Explainable text-tabular models for predicting mortality risk in companion animals

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets containing a range of modalities, from the free-text of clinician notes to structured tabular data entries, there is a need for frameworks capable of providing comprehensive explanation values across diverse modalities. Here, we present a multimodal masking framework to extend the reach of SHapley Additive exPlanations (SHAP) to text and tabular datasets to identify risk factors for companion animal mortality in first-opinion veterinary electronic health records (EHRs) from across the United Kingdom. The framework is designed to treat each modality consistently, ensuring uniform and consistent treatment of features and thereby fostering predictability in unimodal and multimodal contexts. We present five multimodality approaches, with the best-performing method utilising PetBERT, a language model pre-trained on a veterinary dataset. Utilising our framework, we shed light for the first time on the reasons each model makes its decision and identify the inclination of PetBERT towards a more pronounced engagement with free-text narratives compared to BERT-base’s predominant emphasis on tabular data. The investigation also explores the important features on a more granular level, identifying distinct words and phrases that substantially influenced an animal’s life status prediction. PetBERT showcased a heightened ability to grasp phrases associated with veterinary clinical nomenclature, signalling the productivity of additional pre-training of language models

PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production animal veterinary medicine, comparable frameworks for companion animals are lacking. First-opinion veterinary electronic health records (EHRs) have the potential to reveal disease signals and often represent the initial reporting of clinical syndromes in animals presenting for medical attention, highlighting their possible significance in early disease detection. Yet despite their availability, there are limitations surrounding their free text-based nature, inhibiting the ability for national-level mortality and morbidity statistics to occur. This paper presents PetBERT, a large language model trained on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the additional training of PetBERT as a multi-label classifier for the automated coding of veterinary clinical EHRs with the International Classification of Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, outperforming current clinician-assigned point-of-care labelling strategies up to 3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in veterinary medicine represents a promising avenue for advancing animal health and improving public health outcomes

Patent Foramen Ovale, Ischemic Stroke and Migraine: Systematic Review and Stratified Meta-Analysis of Association Studies

BACKGROUND: Observational data have reported associations between patent foramen ovale (PFO), cryptogenic stroke and migraine. However, randomized trials of PFO closure do not demonstrate a clear benefit either because the underlying association is weaker than previously suggested or because the trials were underpowered. In order to resolve the apparent discrepancy between observational data and randomized trials, we investigated associations between (1) migraine and ischemic stroke, (2) PFO and ischemic stroke, and (3) PFO and migraine. METHODS: Eligibility criteria were consistent; including all studies with specifically defined exposures and outcomes unrestricted by language. We focused on studies at lowest risk of bias by stratifying analyses based on methodological design and quantified associations using fixed-effects meta-analysis models. RESULTS: We included 37 studies of 7,686 identified. Compared to reports in the literature as a whole, studies with population-based comparators showed weaker associations between migraine with aura and cryptogenic ischemic stroke in younger women (OR 1.4; 95% CI 0.9–2.0; 1 study), PFO and ischemic stroke (HR 1.6; 95 CI 1.0–2.5; 2 studies; OR 1.3; 95% CI 0.9–1.9; 3 studies), or PFO and migraine (OR 1.0; 95% CI 0.6–1.6; 1 study). It was not possible to look for interactions or effect modifiers. These results are limited by sources of bias within individual studies. CONCLUSIONS: The overall pairwise associations between PFO, cryptogenic ischemic stroke and migraine do not strongly suggest a causal role for PFO. Ongoing randomized trials of PFO closure may need larger numbers of participants to detect an overall beneficial effect

Impact of single and multiple specimen suction control oedometer testing on the measurement of the soil–water characteristic curve

Devices that simultaneously facilitate controlling suction and applying a net stress on soil specimen provide soil–water characteristic curves (SWCCs) in terms of both the water content and degree of saturation, and volumetric deformations at various applied suctions. Such tests determine the water content of soil specimens based on the measured water volume changes at various applied suctions. However, studies have shown disagreements between the water volume–based calculated water content and the actual water content of soil specimens determined by the oven-drying method. Testing multiple soil specimens at predetermined suctions and measuring water content by the oven-drying method can overcome this but are a time-consuming approach. In this study, the impact of testing single and multiple soil specimens on the subsequently determined suction-water content and suction-degree of saturation SWCCs for the wetting process were studied. Statically compacted specimens of a sandy clay were used for establishing SWCCs using a suction control oedometer. Differences were noted between the calculated and measured water content and degree of saturation for an applied suction range of 0 to 95 kPa. Differences were noted between the SWCC fitting parameters obtained from the test results of single and multiple soil specimens. Statistical analysis suggested the differences between the results from single and multiple soil specimens testing were not significant. Corrections applied to the water volume change measurements were found to minimize these differences

Fine-tuning polyoxometalate non-linear optical chromophores: a molecular electronic “Goldilocks” effect

A new aryl-imido polyoxometalate non-linear optical chromophore (POMophore) with a diphenylamino donor group attains the highest βzzz, 0 value (196 × 10−30 esu by Hyper-Rayleigh Scattering, HRS), and best transparency/non-linearity trade off yet for such materials. Stark spectroscopic and DFT investigation of this compound, plus NMe2 and carbazole analogues, show that its high performance results from a combination of strongly dipolar electronic transitions, and strong electronic communication across the π-system