Advances in Large Language Modeling (LLM) have allowed LLMs to be integrated into multi-agent problem-solving systems. We present a novel approach to enhance this technique and incorporate it, and some of its practical variants, into an intelligent agent system. Our approach is based on clustering with overlaps via an algorithm for Cluster Editing with Vertex Splitting (CEVS) to create a Network Hypothesis Search Agent. As a case study, we use a practical example of a panel discussion on possible mRNA treatments for Head and Neck cancer.

Person re-identification with natural language description is a process of retrieving the corresponding person’s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms.

Background There are many proposed benefits of using learning health systems (LHSs), including improved patient outcomes. There has been little adoption of LHS in practice due to challenges and barriers that limit adoption of new data-driven technologies in healthcare. We have identified a more fundamental explanation: the majority of developments in LHS are not identified as LHS. The absence of a unifying namespace and framework brings a lack of consistency in how LHS is identified and classified. As a result, the LHS ‘community’ is fragmented, with groups working on similar systems being unaware of each other’s work. This leads to duplication and the lack of a critical mass of researchers necessary to address barriers to adoption. Objective To find a way to support easy identification and classification of research works within the domain of LHS. Method A qualitative meta-narrative study focusing on works that self-identified as LHS was used for two purposes. First, to find existing standard definitions and frameworks using these to create a new unifying framework. Second, seeking whether it was possible to classify those LHS solutions within the new framework. Results The study found that with apparently limited awareness, all current LHS works fall within nine primary archetypes. These findings were used to develop a unifying framework for LHS to classify works as LHS, and reduce diversity and fragmentation within the domain. Conclusions Our finding brings clarification where there has been limited awareness for LHS among researchers. We believe our framework is simple and may help researchers to classify works in the LHS domain. This framework may enable realisation of the critical mass necessary to bring more substantial collaboration and funding to LHS. Ongoing research will seek to establish the framework’s effect on the LHS domain.

The learning health system (LHS) is one in which progress in science, informatics and care culture converges to continuously create new knowledge as a natural by-product of care processes. While LHS was first described over a decade ago, much of the recent published work that should fall within the domain of LHS fails to claim or be identified as such. This observation was confirmed through a review of papers published at the recent 2017 IEEE International Conference on Health Informatics (ICHI 2017), where no single LHS solution had been so identified. The authors lacked awareness that their work represented an LHS, or of any discrete classification for their work within the LHS domain. We believe this lack of awareness inhibits continued LHS research and prevents formation of a critical mass of researchers within the domain. Efforts to produce a framework and classification structure to enable confident identification of work with the LHS domain are urgently needed to address this pressing research community challenge.

Presented here is a summation of an investigation i nto the domain of coding of electronic medical records at the primary healthcare level. This looke d specifically at the issues of the time taken to code records by the General Practitioner and at the lack of a standardised way in which the codes are applied. The problems these issues cause are id entified and an IT system design, based on natural language processing techniques, which would allow for the automated application of codes, is put forward as a way of reducing the effe cts of these two issues. Design decisions are highlighted and justified and a prototype design co ncept is produced. Finally, it highlights what is believed should be the focus of further investigati on in the first year of a proposed PhD thesis which will continue research in this same field.