Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (2021.eacl-main)
Copied to clipboard
Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Loeser
| Challenge: | Clinical decision support systems can help in situations where the patient's development is predicted based on textual data. |
| Approach: | They propose to use clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources into the models. |
| Outcome: | The proposed model improves performance against several baselines and demonstrates that it is transferable and can be used in clinical decision support systems. |
Similar Papers
Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
Copied to clipboard
Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
| Challenge: | a pivotal dataset for clinical NLP research was released in 2016 . public access to such datasets is limited due to privacy and ethical concerns . |
| Approach: | They propose a novel clinical outcome prediction dataset based on MIMIC-IV . they provide initial insights into the performance of models trained on MIDIC-III . |
| Outcome: | The proposed dataset aims to probe the robustness and generalization of clinical outcome prediction models . the study focuses on challenges tied to evolving documentation standards and changing codes in the ICD taxonomy . |
Literature-Augmented Clinical Outcome Prediction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to clinical outcome prediction use only clinical notes and general biomedical literature. |
| Approach: | They propose to retrieve patient-specific medical literature and incorporate it into predictive models by combining clinical notes with language models. |
| Outcome: | The proposed approach boosts predictive performance on three important clinical tasks in comparison to strong LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%. |
Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction (2022.coling-1)
Copied to clipboard
| Challenge: | Clinical language models may suffer from imbalanced vocabulary for describing diseases or symptoms. |
| Approach: | They propose to augment clinical text with potentially complementary diagnostic codes from prior admissions or as they emerge during differential diagnosis to improve the performance. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art PubMedBERT by up 3% points. |
PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focused on time series data but ignored clinical notes . fusion of multi-modal features of patients from different views is not feasible due to the time series and clinical notes data being stored as time series. |
| Approach: | They propose to combine time series and clinical notes to fuse multi-modal features of patients from different perspectives using graph neural networks. |
| Outcome: | The proposed method is superior to existing models on MIMIC-III benchmark. |
Predicting in-hospital mortality by combining clinical notes with time-series data (2021.findings-acl)
Copied to clipboard
| Challenge: | In intensive care units, patient health is monitored through vital signals and clinical notes . previous work focused on predicting patient health using time-series data gathered from medical devices . |
| Approach: | They propose a model that combines clinical notes and vital data to make accurate mortality predictions. |
| Outcome: | The proposed model achieves an AUC score of 0.9, compared to the previous 0.87 . it can be used to make accurate in-hospital mortality predictions . |
Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
Copied to clipboard
| Challenge: | Clinical coding is labor-intensive and prone to delays, leading to global backlogs. |
| Approach: | They propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
| Outcome: | The proposed approach reduces training time by over half on a standard evaluation dataset compared to current methods . it uses Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
Explainable Clinical Decision Support from Text (2020.emnlp-main)
Copied to clipboard
| Challenge: | Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. |
| Approach: | They propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model. |
| Outcome: | The proposed model achieves AUROCs of 0.75 and 0.78 on sepsis and mortality prediction. |
How to leverage the multimodal EHR data for better medical prediction? (2021.emnlp-main)
Copied to clipboard
| Challenge: | Using deep learning to improve healthcare is challenging due to the complexity of EHR data. |
| Approach: | They propose a method to integrate clinical notes from EHR and combine them with different data to improve prediction performance. |
| Outcome: | The proposed model outperforms the state-of-the-art method without clinical notes on two prediction tasks. |
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction (D19-62)
Copied to clipboard
Elena Alvarez-Mellado, Eben Holderness, Nicholas Miller, Fyonn Dhang, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
| Challenge: | Previously, readmission risk classifications rely on structured information, such as sociodemographic data, comorbidity codes and physiological variables. |
| Approach: | They propose to incorporate additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
| Outcome: | The proposed model incorporates topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | generative large language models are being investigated for complex medical tasks, but their effectiveness in real-world clinical applications remains underexplored. |
| Approach: | They propose to compare encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in a MIMIC-IV dataset. |
| Outcome: | The proposed benchmark compares encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in the MIMIC-IV dataset. |