Differential Diagnosis Documentation In Emergency Medicine

Diagnosis is a central aspect of emergency medicine. Coming to the correct diagnosis impacts patient morbidity and mortality and also the healthcare expenditures. Medical decision making is driven by the path of figuring out the differential diagnosis. Once a decent Natural Language Processing (NLP) system is developed including general characterization of differential diagnose, associated with downstream testing, diagnostic error, etc., we could be able to automatically extract differential diagnoses within clinical notes, which would have a large impact on healthcare. The main purpose of our investigative study is the characterization of differential diagnosis documentation within emergency provider notes and the development of an annotated corpus that could be used for further downstream development of NLP applications. We conducted a retrospective analysis of emergency provider notes to identify, categorize, and extract information around differential diagnoses using manual annotation. We used a light annotation framework within the MATTER cycle and extracted the information from our annotations based on a random sample of 1545 medical records. We describe the demographics information and note that only 18.1% of patients were actually given a differential diagnosis by the physicians. We examined factors including age groups, race and ethnicity groups, language preferred, acuity level, and major complaints that could lead to differences in differential diagnosis rates among patients. Within the differential diagnosis groups, evidence support and probability terms are reported. We also examined cough, chest pain, shortness of breath, abdominal pain, back pain, and falling, which are the top six complaints. Still, we suffered from limitations including sample size, nature of the accuracy of annotations, etc

An equivalent-effect phenomenon in eddy current non-destructive testing of thin structures

The inductance/impedance due to thin metallic structures in non-destructive testing (NDT) is difficult to evaluate. In particular, in Finite Element Method (FEM) eddy current simulation, an extremely fine mesh is required to accurately simulate skin effects especially at high frequencies, and this could cause an extremely large total mesh for the whole problem, i.e. including, for example, other surrounding structures and excitation sources like coils. Consequently, intensive computation requirements are needed. In this paper, an equivalent-effect phenomenon is found, which has revealed that alternative structures can produce the same effect on the sensor response, i.e. mutual impedance/inductance of coupled coils if a relationship (reciprocal relationship) between the electrical conductivity and the thickness of the structure is observed. By using this relationship, the mutual inductance/impedance can be calculated from the equivalent structures with much fewer mesh elements, which can significantly save the computation time. In eddy current NDT, coils inductance/impedance is normally used as a critical parameter for various industrial applications, such as flaw detection, coating and microstructure sensing. Theoretical derivation, measurements and simulations have been presented to verify the feasibility of the proposed phenomenon

sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation

Spatial time series imputation is critically important to many real applications such as intelligent transportation and air quality monitoring. Although recent transformer and diffusion model based approaches have achieved significant performance gains compared with conventional statistic based methods, spatial time series imputation still remains as a challenging issue due to the complex spatio-temporal dependencies and the noise uncertainty of the spatial time series data. Especially, recent diffusion process based models may introduce random noise to the imputations, and thus cause negative impact on the model performance. To this end, we propose a self-adaptive noise scaling diffusion model named SaSDim to more effectively perform spatial time series imputation. Specially, we propose a new loss function that can scale the noise to the similar intensity, and propose the across spatial-temporal global convolution module to more effectively capture the dynamic spatial-temporal dependencies. Extensive experiments conducted on three real world datasets verify the effectiveness of SaSDim by comparison with current state-of-the-art baselines

Cylindrical roller bearing fault diagnosis based on VMD-SVD and Adaboost classifier method

Fault diagnosis for cylindrical roller bearing is of great significance for industry. In order to excavate the features of the vibration signal adequately, and to construct an effective classifier for complex vibration signals, this paper proposed a new fault diagnosis method based on Variational Mode Decomposition (VMD), Singular Value Decomposition (SVD) and Adaboost classifier. Firstly, the VMD was applied to decompose the sampled vibration signal in time-frequency domain. Subsequently, the features were extracted by using SVD. Finally, the constructed Adaboost classifier were employed to fault detection and diagnosis, which were trained by using the extracted features. Experimental data measured in a rotating machinery fault diagnosis experiment platform was used to verify the proposed method. The results demonstrate that the proposed method was effective to detect and diagnose the outer ring fault and rolling element fault in cylindrical roller bearing

