Bidirectional Encoder Representations from Transformers-like large language models in patient safety and pharmacovigilance: A comprehensive assessment of causal inference implications.

Causality assessment is vital in patient safety and pharmacovigilance (PSPV) for safety signal detection, adverse reaction management, and regulatory submission. Large language models (LLMs), especially those designed with transformer architecture, are revolutionizing various fields, including PSPV. While attempts to utilize Bidirectional Encoder Representations from Transformers (BERT)-like LLMs for causal inference in PSPV are underway, a detailed evaluation of "fit-for-purpose" BERT-like model selection to enhance causal inference performance within PSPV applications remains absent. This study conducts an in-depth exploration of BERT-like LLMs, including generic pre-trained BERT LLMs, domain-specific pre-trained LLMs, and domain-specific pre-trained LLMs with safety knowledge-specific fine-tuning, for causal inference in PSPV. Our investigation centers around (1) the influence of data complexity and model architecture, (2) the correlation between the BERT size and its impact, and (3) the role of domain-specific training and fine-tuning on three publicly accessible PSPV data sets. The findings suggest that (1) BERT-like LLMs deliver consistent predictive power across varied data complexity levels, (2) the predictive performance and causal inference results do not directly correspond to the BERT-like model size, and (3) domain-specific pre-trained LLMs, with or without safety knowledge-specific fine-tuning, surpass generic pre-trained BERT models in causal inference. The findings are valuable to guide the future application of LLMs in a broad range of application.

Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43.

Background: Cerebral vasospasm (CV) can cause inflammation and damage to neuronal cells in the elderly, leading to dementia. Purpose: This study aimed to investigate the genetic mechanisms underlying dementia caused by CV in the elderly, identify preventive and therapeutic drugs, and evaluate their efficacy in treating neurodegenerative diseases. Methods: Genes associated with subarachnoid hemorrhage and CV were acquired and screened for differentially expressed miRNAs (DEmiRNAs) associated with aneurysm rupture. A regulatory network of DEmiRNAs and mRNAs was constructed, and virtual screening was performed to evaluate possible binding patterns between Food and Drug Administration (FDA)-approved drugs and core proteins. Molecular dynamics simulations were performed on the optimal docked complexes. Optimally docked drugs were evaluated for efficacy in the treatment of neurodegenerative diseases through cellular experiments. Results: The study found upregulated genes (including WDR43 and THBS1) and one downregulated gene associated with aneurysm rupture. Differences in the expression of these genes indicate greater disease risk. DEmiRNAs associated with ruptured aortic aneurysm were identified, of which two could bind to THBS1 and WDR43. Cromolyn and lanoxin formed the best docking complexes with WDR43 and THBS1, respectively. Cellular experiments showed that cromolyn improved BV2 cell viability and enhanced Aß42 uptake, suggesting its potential as a therapeutic agent for inflammation-related disorders. Conclusion: The findings suggest that WDR43 and THBS1 are potential targets for preventing and treating CV-induced dementia in the elderly. Cromolyn may have therapeutic value in the treatment of Alzheimer's disease and dementia.

Investigation of tactile gait parameters based on deep learning of energy consumption estimation algorithm in sport / Investigação sobre parâmetros tácteis de marcha baseados no aprendizado profundo do algoritmo de estimativa de consumo energético no esporte / Investigación sobre los parámetros táctiles de marcha basados en el aprendizaje profundo del algoritmo de estimación del consumo de energía en el deporte

