BMC Pregnancy Childbirth. 2022 Oct 1;22(1):735. doi: 10.1186/s12884-022-05045-8.

ABSTRACT

BACKGROUND: International research shows marital status impacts the mental health of pregnant women, with prenatal depression and anxiety being higher among non-partnered women. However, there have been few studies examining the relationship between marital status and prenatal mental disorders among Australian women.

METHODS: This is a population-based retrospective cohort study using linked data from the New South Wales (NSW) Perinatal Data Collection (PDC) and Admitted Patients Data Collection (APDC). The cohort consists of a total of 598,599 pregnant women with 865,349 admissions. Identification of pregnant women for mental disorders was conducted using the 10th version International Classification of Diseases and Related Health Problems, Australian Modification (ICD-10-AM). A binary logistic regression model was used to estimate the relationship between marital status and prenatal mental disorder after adjusting for confounders.

RESULTS: Of the included pregnant women, 241 (0.04%), 107 (0.02%) and 4359 (0.5%) were diagnosed with depressive disorder, anxiety disorder, and self-harm, respectively. Non-partnered pregnant women had a higher likelihood of depressive disorder (Adjusted Odds Ratio (AOR) = 2.75; 95% CI: 2.04, 3.70) and anxiety disorder (AOR = 3.16, 95% CI: 2.03, 4.91), compared with partnered women. Furthermore, the likelihood of experiencing self-harm was two times higher among non-partnered pregnant women (AOR = 2.00; 95% CI: 1.82, 2.20) than partnered pregnant women.

CONCLUSIONS: Non-partnered marital status has a significant positive association with prenatal depressive disorder, anxiety disorder and self-harm. This suggests it would be highly beneficial for maternal health care professionals to screen non-partnered pregnant women for prenatal mental health problems such as depression, anxiety and self-harm.

PMID:36182904 | PMC:PMC9526285 | DOI:10.1186/s12884-022-05045-8

BMC Bioinformatics. 2022 Sep 29;23(1):400. doi: 10.1186/s12859-022-04932-3.

ABSTRACT

BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API).

RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink.

CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

PMID:36175836 | DOI:10.1186/s12859-022-04932-3

Appl Clin Inform. 2022 Aug;13(4):901-909. doi: 10.1055/s-0042-1757174. Epub 2022 Sep 28.

ABSTRACT

BACKGROUND: Chronic kidney disease (CKD) is a major global health problem that affects approximately one in 10 adults. Up to 90% of individuals with CKD go undetected until its progression to advanced stages, invariably leading to death in the absence of treatment. The project aims to fill information gaps around the burden of CKD in the Western Australian (WA) population, including incidence, prevalence, rate of progression, and economic cost to the health system.

METHODS: Given the sensitivity of the information involved, the project employed a privacy preserving record linkage methodology to link data from four major pathology providers in WA to hospital records, to establish a CKD registry with continuous medical record for individuals with biochemical specification for CKD. This method uses encrypted personal identifying information in a probability-based linkage framework (Bloom filters) to help mitigate risk while maximizing linkage quality.

RESULTS: The project developed interoperable technology to create a transparent CKD data catalogue which is linkable to other datasets. This technology has been designed to support the aspirations of the research program to provide linked de-identified pathology, morbidity, and mortality data that can be used to derive insights to enable better CKD patient outcomes. The cohort includes over 1 million individuals with creatinine results over the period 2002 to 2021.

CONCLUSION: Using linked data from across the care continuum, researchers are able to evaluate the effectiveness of service delivery and provide evidence for policy and program development. The CKD registry will enable an innovative review of the epidemiology of CKD in WA. Linking pathology records can identify cases of CKD that are missed in the early stages due to disaggregation of results, enabling identification of at-risk populations that represent targets for early intervention and management.

PMID:36170880 | DOI:10.1055/s-0042-1757174

CMAJ Open. 2022 Sep 27;10(3):E841-E847. doi: 10.9778/cmajo.20210257. Print 2022 Sep-Oct.

