable Machine Learning Approach

Labeling a large number of electronic health records is expensive and time consuming, and having a labeling assistant tool can significantly reduce medical experts' workload. Nevertheless, to gain the experts' trust, the tool must be able to explain the reasons behind its outputs. Motivated by this, we introduce Explainable Labeling Assistant (XLabel) a new visual-interactive tool for data labeling. At a high level, XLabel uses Explainable Boosting Machine (EBM) to classify the labels of each data point and visualizes heatmaps of EBM's explanations. As a case study, we use XLabel to help medical experts label electronic health records with four common non-communicable diseases (NCDs). Our experiments show that 1) XLabel helps reduce the number of labeling actions, 2) EBM as an explainable classifier is as accurate as other well-known machine learning models outperforms a rule-based model used by NCD experts, and 3) even when more than 40% of the records were intentionally mislabeled, EBM could recall the correct labels of more than 90% of these records.

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