Hsieh, Chia-Chun
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
Yang, Rui, Zeng, Qingcheng, You, Keen, Qiao, Yujie, Huang, Lucas, Hsieh, Chia-Chun, Rosand, Benjamin, Goldwasser, Jeremy, Dave, Amisha D, Keenan, Tiarnan D. L., Chew, Emily Y, Radev, Dragomir, Lu, Zhiyong, Xu, Hua, Chen, Qingyu, Li, Irene
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. The toolkit, its models, and associated data are publicly available via https://github.com/Yale-LILY/MedGen.
aoip.ai: An Open-Source P2P SDK
Konan, Joseph, Agnihotri, Shikhar, Hsieh, Chia-Chun
This white paper introduces aoip.ai, a groundbreaking open-source SDK incorporating peer-to-peer technology and advanced AI integration to transform VoIP and IoT applications. It addresses key market challenges by enhancing data security, elevating communication quality, and providing greater flexibility for developers and users. Developed in collaboration with Carnegie Mellon University, aoip.ai sets a new standard for decentralized and democratized communication solutions.
EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts
Li, Irene, You, Keen, Qiao, Yujie, Huang, Lucas, Hsieh, Chia-Chun, Rosand, Benjamin, Goldwasser, Jeremy, Radev, Dragomir
The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research. Unstructured text is attracting much attention despite structured information in the EHRs and has become an exciting research field. The success of the recent neural Natural Language Processing (NLP) method has led to a new direction for processing unstructured clinical notes. In this work, we create a python library for clinical texts, EHRKit. This library contains two main parts: MIMIC-III-specific functions and tasks specific functions. The first part introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data, including basic search, information retrieval, and information extraction. The second part integrates many third-party libraries for up to 12 off-shelf NLP tasks such as named entity recognition, summarization, machine translation, etc.