kubeflow-pipelines/USE_CASES.md

Healthcare ML Use Cases & Datasets

Curated list of similar healthcare/biomedical use cases with publicly available datasets for training on RunPod.

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🔥 Priority 1: Ready to Train

1. Adverse Drug Event Classification

Dataset: Lots-of-LoRAs/task1495_adverse_drug_event_classification

• Task: Classify text for presence of adverse drug events
• Size: ~10K samples
• Labels: Binary (adverse event / no adverse event)
• Use Case: Pharmacovigilance, FDA reporting automation
• Model: Bio_ClinicalBERT

from datasets import load_dataset
ds = load_dataset("Lots-of-LoRAs/task1495_adverse_drug_event_classification")

2. PubMed Multi-Label Classification (MeSH)

Dataset: owaiskha9654/PubMed_MultiLabel_Text_Classification_Dataset_MeSH

• Task: Assign MeSH medical subject headings to research articles
• Size: ~50K articles
• Labels: Multi-label (medical topics)
• Use Case: Literature categorization, research discovery
• Model: PubMedBERT

from datasets import load_dataset
ds = load_dataset("owaiskha9654/PubMed_MultiLabel_Text_Classification_Dataset_MeSH")

3. Symptom-to-Disease Prediction

Dataset: shanover/disease_symptoms_prec_full

• Task: Predict disease from symptom descriptions
• Size: Variable
• Labels: Disease categories
• Use Case: Triage, symptom checker apps
• Model: Bio_ClinicalBERT

from datasets import load_dataset
ds = load_dataset("shanover/disease_symptoms_prec_full")

4. Medical Triage Classification

Dataset: shubham212/Medical_Triage_Classification

• Task: Classify urgency level of medical cases
• Size: ~500 downloads (popular)
• Labels: Triage levels (Emergency, Urgent, Standard)
• Use Case: ER automation, telemedicine routing
• Model: Bio_ClinicalBERT

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📚 Priority 2: QA & Reasoning

5. MedMCQA - Medical Exam Questions

Dataset: openlifescienceai/medmcqa (24K downloads!)

• Task: Answer medical entrance exam questions
• Size: 194K MCQs covering 2.4K healthcare topics
• Labels: Multiple choice (A/B/C/D)
• Use Case: Medical education, knowledge testing
• Model: Llama-3 or Gemma (LLM fine-tuning)

from datasets import load_dataset
ds = load_dataset("openlifescienceai/medmcqa")

6. PubMedQA - Research Question Answering

Dataset: qiaojin/PubMedQA (18K downloads!)

• Task: Answer yes/no/maybe questions from abstracts
• Size: 274K samples
• Labels: yes / no / maybe
• Use Case: Evidence-based medicine, literature review
• Model: PubMedBERT or Bio_ClinicalBERT

from datasets import load_dataset
ds = load_dataset("qiaojin/PubMedQA")

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🧬 Priority 3: Specialized NLP

7. Medical Abbreviation Disambiguation (MeDAL)

Dataset: McGill-NLP/medal

• Task: Disambiguate medical abbreviations in context
• Size: 14GB → curated to 4GB
• Labels: Abbreviation meanings
• Use Case: Clinical note processing, EHR parsing
• Model: Bio_ClinicalBERT

8. BioInstruct - Instruction Following

Dataset: bio-nlp-umass/bioinstruct

• Task: Instruction-tuned biomedical tasks
• Size: 25K instructions
• Labels: Various biomedical tasks
• Use Case: General biomedical assistant
• Model: Llama-3 or Mistral (LoRA fine-tuning)

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🛠️ Implementation Roadmap

Week 1: Adverse Drug Events

1. Download ADE dataset
2. Add to handler.py as new training mode
3. Train classifier → S3
4. Build inference endpoint

Week 2: PubMed Classification

1. Download PubMed MeSH dataset
2. Multi-label classification head
3. Train → S3
4. Literature search API

Week 3: Medical QA

1. Download MedMCQA
2. LLM fine-tuning with LoRA
3. Deploy QA endpoint

Week 4: Symptom Checker

1. Symptom-disease dataset
2. Train classifier
3. Build symptom input → disease prediction API

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📊 Dataset Comparison

Dataset	Size	Task	Difficulty	Business Value
DDI (current)	176K	Classification	Medium	⭐⭐⭐⭐⭐
Adverse Events	10K	Binary	Easy	⭐⭐⭐⭐⭐
PubMed MeSH	50K	Multi-label	Medium	⭐⭐⭐⭐
MedMCQA	194K	MCQ	Hard	⭐⭐⭐⭐
PubMedQA	274K	Yes/No/Maybe	Medium	⭐⭐⭐⭐
Symptom→Disease	Varies	Classification	Easy	⭐⭐⭐⭐⭐
Triage	~5K	Classification	Easy	⭐⭐⭐⭐⭐

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🔗 Additional Resources

• MIMIC-III/IV: ICU clinical data (requires PhysioNet access)
• n2c2 Challenges: Clinical NLP shared tasks
• i2b2: De-identified clinical records
• ChemProt: Chemical-protein interactions
• BC5CDR: Chemical-disease relations

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Generated: 2026-02-03