# Condensed Memory Networks for Clinical Diagnostic Inferencing

arXiv: https://arxiv.org/abs/1612.01848 · HTML: https://mlsanity.com/paper/1612.01848/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2016.

## Built on by (most cited first, 14 of 14)

- [Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study](https://mlsanity.com/paper/2103.06511/) (2021)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Towards BERT-based Automatic ICD Coding: Limitations and Opportunities](https://mlsanity.com/paper/2104.06709/) (2021)
- [TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding](https://mlsanity.com/paper/2104.10652/) (2021)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)
- [An Explainable CNN Approach for Medical Codes Prediction from Clinical Text](https://mlsanity.com/paper/2101.11430/) (2021)
- [Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review](https://mlsanity.com/paper/2412.18043/) (2024)
- [Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis](https://mlsanity.com/paper/2101.11374/) (2021)
- [Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding](https://mlsanity.com/paper/2411.06823/) (2024)
- [Few-Shot Electronic Health Record Coding through Graph Contrastive Learning](https://mlsanity.com/paper/2106.15467/) (2021)
- [Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification](https://mlsanity.com/paper/2311.16650/) (2023)
- [Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes](https://mlsanity.com/paper/2403.14327/) (2024)
- [Effective Medical Code Prediction via Label Internal Alignment](https://mlsanity.com/paper/2305.05162/) (2023)
- [AHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction](https://mlsanity.com/paper/2407.17442/) (2024)

## Abstract (arXiv)

Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.