BibSonomy

ChatGPT-4, powered by OpenAI, is the ultimate game-changer in conversational AI. It is advanced NLP and machine learning capabilities offer next-gen virtual assistant and chatbot solutions.

The paper discusses the capabilities of large pre-trained language models and their limitations in accessing and manipulating knowledge. The authors introduce retrieval-augmented generation (RAG) models that combine pre-trained parametric and non-parametric memory for language generation. The study explores the effectiveness of RAG models in various NLP tasks and compares them with other architectures.

odyCy is a state of the art NLP library for Ancient Greek, capable of part-of-speech tagging, morphological analysis, dependency parsing, lemmatization and more.

So, what’s this Twitter bot thing? A Twitter bot is essentially a Twitter account controlled by software automation rather than an actual human. It is programmed to behave like regular Twitter accounts, liking Tweets, retweeting, and engaging with other accounts. Twitter bots can be helpful for specific use cases, such as sending out critical alerts and announcements. On the flip side, they can also be used for nefarious purposes, such as starting a disinformation campaign. These bots can also turn nefarious when “programmed” incorrectly. This is what happened with Tay, an AI Twitter bot from 2016. Tay was an experiment at the intersection of ML, NLP, and social networks. She had the capacity to Tweet her “thoughts” and engage with her growing number of followers. While other chatbots in the past, such as Eliza, conducted conversations using narrow scripts, Tay was designed to learn more about language over time from its environment, allowing her to have conversations about any topic. In the beginning, Tay engaged harmlessly with her followers with benign Tweets. However, after a few hours, Tay started tweeting highly offensive things, and as a result, she was shut down just sixteen hours after her launch. You may wonder how can such an “error” happen so publicly. Wasn’t this bot tested? Weren’t the researchers aware that this bot was an evil, racist bot before releasing it? These are valid questions. To get into the crux of what went wrong, let’s study some of the problems in detail and try to learn from them. This will help us all see how to handle similar challenges when deploying AI in our organizations

Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies.

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Named-Entity evaluation metrics based on entity-level

HEALTHCARE TEXT ANALYTICS CONFERENCE 2022

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