---
title: 'CHIP 2025 Shared Task 2: Discharge Med Rec'
url: https://www.emergentmind.com/topics/chip-2025-shared-task-2
type: topic
---

# CHIP 2025 Shared Task 2: Discharge Med Rec

The CHIP 2025 Shared Task 2 centered on discharge medication recommendation for metabolic diseases using real-world Chinese Electronic Health Records (EHRs). The task operationalized the multi-label problem of predicting appropriate discharge medications from heterogeneous, de-identified clinical narratives, leveraging recent advances in large language models (LLMs) and ensemble methodologies. With strong participation and measurable methodological improvements over established baselines, the competition provides a benchmark for algorithmic solutions to realistic, high-impact clinical decision support challenges in Chinese healthcare contexts [2511.06230].

## 1. Problem Scope and Clinical Motivation

Discharge medication recommendation is an integral component of chronic disease management. Chronic metabolic diseases, including diabetes, hypertension, and fatty liver disease, demand long-term, multifaceted pharmacotherapy often complicated by comorbidities and polypharmacy. At the point of hospital discharge, identifying an optimal subset of medications is critical for:

- Maintaining disease control and preventing exacerbations.
- Reducing hospital readmission rates and associated healthcare costs.
- Promoting patient safety by minimizing drug–drug interactions and dosing errors.

Formally, the shared task required, for each de-identified inpatient EHR containing demographic details, clinical course, laboratory values, prior medical history, and discharge diagnoses, the prediction of a subset $\hat y_i \subseteq \mathcal{D}$ where $\mathcal{D}$ is a vocabulary of 651 candidate medications. This effectively framed the problem as multi-label classification with a large, flat label space.

## 2. Dataset Construction: CDrugRed

CDrugRed—introduced for CHIP 2025 Shared Task 2—comprises 5,894 hospitalization records from 3,190 unique patients (spanning 2013–2023) at a top-tier tertiary hospital in China. Its key features are summarized below:

| Aspect                  | Description                                               | Notes                                                  |
|-------------------------|----------------------------------------------------------|--------------------------------------------------------|
| Records                 | 5,894 (from 3,190 patients)                              | Patient-level split to prevent leakage                 |
| Drug Vocabulary         | 651 distinct, most-used metabolic disease medications    | No hierarchical ontology provided                      |
| Input Modalities        | EHR text: demographics, vitals, admission info, labs, etc.| Structured clinical and narrative text                 |
| De-identification       | Automated PHI masking (large-model NER), normalization, clinician review | Ensured privacy and clinical term consistency    |
| Data Partitioning       | 60% train, 10% val, 30% test (by patient, not by record) | Standardized split across participants                 |

Candidate medications in the dataset are presented as a flat list; participants could, however, engineer auxiliary higher-level groupings (e.g., statins, antihypertensives) for feature construction.

## 3. Evaluation Metrics

Given the multi-label nature, the task adopted a comprehensive suite of metrics for single-example (per-record) and macro-averaged performance:

- **Jaccard Index**:
  $$
  \mathrm{Jaccard}(y_i,\hat y_i)=\frac{|y_i\cap \hat y_i|}{|y_i\cup \hat y_i|}
  $$
- **Precision**:
  $$
  \mathrm{Precision}(y_i,\hat y_i) = \frac{|y_i\cap \hat y_i|}{|\hat y_i|}
  $$
- **Recall**:
  $$
  \mathrm{Recall}(y_i,\hat y_i) = \frac{|y_i\cap \hat y_i|}{|y_i|}
  $$
- **F1 Score**:
  $$
  \mathrm{F1}(y_i,\hat y_i) = 2\;\frac{\mathrm{Precision}\times \mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}
  $$
- **Macro-averaged metrics** over $N$ records ($\mathrm{MacroP}$, $\mathrm{MacroR}$, $\mathrm{MacroF1}$).
- **Leaderboard Composite Score**:
  $$
  \mathrm{Score} = \frac{1}{2}\left(\mathrm{Jaccard}_{\rm avg} + \mathrm{MacroF1}\right)
  $$

Jaccard directly quantifies set overlap, while F1 balances precision (mitigating over-prescription) and recall (avoiding omission of necessary medications).