Vulnerability and Resilience in the Wake of COVID-19: Family Resources and Children’s Well-being in China

The present study uses data from a 2020 survey conducted in Shaanxi Province during the COVID-19 outbreak to examine the family resources and psychological well-being of four major groups of Chinese children (urban, migrant, rural nonmigrant, and rural left-behind children). The results highlight the complex ways in which family resources intersect with the pandemic to affect these different groups of children. Family economic resources have generally declined across all groups, but left-behind children have suffered the most severe economic shock. However, parent-child relationships for all children have improved across the board during the pandemic. Diminished economic resources act as a risk factor, while improved family relationships play a protective role in children’s psychological well-being. Parent-child relationships have had a more pronounced positive impact on psychological outcomes for migrant and left-behind children, who are the most deprived of parental input under normal circumstances, than for other groups of children. Because of these processes, migrant children and left-behind children fare similarly to urban children in terms of their resilience to the COVID-19 crisis. Among children enjoying especially favorable parent-child relationships, migrant children and left-behind children even appear to have higher psychological well-being than urban children during the pandemic. In comparison to this social impact, the impact of family economic resources is more moderate in magnitude and does not vary systematically across different groups of children. As a result, the positive impact of improved parent-child relationships largely outweighs the adverse effect of reduced family economic resources. Overall, the findings provide new insight into the relationship among disasters, family resources, and child well-being in the context of the COVID-19 crisis in China

Family Structure, Family Instability, and Child Psychological Well-being in the Context of Migration: Evidence from Sequence Analysis in China

This study conceptualizes parental migration as a dynamic family process that exposes children to parental absence and family instability. Using detailed migration histories, this study identifies the left-behind trajectories of rural Chinese children throughout childhood (age 1-12) and examines the impact on psychological well-being (N=3,961). Results indicate heterogeneity in children’s experience of parental migration, which is characterized by both persistence (prolonged parental absence) and instability (repeated parental migration). A quarter of rural children experienced prolonged parental migration, and for half of these, by both parents. Another 50% of rural children experienced repeated parental migration. Children continuously left behind by both parents and children who experienced substantial family instability both fared worse in psychological development than those in stable two-parent families

Multilingual Large Language Models Are Not (Yet) Code-Switchers

Multilingual Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks, exhibiting state-of-the-art performance through zero-shot or few-shot prompting methods. While there have been extensive studies on their abilities in monolingual tasks, the investigation of their potential in the context of code-switching (CSW), the practice of alternating languages within an utterance, remains relatively uncharted. In this paper, we provide a comprehensive empirical analysis of various multilingual LLMs, benchmarking their performance across four tasks: sentiment analysis, machine translation, summarization and word-level language identification. Our results indicate that despite multilingual LLMs exhibiting promising outcomes in certain tasks using zero or few-shot prompting, they still underperform in comparison to fine-tuned models of much smaller scales. We argue that current "multilingualism" in LLMs does not inherently imply proficiency with code-switching texts, calling for future research to bridge this discrepancy.Comment: Accepted at EMNLP 202

UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization

The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization. However, despite the emergence of many summarization tasks and datasets, the current training paradigm for few-shot summarization systems ignores potentially shareable knowledge in heterogeneous datasets. To this end, we propose \textsc{UniSumm}, a unified few-shot summarization model pre-trained with multiple summarization tasks and can be prefix-tuned to excel at any few-shot summarization task. Meanwhile, to better evaluate few-shot summarizers, under the principles of diversity and robustness, we assemble and release a new benchmark \textsc{SummZoo}. It consists of $8$ summarization tasks with multiple sets of few-shot samples for each task, covering diverse domains. Experimental results and analysis show that \textsc{UniSumm} outperforms strong baselines by a large margin across all sub-tasks in \textsc{SummZoo} under both automatic and human evaluations and achieves comparable results in human evaluation compared with a GPT-3.5 model.Comment: ACL2023 main conferenc