Papers by Pekka Marttinen
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning (2023.eacl-main)
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| Challenge: | Recent advances apply artificial intelligence to predict clinical events or infer the probable diagnosis for clinical decision support. |
| Approach: | They propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning. |
| Outcome: | Experiments on clinical notes from the real-world MIMIC database show that the proposed model can achieve better performance than baselines and improve zero-shot prediction on unseen diagnoses. |
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery (2024.acl-srw)
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| Challenge: | State of the art Symbolic Regression (SR) methods build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. |
| Approach: | They propose a framework which iteratively refines a functional form with an LLM and determines its coefficients with an external optimizer. |
| Outcome: | The proposed method outperforms the best SR methods on four popular benchmarks while yielding simpler equations with better out of distribution generalization. |
Medical Code Assignment with Gated Convolution and Note-Code Interaction (2021.findings-acl)
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| Challenge: | Medical code assignment from clinical text is a longstanding challenge due to lengthy semantic information in medical notes. |
| Approach: | They propose a method to capture the semantic information of medical notes and a note-code interaction to automate medical code assignment from clinical text. |
| Outcome: | The proposed method outperforms state-of-the-art models on real-world clinical datasets and is on par with light-weighted baselines. |
Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning (2024.emnlp-main)
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| Challenge: | In-Context-learning and few-shot prompting are viable methods for compositional output generation but they are sensitive to the choice of support examples. |
| Approach: | They propose a method which generates supports and targets current state of the world and then uses them in-context-learning to solve a query. |
| Outcome: | The proposed agent improves performance on a previously unsolved compositional generalization test without loss of performance in other areas. |
Can docstring reformulation with an LLM improve code generation? (2024.eacl-srw)
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| Challenge: | Existing approaches focus on training, fine-tuning or prompting LLMs to generate better outputs given the same input. |
| Approach: | They propose to optimize part of the input, the docstring, via reformulation with an LLM to improve code generation. |
| Outcome: | The proposed methods improve code generation on the original HumanEval benchmark and multiple curated variants on the same input. |
Reader: Model-based language-instructed reinforcement learning (2023.emnlp-main)
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| Challenge: | Existing models of RL are limited and need to be re-trained for every new problem. |
| Approach: | They propose a model-based reinforcement learning approach to tackle the environment Read To Fight Monsters, a grounded policy learning problem. |
| Outcome: | The proposed approach performs better than existing model-free SOTA agents in the read to fight monsters environment and is more sample efficient than existing models. |
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection (2024.lrec-main)
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| Challenge: | Recent studies have used word embedding and deep learning to automate ADE detection from text, but they did not incorporate explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. |
| Approach: | They propose to integrate medical knowledge into ADE detection from text . they use contextualized embeddings from pretrained language models and convolutional graph neural networks to learn features differently for different types of nodes in the graph. |
| Outcome: | The proposed model outperforms existing models on four public datasets and shows that it is based on medical knowledge and embeddings from pretrained language models and neural networks. |