Effective Convolutional Attention Network for Multi-label Clinical Document Classification

Medical Codes Prediction from Clinical Notes: From Human Coders to Machines

“…Third, based on the MIMIC-III dataset 3 , we demonstrate the RAC model's effectiveness in the most challenging full codes prediction testing set from inpatient clinical notes. The RAC model wins over all the previously reported SOTA results considerably.…”

“…However, the biggest challenge is directly identifying appropriate medical codes from several thousands of high-dimensional codes from unstructured free-text clinical notes. This complex medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies [1,6,3,8] have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods. This progress raises the fundamental question of how far automated machine learning (ML) systems are from human coders' working performance, as well as the important question of how well current explainability methods apply to advanced neural network models such as transformers.…”

A Novel Metadata Based Multi-Label Document Classification Technique

“…Furthermore, this paper discussed how Ye et al [45] took a similar approach to MATCH by incorporating label hierarchy and metadata in their model, with the addition of representing those using heterogeneous graphs, showing further improvement in their results compared to MATCH. The authors proposed a different approach from the previous two by focusing only on extracting significant parts of long documents using multi-layer attention, producing meaningful segments by a convolution-based encoder with multiple Res-SE blocks, then assigning the most relevant ones for each label [46]. Xu et al [47] model focused on solving the lack of labeled data ready for training by creating a dynamic self-training semi-supervised classification method, which combines self-training into the process of Multi-Label Classification.…”

“…Per Table 1, there is a reasonable ratio of papers with multiple labels which motivates the idea of multi-label classification further. Some pre-processing operations, such as normalization (converting all terms to lowercase), removal of stop words from the Title and Keywords of all research papers, and conversion of all compound words into single words, are conducted to make metadata parameters ready for experimentation [46]. CMM Algorithm merges the metadata of all research papers that belong to the same category.…”

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing

“…• detection of events [29,30], including self-harm events [31]; • extraction of diagnoses [13,[32][33][34][35] and their codes [36][37][38]; • recognition of named entities [5,14,[39][40][41], and more specifically of personal information [21,42,43] and family history [20]; • localization of advices [44] and arguments [45] in scientific literature; • extraction of relations [46][47][48], including temporal [49] and causality [50,51] relations.…”