ABSTRACT

BACKGROUND: In Quebec, antibiotic use is higher among outpatients with chronic diseases. We sought to measure compliance with provincial guidelines for the treatment of otitis media and common respiratory infections, and to measure variations in compliance according to the presence of certain chronic diseases.

METHODS: We conducted a population-based study of linked data on antibiotic dispensing covered by the public drug insurance plan between April 2010 and March 2017. We included patients who had consulted a primary care physician within 2 days before being dispensed an antibiotic for an infection targeted by provincial guidelines, including bronchitis in patients with chronic obstructive pulmonary disease, otitis media, pharyngitis, pneumonia and sinusitis. We computed proportions of prescriptions compliant with guidelines (use of recommended antibiotic for children, and use of recommended antibiotic and dosage for adults) by age group (children or adults) and chronic disease (respiratory, cardiovascular, diabetes, mental disorder or none). We measured the impact of chronic diseases on compliance using robust Poisson regression.

RESULTS: We analyzed between 14 677 and 198 902 prescriptions for each infection under study. Compliance was greater than 87% among children, but was lower among children with asthma (proportion ratios between 0.97 and 1.00). In adults, the chosen antibiotic was compliant for at least 73% of prescriptions, except for pharyngitis (≤ 61%). Accounting for dosage lowered compliance to between 31% and 61%. Compliance was lower in the presence of chronic diseases (proportion ratios between 0.94 and 0.98).

INTERPRETATION: It is possible that prescribing noncompliant prescriptions was sometimes appropriate, but the high frequency of noncompliance suggests room for improvement. Given that variations associated with chronic diseases were small, disease-specific guidelines for antibiotic prescriptions are likely to have a limited impact on compliance.

PMID:36167419 | DOI:10.9778/cmajo.20210257

BMC Health Serv Res. 2022 Sep 21;22(1):1180. doi: 10.1186/s12913-022-08565-3.

ABSTRACT

AIM: Increasing access to long-acting modern contraceptives (LMAC) is one of the key factors in preventing unintended pregnancy and protecting women's health rights. However, the availability and accessibility of health facilities and their impacts on LAMC utilisation (implant, intrauterine devices, sterilisation) in low- and middle-income countries is an understudied topic. This study aimed to examine the association between the availability and readiness of health facilities and the use of LAMC in Bangladesh.

METHODS: In this survey study, we linked the 2017/18 Bangladesh Demographic and Health Survey data with the 2017 Bangladesh Health Facility Survey data using the administrative-boundary linkage method. Mixed-effect multilevel logistic regressions were conducted. The sample comprised 10,938 married women of 15-49 years age range who were fertile but did not desire a child within 2 years of the date of survey. The outcome variable was the current use of LAMC (yes, no), and the explanatory variables were health facility-, individual-, household- and community-level factors.

RESULTS: Nearly 34% of participants used LAMCs with significant variations across areas in Bangladesh. The average scores of the health facility management and health facility infrastructure were 0.79 and 0.83, respectively. Of the facilities where LAMCs were available, 69% of them were functional and ready to provide LAMCs to the respondents. The increase in scores for the management (adjusted odds ratio (aOR), 1.59; 95% CI, 1.21-2.42) and infrastructure (aOR, 1.44; 95% CI, 1.01-1.69) of health facilities was positively associated with the overall uptake of LAMC. For per unit increase in the availability and readiness scores to provide LAMC at the nearest health facilities, the aORs for women to report using LAMC were 2.16 (95% CI, 1.18-3.21) and 1.74 (95% CI, 1.15-3.20), respectively. A nearly 27% decline in the likelihood of LAMC uptake was observed for every kilometre increase in the average regional-level distance between women's homes and the nearest health facilities.

CONCLUSION: The proximity of health facilities and their improved management, infrastructure, and readiness to provide LAMCs to women significantly increase their uptake. Policies and programs should prioritise improving health facility readiness to increase LAMC uptake.

PMID:36131314 | DOI:10.1186/s12913-022-08565-3

Behav Res Methods. 2022 Sep 21. doi: 10.3758/s13428-022-01965-7. Online ahead of print.

ABSTRACT

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .

