Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

BACKGROUND: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls. METHODS: This case-cohort study utilised primary care EHR data covering information on older adults between 2016 and 2019. Cases were individuals who fell in 2019 but had no falls in the preceding three years (2016-18). The control group was randomly sampled individuals, with similar size to the cases group, who did not endure falls during the whole study follow-up period. We applied DTM on the clinical notes collected between 2016 and 2018. We compared the trend lines of the case and control groups using the slopes, which indicate direction and steepness of the change over time. RESULTS: A total of 2,384 fallers (cases) and an equal number of controls were included. We identified 25 topics that showed significant differences in trends between the case and control groups. Topics such as medications, renal care, family caregivers, hospital admission/discharge and referral/streamlining diagnostic pathways exhibited a consistent increase in steepness over time within the cases group before the occurrence of falls. CONCLUSIONS: Early recognition of health conditions demanding care is crucial for applying proactive and comprehensive multifactorial assessments that address underlying causes, ultimately reducing falls and fall-related injuries.

Leveraging Multi-Word Concepts to Predict Acute Kidney Injury in Intensive Care.

Acute kidney injury (AKI) is an abrupt decrease in kidney function widespread in intensive care. Many AKI prediction models have been proposed, but only few exploit clinical notes and medical terminologies. Previously, we developed and internally validated a model to predict AKI using clinical notes enriched with single-word concepts from medical knowledge graphs. However, an analysis of the impact of using multi-word concepts is lacking. In this study, we compare the use of only the clinical notes as input to prediction to the use of clinical notes retrofitted with both single-word and multi-word concepts. Our results show that 1) retrofitting single-word concepts improved word representations and improved the performance of the prediction model; 2) retrofitting multi-word concepts further improves both results, albeit slightly. Although the improvement with multi-word concepts was small, due to the small number of multi-word concepts that could be annotated, multi-word concepts have proven to be beneficial.

Drug-related causes attributed to acute kidney injury and their documentation in intensive care patients.

PURPOSE: To investigate drug-related causes attributed to acute kidney injury (DAKI) and their documentation in patients admitted to the Intensive Care Unit (ICU). METHODS: This study was conducted in an academic hospital in the Netherlands by reusing electronic health record (EHR) data of adult ICU admissions between November 2015 to January 2020. First, ICU admissions with acute kidney injury (AKI) stage 2 or 3 were identified. Subsequently, three modes of DAKI documentation in EHR were examined: diagnosis codes (structured data), allergy module (semi-structured data), and clinical notes (unstructured data). RESULTS: n total 8124 ICU admissions were included, with 542 (6.7%) ICU admissions experiencing AKI stage 2 or 3. The ICU physicians deemed 102 of these AKI cases (18.8%) to be drug-related. These DAKI cases were all documented in the clinical notes (100%), one in allergy module (1%) and none via diagnosis codes. The clinical notes required the highest time investment to analyze. CONCLUSIONS: Drug-related causes comprise a substantial part of AKI in the ICU patients. However, current unstructured DAKI documentation practice via clinical notes hampers our ability to gain better insights about DAKI occurrence. Therefore, both automating DAKI identification from the clinical notes and increasing structured DAKI documentation should be encouraged.

Multi3Generation: Multitask, Multilingual, and Multimodal Language Generation.

The article emphasizes the critical importance of language generation today, particularly focusing on three key aspects: Multitasking, Multilinguality, and Multimodality, which are pivotal for the Natural Language Generation community. It delves into the activities conducted within the Multi3Generation COST Action (CA18231) and discusses current trends and future perspectives in language generation.

The Multi3Generation COST Action is a collaborative project that brings together researchers from various fields, all centered around Natural Language Generation. Natural Language Generation involves using computers to generate human-like language for tasks such as translation, summarization, question-answering, and dialogue interaction, among others. The Action addresses common challenges including efficient information representation, advanced machine learning techniques, managing uncertainty in human-Natural Language Generation interactions, and using structured knowledge from diverse sources like databases, images, and videos. Its overarching goal is to make NLG beneficial to society and widely accessible by fostering collaboration between industry and academic experts. Structured into five working groups, the Action focuses on specific aspects of Natural Language Generation, such as understanding and generating different types of information, developing efficient machine learning algorithms, enhancing dialogue and conversational language generation using knowledge bases, and fostering industry collaboration and end-user engagement. With over 133 scientists from 34 countries involved, spanning disciplines from computer science to linguistics, the project promotes diversity and inclusivity, with 60% male and 40% female participants. Relevant businesses like Unbabel and JabberBrain and other AI stakeholders like the Center for Responsible AI contribute to the Action, aiming to have a broader European impact. The Multi3Generation Action prioritizes three main areas: Multitasking, Multilinguality, and Multimodality, aiming to enhance language generation in these domains to support underrepresented languages and meet diverse user needs. The article provides insights into the initiatives and planned activities of Multi3Generation, offering valuable information for those interested in NLG and shedding light on future perspectives in this field.

An error analysis for image-based multi-modal neural machine translation.

In this article, we conduct an extensive quantitative error analysis of different multi-modal neural machine translation (MNMT) models which integrate visual features into different parts of both the encoder and the decoder. We investigate the scenario where models are trained on an in-domain training data set of parallel sentence pairs with images. We analyse two different types of MNMT models, that use global and local image features: the latter encode an image globally, i.e. there is one feature vector representing an entire image, whereas the former encode spatial information, i.e. there are multiple feature vectors, each encoding different portions of the image. We conduct an error analysis of translations generated by different MNMT models as well as text-only baselines, where we study how multi-modal models compare when translating both visual and non-visual terms. In general, we find that the additional multi-modal signals consistently improve translations, even more so when using simpler MNMT models that use global visual features. We also find that not only translations of terms with a strong visual connotation are improved, but almost all kinds of errors decreased when using multi-modal models.