The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rapid vulnerability identification to mitigate cybersecurity attacks and save costs and resources. In this work, we propose using large language models (LLMs) to learn vulnerability evaluation from historical assessments of medical device vulnerabilities in a single manufacturer's portfolio. We highlight the effectiveness and challenges of using LLMs for automatic vulnerability evaluation and introduce a method to enrich historical data with cybersecurity ontologies, enabling the system to understand new vulnerabilities without retraining the LLM. Our LLM system integrates with the in-house application - Cybersecurity Management System (CSMS) - to help Siemens Healthineers (SHS) product cybersecurity experts efficiently assess the vulnerabilities in our products. Also, we present guidelines for efficient integration of LLMs into the cybersecurity tool.

The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses.A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report.These tasks include acquisition of salient content from the report and generation of a concise, easily consumable IMPRESSIONS section.Prior research on radiology report summarization has focused on single-step end-to-end models -which subsume the task of salient content acquisition.To fully explore the cascade structure and explainability of radiology report summarization, we introduce two innovations.First, we design a two-step approach: extractive summarization followed by abstractive summarization.Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords.Experiments on English radiology reports from two clinical sites show our novel approach leads to a more precise summary compared to single-step and to two-stepwith-single-extractive-process baselines with an overall improvement in F1 score of 3-4%.

Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.

Clinical diagnosis is a critical aspect of patient care that is typically driven by expert medical knowledge and intuition. An automated system for clinical diagnosis could reduce the cognitive burden of clinicians during patient care and medical education. In this paper, we describe a Knowledge Graph (KG)-based clinical diagnosis system that leverages publicly available knowledge sources to infer possible diagnoses from free-text clinical narratives. We experiment with the content in various sections of a clinical document within the electronic health record (EHR) to investigate the contribution of each section to the performance of automated diagnosis systems. Evaluation on MIMIC-III dataset demonstrates that the content of “history of present illness” and “past medical history” sections can play a greater role for clinical diagnosis inference than other sections and all sections combined. Comparison with a state-of-the-art deep learning-based clinical diagnosis system confirms the effectiveness of our system.

Natural Language Processing and Machine Learning techniques can be used to automatically identify, extract and manipulate textual clinical data. Many of these methods are strongly dependent on annotated corpora that are very difficult to find in the clinical domain, especially for the Brazilian Portuguese language. The annotation task is expensive and time-consuming; hence, it is important to provide intelligent computational tools to facilitate this kind of work. In this paper, we propose a collaborative annotation tool that assists the user by proposing the UMLS semantic types of the clinical concepts based on the previous annotation statistics and UMLS terminology access via REST API. Our evaluation was focused on the amount of effort saved by the annotation tool, reliability of the preliminary annotations and efficacy of the annotation assistant.

Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating clinical diagnosis as a reinforcement learning problem. During training, the reinforcement learning agent mimics the clinician’s cognitive process and learns the optimal policy to obtain the most appropriate diagnoses for a clinical narrative. This is achieved through an iterative search for candidate diagnoses from external knowledge sources via a sentence-by-sentence analysis of the inherent clinical context. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses. Experiments on the TREC CDS datasets demonstrate the effectiveness of our system over various non-reinforcement learning-based systems.

Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published. Online social media such as Twitter could describe adverse drug events in real-time, prior to official reporting. Deep learning has significantly improved text classification performance in recent years and can potentially enhance ADE classification in tweets. However, these models typically require large corpora with human expert-derived labels, and such resources are very expensive to generate and are hardly available. Semi-supervised deep learning models, which offer a plausible alternative to fully supervised models, involve the use of a small set of labeled data and a relatively larger collection of unlabeled data for training. Traditionally, these models are trained on labeled and unlabeled data from similar topics or domains. In reality, millions of tweets generated daily often focus on disparate topics, and this could present a challenge for building deep learning models for ADE classification with random Twitter stream as unlabeled training data. In this work, we build several semi-supervised convolutional neural network (CNN) models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem. We demonstrate that, with the selective use of a variety of unlabeled data, our semi-supervised CNN models outperform a strong state-of-the-art supervised classification model by +9.9% F1-score. We evaluated our models on the Twitter data set used in the PSB 2016 Social Media Shared Task. Our results present the new state-of-the-art for this data set.

In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers, and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based, and bi-directional LSTM models on BLEU, METEOR, TER, and an embedding-based sentence similarity metric.