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Articles by Dingmin Wang
Development and validation of a machine learning model for predicting 3-year recurrence of adenomatous colorectal polyps after EMR: a multicenter study
Abstract Objective: This study aimed to identify factors associated with adenoma recurrence within three years after endoscopic mucosal resection (EMR) and to develop an individualized predictive model. Methods: Patients undergoing their first EMR for colorectal polyps at the Affiliated Hospital of Xuzhou Medical University (September 2018–May 2025) were retrospectively included and randomly divided into training and testing cohorts (7:3).
CodeStruct: Code agents over structured action spaces
LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting drift or ambiguous patterns. We propose reframing the codebase as a structured action space where agents operate on named AST entities rather than text spans. Our framework, CODESTRUCT, provides readCode for retrieving complete syntactic units and editCode for applying syntax-validated transformations to semantic program elements.
A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database
Abstract Objective To develop and validate the best machine learning model for predicting 28-day mortality in septic patients with alcoholic cirrhosis (AC), leveraging data from the Medical Information Mart for Intensive Care Database, 4th Edition (MIMIC-IV). Methods Clinical data of 1958 patients with AC complicated with sepsis were retrospectively extracted. Missing data (<20%) were addressed using Multiple Imputation by Chained Equations (MICE).
Fine-tuning language models for joint rewriting and completion of code with potential bugs
Handling drafty partial code remains a notable challenge in real-time code suggestion applications. Previous work has demonstrated shortcomings of large language models of code (CodeLLMs) in completing partial code with potential bugs. In this study, we view partial code as implementation hints and fine-tune CodeLLMs to jointly rewrite and complete partial code into functional full programs. We explore two strategies: one-pass generation and multi-pass iterative refinement.
[2406.09021] Contextual Distillation Model for Diversified Recommendation
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