Datasets:
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
medical
biomedicine
clinical-trials
large-language-models
supervised-finetuning
evidence-based-medicine
License:
File size: 4,165 Bytes
4766213 d5f425b 4766213 fdd6554 4766213 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
---
license: apache-2.0
language:
- en
tags:
- medical
- biomedicine
- clinical-trials
- large-language-models
- supervised-finetuning
- evidence-based-medicine
- trial-design
- systematic-review
config_names:
- study_search
- study_screening
- evidence_summarization
- trial_design
- sample_size_estimation
- trial_completion_assessment
configs:
- config_name: study_search
data_files:
- split: train
path: sft_study_search_data_cleaned.parquet
- config_name: study_screening
data_files:
- split: train
path: sft_study_screening_data.parquet
- config_name: evidence_summarization
data_files:
- split: train
path: sft_evidence_summarization_data.parquet
- config_name: trial_design
data_files:
- split: train
path: sft_design_data.parquet
- config_name: sample_size_estimation
data_files:
- split: train
path: sft_sample_size_data.parquet
- config_name: trial_completion_assessment
data_files:
- split: train
path: sft_trial_completion_assessment_data.parquet
---
# TrialPanorama: Supervised Fine-Tuning Data for Clinical Research LLMs
## Dataset Summary
**TrialPanorama SFT** is a large-scale, task-oriented **supervised fine-tuning (SFT) dataset** designed to train large language models for **end-to-end clinical research and trial development workflows**.
The dataset is derived from **TrialPanorama**, a structured clinical research resource aggregating **1.6M+ clinical trial records** across global registries and linking them with biomedical ontologies and supporting literature. It focuses on transforming raw clinical trial data and curated evidence into **instruction–response pairs** that reflect realistic, expert-level research tasks.
The dataset supports training LLMs to operate as **clinical research assistants** capable of systematic literature review, trial design reasoning, and evidence-based decision making.
---
## Supported Training Tasks
Each task is released as a separate dataset configuration.
| Task (config) | Description |
|---|---|
| `study_search` | Given a clinical research question, retrieve and justify relevant studies from large trial and literature corpora. |
| `study_screening` | Perform inclusion/exclusion decisions for candidate studies based on eligibility criteria and study metadata. |
| `evidence_summarization` | Synthesize structured and unstructured trial evidence into concise, faithful summaries. |
| `trial_design` | Generate or refine clinical trial designs, including arms, interventions, and eligibility criteria. |
| `sample_size_estimation` | Estimate appropriate sample sizes under specified statistical and design assumptions. |
| `trial_completion_assessment` | Assess trial completion likelihood and rationalize risks using trial design and historical evidence. |
---
## Data Characteristics
- **Instruction-following format** suitable for SFT
- Grounded in **real clinical trial records**
- Emphasizes **clinical reasoning**, not surface text generation
- Covers both **systematic review** and **trial design & optimization** tasks
- Designed to support **generalist and agentic LLM training**
All files are provided in **Apache Parquet** format.
---
## Typical Fields
Each record may include:
- Task-specific **instruction or prompt**
- Structured **context** (trial metadata, eligibility criteria, outcomes, phase)
- **Model response targets** written or validated by domain experts
- Task and difficulty metadata
Exact schemas vary by task.
---
## Intended Use
This dataset is intended for:
- **Supervised fine-tuning of LLMs** for clinical research tasks
- Training **research-oriented AI agents** for trial planning and evidence synthesis
- Building domain-adapted models for **systematic review automation**
- Academic benchmarking of clinical reasoning capabilities
### Not Intended For
- Model evaluation (see TrialPanorama benchmarks for evaluation)
- Clinical decision making
- Direct medical or regulatory use
---
## How to Load
Load a specific training task via its configuration name:
```python
from datasets import load_dataset
ds = load_dataset("TrialPanorama/dataset", "study_screening")
ds["train"]
|