LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information.

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.

Consolidating Emergency Department-specific Data to Enable Linkage with Large Administrative Datasets.

INTRODUCTION: The American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results. METHODS: Using the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate. RESULTS: Of the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs. CONCLUSION: Excluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Maternal health, pregnancy and birth outcomes for women involved in care proceedings in Wales: a linked data study.

BACKGROUND: Under the Children Act 1989, local authorities in Wales, UK, can issue care proceedings if they are concerned about the welfare of a child, which can lead to removal of a child from parents. For mothers at risk of child removal, timely intervention during pregnancy may avert the need for this and improve maternal/fetal health; however, little is known about this specific population during the antenatal period. The study examined maternity characteristics of mothers whose infants were subject to care proceedings, with the aim of informing preventative interventions targeted at high risk mothers. METHODS: Anonymised administrative data from Cafcass Cymru, who provide child-focused advice and support for family court proceedings in Wales, were linked to population-based maternity and health records held within the Secure Anonymised Information Linkage Databank. Linked data were available for 1111 birth mothers of infants involved in care proceedings between 2015 and 2018. Findings were benchmarked with reference to an age-deprivation-matched comparison group (n = 23,414), not subject to care proceedings but accessing maternity services during this period. Demographic characteristics, maternal health, reproductive history, interaction with midwifery services, and pregnancy and birth outcomes were examined. Descriptive and statistical tests of independence were used. RESULTS: Half of the women in the cohort (49.4%) resided in the most deprived areas. They were more likely to be younger at entry to motherhood (63.5% < 21 years-of-age compared to 42.7% in the comparison group), to have mental health (28.6% compared to 8.2%) and substance use issues (10.4% compared to 0.6%) and to smoke (62.7% compared to 24.8%) during pregnancy. The majority first engaged with maternity services within their first trimester of pregnancy (63.5% compared to 84.4%). Babies were more likely to be born preterm (14.2% compared to 6.7%) and, for full-term babies, to have low birthweights (8.0% compared to 2.8%). CONCLUSION: This novel linkage study highlights multiple vulnerabilities experienced by pregnant mothers who have experienced care proceedings concerning an infant. Policy and practice colleagues require a clearer picture of women's needs if child protection and health services are to offer effective services which prevent the need for family court proceedings and infant removal.

Actitudes antiobesidad, sintomatología alimentaria y significados asociados a la obesidad en estudiantes universitarios vinculados al área de la salud / Anti-obesity attitudes, food symptomatology and meanings associated with obesity in students linked to the health area

INTRODUCCIÓN: las personas con obesidad suelen ser objeto de estigmas asociados al peso por parte de la población. Los estudiantes del área de la salud, al formar parte de la sociedad, también experimentan actitudes antiobesidad, lo que puede afectar a la calidad de la atención que ofrecen. OBJETIVOS: analizar las diferencias por sexo, en estudiantes universitarios vinculados al área de la salud, con respecto a las actitudes antiobesidad, la obsesión por la delgadez, la bulimia y la insatisfacción corporal, identificando las variables que permitan predecir las actitudes negativas respecto a la obesidad. Además, se exploraron los significados asociados a la malnutrición por exceso. MÉTODO: con un diseño no experimental transversal y un muestreo no probabilístico por conveniencia, se accedió a 212 participantes. Los instrumentos fueron: AFA, EDI-2 y redes semánticas naturales. RESULTADOS: las mujeres presentaron valores más altos que los hombres en todas las variables estudiadas (p < 0,05), excepto en la bulimia (p > 0,05). La obsesión por la delgadez fue el único predictor de las actitudes antiobesidad (R2 = 0,40). Los significados más prevalentes hacia las personas con obesidad fueron "enfermedad" y "aspectos psicológicos". CONCLUSIONES: la presencia de obsesión por la delgadez en estudiantes que trabajarán en el abordaje integral de la obesidad es un predisponente a experimentar actitudes antiobesidad. Dentro de los significados asociados al sobrepeso más prevalentes en este grupo están los aspectos psicológicos, antes que los conceptos relacionados con la alimentación y la actividad física. Todo esto puede tener un impacto negativo en la calidad de la atención que ofrezcan

