Table 1.
A comparison of Ascle with existing Python-based toolkits.
| Toolkits | Question- Answeringa |
Text Summarization |
Text Simplification |
Machine Translation |
Basic NLPb Functions | Query Search |
| MIMIC-Extract [16] | —c | — | — | — | — | ✓ |
| ScispaCy [17] | — | — | — | — | ✓ | — |
| MedspaCy [18] | — | — | — | — | ✓ | — |
| Transformers-sklearn [19] | — | — | — | — | ✓ | — |
| Stanza Biomed [20] | — | — | — | — | ✓ | — |
| Ascle (this study) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
aFor the question-answering task, we specifically propose a retrieval-augmented generation framework for large language models that incorporates a medical knowledge graph with ranking techniques.
bNLP: natural language processing. Basic natural language processing functions include abbreviation extraction, sentence tokenization, word tokenization, negation detection, hyponym detection, Unified Medical Language System concept extraction, named entity recognition, document clustering, part-of-speech tagging, entity linking, text summarization (extractive methods), and multiple-choice question-answering. It is worth noting that not every toolkit includes these 12 basic natural language processing functions, but Ascle includes them all.
cNot applicable.