Developing an efficient corpus using Ensemble Data cleaning approach
- URL: http://arxiv.org/abs/2406.00789v1
- Date: Sun, 2 Jun 2024 16:03:31 GMT
- Title: Developing an efficient corpus using Ensemble Data cleaning approach
- Authors: Md Taimur Ahad,
- Abstract summary: This research aims to clean a medical dataset using ensemble techniques and to develop a corpus.
The data cleaning method in this research suggests that the ensemble technique provides the highest accuracy (94%) compared to the single process.
It underscores the importance of NLP in the medical field, where accurate and timely information extraction can be a matter of life and death.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite the observable benefit of Natural Language Processing (NLP) in processing a large amount of textual medical data within a limited time for information retrieval, a handful of research efforts have been devoted to uncovering novel data-cleaning methods. Data cleaning in NLP is at the centre point for extracting validated information. Another observed limitation in the NLP domain is having limited medical corpora that provide answers to a given medical question. Realising the limitations and challenges from two perspectives, this research aims to clean a medical dataset using ensemble techniques and to develop a corpus. The corpora expect that it will answer the question based on the semantic relationship of corpus sequences. However, the data cleaning method in this research suggests that the ensemble technique provides the highest accuracy (94%) compared to the single process, which includes vectorisation, exploratory data analysis, and feeding the vectorised data. The second aim of having an adequate corpus was realised by extracting answers from the dataset. This research is significant in machine learning, specifically data cleaning and the medical sector, but it also underscores the importance of NLP in the medical field, where accurate and timely information extraction can be a matter of life and death. It establishes text data processing using NLP as a powerful tool for extracting valuable information like image data.
Related papers
- AI-assisted summary of suicide risk Formulation [0.9224875902060083]
This study describes how we developed advanced Natural Language Processing (NLP) algorithms, a branch of Artificial Intelligence (AI)
Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's problems.
arXiv Detail & Related papers (2024-11-29T16:40:28Z) - A Survey on Data Selection for Language Models [148.300726396877]
Data selection methods aim to determine which data points to include in a training dataset.
Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive.
Few organizations have the resources for extensive data selection research.
arXiv Detail & Related papers (2024-02-26T18:54:35Z) - LLMs Accelerate Annotation for Medical Information Extraction [7.743388571513413]
We propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating ground truth labels for medical text annotation.
We rigorously evaluate our method on a medical information extraction task, demonstrating that our approach not only substantially cuts down on human intervention but also maintains high accuracy.
arXiv Detail & Related papers (2023-12-04T19:26:13Z) - Into the Single Cell Multiverse: an End-to-End Dataset for Procedural
Knowledge Extraction in Biomedical Texts [2.2578044590557553]
FlaMB'e is a collection of expert-curated datasets that capture procedural knowledge in biomedical texts.
The dataset is inspired by the observation that one ubiquitous source of procedural knowledge that is described as unstructured text is within academic papers describing their methodology.
arXiv Detail & Related papers (2023-09-04T21:02:36Z) - Advancing Italian Biomedical Information Extraction with
Transformers-based Models: Methodological Insights and Multicenter Practical
Application [0.27027468002793437]
Information Extraction can help clinical practitioners overcome the limitation by using automated text-mining pipelines.
We created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Transformers-based model.
The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "low-resource" approach.
arXiv Detail & Related papers (2023-06-08T16:15:46Z) - Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning.
They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health.
Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
arXiv Detail & Related papers (2023-05-30T22:05:11Z) - Detecting automatically the layout of clinical documents to enhance the
performances of downstream natural language processing [53.797797404164946]
We designed an algorithm to process clinical PDF documents and extract only clinically relevant text.
The algorithm consists of several steps: initial text extraction using a PDF, followed by classification into such categories as body text, left notes, and footers.
Medical performance was evaluated by examining the extraction of medical concepts of interest from the text in their respective sections.
arXiv Detail & Related papers (2023-05-23T08:38:33Z) - Does Synthetic Data Generation of LLMs Help Clinical Text Mining? [51.205078179427645]
We investigate the potential of OpenAI's ChatGPT to aid in clinical text mining.
We propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data.
Our method has resulted in significant improvements in the performance of downstream tasks.
arXiv Detail & Related papers (2023-03-08T03:56:31Z) - An Empirical Survey of Data Augmentation for Limited Data Learning in
NLP [88.65488361532158]
dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks.
Data augmentation methods have been explored as a means of improving data efficiency in NLP.
We provide an empirical survey of recent progress on data augmentation for NLP in the limited labeled data setting.
arXiv Detail & Related papers (2021-06-14T15:27:22Z) - Active learning for medical code assignment [55.99831806138029]
We demonstrate the effectiveness of Active Learning (AL) in multi-label text classification in the clinical domain.
We apply a set of well-known AL methods to help automatically assign ICD-9 codes on the MIMIC-III dataset.
Our results show that the selection of informative instances provides satisfactory classification with a significantly reduced training set.
arXiv Detail & Related papers (2021-04-12T18:11:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.