INTRODUCTION: people with obesity are often subjected to weight-related stigma by the population. Career students linked to the approach to obesity, as part of society, also experience anti-obesity attitudes, which can affect the quality of care they will offer. OBJECTIVES: to analyze the differences by sex, in university students linked to the health area, in anti-obesity attitudes, drive for thinness, bulimia, and body dissatisfaction, identifying variables allowing to predict negative attitudes regarding obesity. In addition, the meanings associated with excess malnutrition were explored. METHOD: with a cross-sectional, non-experimental design and non-probabilistic, convenience sampling 212 participants were accessed. Instruments included: AFA, EDI-2, and natural semantic networks. RESULTS: women showed higher values than men in all the variables studied (p < 0.05) except bulimia (p > 0.05). Drive for thinness was the only predictor for anti-obesity attitudes (R2 = 0.40). The most prevalent meanings attached to people with obesity were "illness" and "psychological issues". CONCLUSIONS: the presence of drive for thinness in students who will work in the comprehensive approach to obesity is a predisposing factor to experiencing anti-obesity attitudes. Among the most prevalent meanings associated with overweight in this group are psychological issues, rather than concepts related to food and physical activity. All this can have a negative impact on the quality of the care they offer

Reflections on modern methods: linkage error bias.

Linked data are increasingly being used for epidemiological research, to enhance primary research, and in planning, monitoring and evaluating public policy and services. Linkage error (missed links between records that relate to the same person or false links between unrelated records) can manifest in many ways: as missing data, measurement error and misclassification, unrepresentative sampling, or as a special combination of these that is specific to analysis of linked data: the merging and splitting of people that can occur when two hospital admission records are counted as one person admitted twice if linked and two people admitted once if not. Through these mechanisms, linkage error can ultimately lead to information bias and selection bias; so identifying relevant mechanisms is key in quantitative bias analysis. In this article we introduce five key concepts and a study classification system for identifying which mechanisms are relevant to any given analysis. We provide examples and discuss options for estimating parameters for bias analysis. This conceptual framework provides the 'links' between linkage error, information bias and selection bias, and lays the groundwork for quantitative bias analysis for linkage error.

Complexity in disease management: A linked data analysis of multimorbidity in Aboriginal and non-Aboriginal patients hospitalised with atherothrombotic disease in Western Australia.

BACKGROUND: Hospitalisation for atherothrombotic disease (ATD) is expected to rise in coming decades. However, increasingly, associated comorbidities impose challenges in managing patients and deciding appropriate secondary prevention. We investigated the prevalence and pattern of multimorbidity (presence of two or more chronic conditions) in Aboriginal and non-Aboriginal Western Australian residents with ATDs. METHODS AND FINDINGS: We used population-based de-identified linked administrative health data from 1 January 2000 to 30 June 2014 to identify a cohort of patients aged 25-59 years admitted to Western Australian hospitals with a discharge diagnosis of ATD. The prevalence of common chronic diseases in these patients was estimated and the patterns of comorbidities and multimorbidities empirically explored using two different approaches: identification of the most commonly occurring pairs and triplets of comorbid diseases, and through latent class analysis (LCA). Half of the cohort had multimorbidity, although this was much higher in Aboriginal people (Aboriginal: 79.2% vs. non-Aboriginal: 39.3%). Only a quarter were without any documented comorbidities. Hypertension, diabetes, alcohol abuse disorders and acid peptic diseases were the leading comorbidities in the major comorbid combinations across both Aboriginal and non-Aboriginal cohorts. The LCA identified four and six distinct clinically meaningful classes of multimorbidity for Aboriginal and non-Aboriginal patients, respectively. Out of the six groups in non-Aboriginal patients, four were similar to the groups identified in Aboriginal patients. The largest proportion of patients (33% in Aboriginal and 66% in non-Aboriginal) was assigned to the "minimally diseased" (or relatively healthy) group, with most patients having less than two conditions. Other groups showed variability in degree and pattern of multimorbidity. CONCLUSION: Multimorbidity is common in ATD patients and the comorbidities tend to interact and cluster together. Physicians need to consider these in their clinical practice. Different treatment and secondary prevention strategies are likely to be useful for management in these cluster groups.

Improving data workflow systems with cloud services and use of open data for bioinformatics research.

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer.

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.