# Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes

arXiv: https://arxiv.org/abs/2512.08261 · HTML: https://mlsanity.com/paper/2512.08261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot](https://mlsanity.com/paper/2502.04413/) (2025)
- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review](https://mlsanity.com/paper/2010.02809/) (2020)
- [Explainable artificial intelligence model to predict acute critical illness from electronic health records](https://mlsanity.com/paper/1912.01266/) (2019)
- [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://mlsanity.com/paper/2402.16829/) (2024)
- [Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study](https://mlsanity.com/paper/2308.14321/) (2023)
- [Automatic detection of rare pathologies in fundus photographs using few-shot learning](https://mlsanity.com/paper/1907.09449/) (2019)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [AffinityNet: semi-supervised few-shot learning for disease type prediction](https://mlsanity.com/paper/1805.08905/) (2018)
- [Granite Embedding Models](https://mlsanity.com/paper/2502.20204/) (2025)
- [Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records](https://mlsanity.com/paper/2403.04086/) (2024)
- [Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.06912/) (2023)
- [Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction](https://mlsanity.com/paper/2502.10689/) (2025)
- [Enabling Patient-side Disease Prediction via the Integration of Patient Narratives](https://mlsanity.com/paper/2405.02935/) (2024)

## Abstract (arXiv)

Predicting diseases solely from patient-side information, such as demographics and self-reported symptoms, has attracted significant research attention due to its potential to enhance patient awareness, facilitate early healthcare engagement, and improve healthcare system efficiency. However, existing approaches encounter critical challenges, including imbalanced disease distributions and a lack of interpretability, resulting in biased or unreliable predictions. To address these issues, we propose the Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework. KPI systematically integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy, which is particularly important for long-tailed diseases. Additionally, KPI utilizes large language models (LLMs) to generate patient-specific, medically relevant explanations, thereby improving interpretability and reliability. Extensive experiments on real-world datasets demonstrate that KPI outperforms state-of-the-art methods in predictive accuracy and provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.