ABSTRACT Introduction In medicine, Deep Learning is a type of machine learning that aims to train computers to perform human tasks by simulating the human brain. Gait recognition and gait motion simulation is one of the most interesting research areas in the field of biometrics and can benefit from this technological feature. Objective To use Deep Learning to format and validate according to the dynamic characteristics of gait. Methods Gait was used for identity recognition, and gait recognition based on kinematics and dynamic gait parameters was performed through pattern recognition, including the position and the intensity value of maximum pressure points, pressure center point, and pressure ratio. Results The investigation shows that the energy consumption of gait as modeled analyzed, and the model of gait energy consumption can be obtained, which is comprehensively affected by motion parameters and individual feature parameters. Conclusion Real-time energy measurement is obtained when most people walk. The research shows that the gait frequency and body parameters obtained from the tactile parameters of gait biomechanics can more accurately estimate the energy metabolism of exercise and obtain the metabolic formula of exercise. There is a good application prospect for assessing energy metabolism through the tactile parameters of gait. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução Na medicina, o aprendizado profundo é um tipo de aprendizado de máquina que visa treinar computadores para a realização de tarefas humanas simulando o cérebro humano. O reconhecimento da marcha e a simulação do movimento de marcha são um dos pontos de maior interesse da investigação no campo da biometria e pode ser beneficiado com esse recurso tecnológico. Objetivo Utilizar o aprendizado profundo para formatar e validar, de acordo com as características dinâmicas da marcha. Métodos A marcha foi utilizada para o reconhecimento da identidade, e o reconhecimento da marcha baseado na cinemática e parâmetros dinâmicos de marcha foi realizado através do reconhecimento de padrões, incluindo a posição e o valor de intensidade dos pontos de pressão máxima, ponto central de pressão e relação de pressão. Resultados A investigação mostra que o consumo de energia da marcha como modelado analisado, e o modelo de consumo de energia da marcha pode ser obtido, o qual é afetado de forma abrangente pelos parâmetros de movimento e pelos parâmetros de características individuais. Conclusão A medição de energia em tempo real é obtida quando a maioria das pessoas caminha. A investigação mostra que a frequência da marcha e os parâmetros corporais obtidos a partir dos parâmetros tácteis da biomecânica da marcha podem estimar com maior precisão o metabolismo energético do exercício e obter a fórmula metabólica do exercício. Há uma boa perspectiva de aplicação para avaliar o metabolismo energético através dos parâmetros tácteis da marcha. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción En medicina, el aprendizaje profundo es un tipo de aprendizaje que pretende entrenar a los ordenadores para que realicen tareas humanas simulando el cerebro humano. El reconocimiento de la marcha y la simulación de su movimiento es uno de los puntos más interesantes de la investigación en el campo de la biometría y puede beneficiarse de este recurso tecnológico. Objetivo Utilizar el aprendizaje profundo para formatear y validar según las características dinámicas de la marcha. Métodos Se utilizó la marcha para el reconocimiento de la identidad, y el reconocimiento de la marcha basado en la cinemática y los parámetros dinámicos de la marcha se realizó mediante el reconocimiento de patrones, incluyendo la posición y el valor de la intensidad de los puntos de presión máxima, el punto de presión central y la relación de presión. Resultados La investigación muestra que el consumo de energía de la marcha, tal y como se analizó, y el modelo de consumo de energía de la marcha se puede obtener, que es ampliamente afectado por los parámetros de movimiento y los parámetros de las características individuales. Conclusión La medición de la energía en tiempo real se obtiene cuando la mayoría de la gente camina. La investigación muestra que la frecuencia de la marcha y los parámetros corporales obtenidos a partir de los parámetros táctiles de la biomecánica de la marcha pueden estimar con mayor precisión el metabolismo energético del ejercicio y obtener la fórmula metabólica del mismo. Existe una buena perspectiva de aplicación para evaluar el metabolismo energético a través de los parámetros táctiles de la marcha. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox.

Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality framework by employing the LiverTox database to estimate idiosyncratic drug-induced liver injury (DILI)-related causal terms and generate a knowledge-based causal tree for idiosyncratic DILI patient stratification. Consequently, the DeepCausality yielded a prediction performance with an accuracy of 0.92 and an F-score of 0.84 for the DILI prediction. Notably, 90% of causal terms enriched by the DeepCausality were consistent with the clinical causal terms defined by the American College of Gastroenterology (ACG) clinical guideline for evaluating suspected idiosyncratic DILI (iDILI). Furthermore, we observed a high concordance of 0.91 between the iDILI severity scores generated by DeepCausality and domain experts. Altogether, the proposed DeepCausality framework could be a promising solution for causality assessment from free text and is publicly available through https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox.

InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance.

Background: T ransformer-based language models have delivered clear improvements in a wide range of natural language processing (NLP) tasks. However, those models have a significant limitation; specifically, they cannot infer causality, a prerequisite for deployment in pharmacovigilance, and health care. Therefore, these transformer-based language models should be developed to infer causality to address the key question of the cause of a clinical outcome. Results: In this study, we propose an innovative causal inference model-InferBERT, by integrating the A Lite Bidirectional Encoder Representations from Transformers (ALBERT) and Judea Pearl's Do-calculus to establish potential causality in pharmacovigilance. Two FDA Adverse Event Reporting System case studies, including Analgesics-related acute liver failure and Tramadol-related mortalities, were employed to evaluate the proposed InferBERT model. The InferBERT model yielded accuracies of 0.78 and 0.95 for identifying Analgesics-related acute liver failure and Tramadol-related death cases, respectively. Meanwhile, the inferred causes of the two clinical outcomes, (i.e. acute liver failure and death) were highly consistent with clinical knowledge. Furthermore, inferred causes were organized into a causal tree using the proposed recursive do-calculus algorithm to improve the model's understanding of causality. Moreover, the high reproducibility of the proposed InferBERT model was demonstrated by a robustness assessment. Conclusion: The empirical results demonstrated that the proposed InferBERT approach is able to both predict clinical events and to infer their causes. Overall, the proposed InferBERT model is a promising approach to establish causal effects behind text-based observational data to enhance our understanding of intrinsic causality. Availability and implementation: The InferBERT model and preprocessed FAERS data sets are available on GitHub at https://github.com/XingqiaoWang/DeepCausalPV-master.

A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization.

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.