## 4. Methodological Landscape and Participant Solutions

Participants employed a spectrum of natural language processing and machine learning methods, summarized as follows:

| Approach                                   | Description                                                                                              |
|---------------------------------------------|----------------------------------------------------------------------------------------------------------|
| TF-IDF + Logistic Regression                | Classical baselines for sparse, bag-of-words features                                                    |
| Deep Neural Networks                       | CNN and RNN architectures for sequential modeling of input narratives                                     |
| Fine-tuned LLMs (Qwen, GLM4-9B-Chat)        | Pretrained transformers with LoRA or full supervised fine-tuning on CDrugRed                              |
| Prompt and Retrieval-Augmented Generation   | Input prompts engineered for better coverage and controlled decoding                                      |
| Data Augmentation                           | Order perturbation, pseudo labeling, oversampling of rare-drug examples                                   |
| Ensemble Methods                            | Temperature-diverse decoding, hierarchical and weighted voting among model outputs                        |

The top-ranked team (DeepDrug) delivered a generative Qwen LLM-based framework featuring:

- Multi-dimensional feature augmentation including drug-class annotations, explicit patient meta-features (e.g., age, BMI bin, comorbidities), and disease–drug co-occurrence as auxiliary prompts.
- Input robustness via stochastic order perturbation of diagnosis and medication lists during training.
- Fusion of multiple Qwen LLMs (varying in model size), integrated via a hierarchical weighted voting scheme with adaptive model weights calibrated by validation Jaccard.
- These innovations contributed to their leaderboard-leading Score of 0.5685 (Jaccard = 0.5102, F1 = 0.6267).

## 5. Competition Results and Empirical Findings

Results from both the validation (Phase A) and test (Phase B) leaderboards are as follows:

| Team/Method               | Jaccard | F1     | Score  | Δ (Top vs. Baseline) |
|---------------------------|---------|--------|--------|----------------------|
| Baseline (GLM4-9B-Chat + LoRA) | 0.4444  | 0.5621 | 0.5032 | —                    |
| DeepDrug (Phase A)        | 0.5102  | 0.6267 | 0.5685 | +6.58%, +6.45%       |
| Baseline (Phase B test)   | 0.4477  | 0.5648 | 0.5062 | —                    |
| DeepDrug (Phase B)        | 0.5102  | 0.6267 | 0.5685 | +6.25%, +6.21%       |

Top 10 teams’ scores were tightly distributed (0.5685–0.5226), indicating a competitive landscape and highlighting incremental, domain-specific advances.

Critical factors underpinning successful submissions included:

- Domain-aware prompt engineering, enriched input features (meta-data and expert-constructed drug groupings).
- Data-centric strategies addressing rare-class imbalance and robustness to input ordering.
- Application of ensemble techniques (weighted voting, two-stage fusion) to balance bias and variance.

Identified weaknesses included reduced recall for infrequently prescribed drugs, input order/format sensitivity, and occasional model hallucinations of drugs absent from the candidate vocabulary. The last was mitigated through post-hoc candidate filtering.

## 6. Remaining Challenges and Prospects for Development

Persistent challenges in algorithmic medication recommendation emerge from:

- Heterogeneity and noise within EHR narratives, varying across patients and hospital departments.
- High inter-patient variability (comorbidities, polypharmacy, individualized regimens).
- Pronounced label imbalance due to skew in drug prescription frequencies.
- Generalization limitations across healthcare institutions with divergent data schemas.

Proposed avenues for future research and dataset/benchmark expansion include:

- Scaling CDrugRed to incorporate multi-center data and broadening scope beyond metabolic diseases.
- Integrating multi-modal clinical data types (e.g., images, laboratory values) to enrich patient representation.
- Evolving the prediction target from medication name lists to full prescription regimens, encompassing dosage, frequency, route.
- Developing explainable models offering clinical rationales, for example by highlighting salient laboratory results or diagnoses.
- Embedding external pharmacological knowledge bases to enhance safety via automated drug-interaction checks.

## 7. Significance and Benchmark Status

CHIP 2025 Shared Task 2 and its CDrugRed corpus establish a high-quality benchmark for multi-label medication recommendation aligned with real-world Chinese hospital workflows. The task demonstrates empirically that fine-tuning LLMs with explicitly engineered features, data augmentation, and robust ensembling can achieve superior performance (up to a Jaccard of 0.5102 and F1 of 0.6267) compared to prior baselines. Nevertheless, the findings underscore unresolved issues of model generalization, explainability, and rare-drug detection, setting a clear research agenda for future clinical NLP studies in the Chinese context [2511.06230].

Source: https://www.emergentmind.com/topics/chip-2025-shared-task-2