PMID:36131199 | DOI:10.3758/s13428-022-01965-7

J Glob Health. 2022 Sep 17;12:04081. doi: 10.7189/jogh.12.04081.

ABSTRACT

BACKGROUND: Under-five mortality is unacceptably high in Bangladesh instead of governmental level efforts to reduce its prevalence over the years. Increased availability and accessibility to the health care facility and its services can play a significant role to reduce its occurrence. We explored the associations of several forms of child mortality with health facility level factors.

METHODS: The 2017-18 Bangladesh Demographic and Health Survey (BDHS) data and 2017 Bangladesh Health Facility Survey (BHFS) data were linked and analysed. The outcome variables were neonatal mortality, infant mortality, and under-five mortality. Health facility level factors were considered as major explanatory variables. They were the basic management and administrative system of the nearest health care facility where child health care services are available, degree of availability of the child health care services at the nearest health care facility, degree of readiness of the nearest health care facility (where child health care services are available) to provide child health care services and average distance of the nearest health care facility from mothers' homes where child health care services are available. The associations between the outcome variables and explanatory variables were determined using the multilevel mixed-effect logistic regression model.

RESULTS: Reported under-five, infant and neonatal mortality were 40, 27, and 22 per 10 000 live births, respectively. The likelihood of neonatal mortality was found to be declined by 15% for every unit increase in the score of the basic management and administrative system of the mothers' homes nearest health care facility where child health care services are available. Similarly, degree of availability and readiness of the mothers' homes nearest health care facilities to provide child health care services were found to be linked with 18%-24% reduction in neonatal and infant mortality. On contrary, for every kilometre increased distance between mothers' homes and its nearest health care facility was found to be associated with a 15%-20% increase in the likelihoods of neonatal, infant and under-five mortality.

CONCLUSIONS: The availability of health facilities providing child health care services close to mothers' residence and its readiness to provide child health care services play a significant role in reducing under-five mortality in Bangladesh. Policies and programs should be taken to increase the availability and accessibility of health facilities that provide child health care services.

PMID:36112406 | DOI:10.7189/jogh.12.04081

Proc Int World Wide Web Conf. 2022 Apr;2022:1037-1046. doi: 10.1145/3485447.3511946. Epub 2022 Apr 25.

ABSTRACT

The Unified Medical Language System (UMLS) Metathesaurus construction process mainly relies on lexical algorithms and manual expert curation for integrating over 200 biomedical vocabularies. A lexical-based learning model (LexLM) was developed to predict synonymy among Metathesaurus terms and largely outperforms a rule-based approach (RBA) that approximates the current construction process. However, the LexLM has the potential for being improved further because it only uses lexical information from the source vocabularies, while the RBA also takes advantage of contextual information. We investigate the role of multiple types of contextual information available to the UMLS editors, namely source synonymy (SS), source semantic group (SG), and source hierarchical relations (HR), for the UMLS vocabulary alignment (UVA) problem. In this paper, we develop multiple variants of context-enriched learning models (ConLMs) by adding to the LexLM the types of contextual information listed above. We represent these context types in context-enriched knowledge graphs (ConKGs) with four variants ConSS, ConSG, ConHR, and ConAll. We train these ConKG embeddings using seven KG embedding techniques. We create the ConLMs by concatenating the ConKG embedding vectors with the word embedding vectors from the LexLM. We evaluate the performance of the ConLMs using the UVA generalization test datasets with hundreds of millions of pairs. Our extensive experiments show a significant performance improvement from the ConLMs over the LexLM, namely +5.0% in precision (93.75%), +0.69% in recall (93.23%), +2.88% in F1 (93.49%) for the best ConLM. Our experiments also show that the ConAll variant including the three context types takes more time, but does not always perform better than other variants with a single context type. Finally, our experiments show that the pairs of terms with high lexical similarity benefit most from adding contextual information, namely +6.56% in precision (94.97%), +2.13% in recall (93.23%), +4.35% in F1 (94.09%) for the best ConLM. The pairs with lower degrees of lexical similarity also show performance improvement with +0.85% in F1 (96%) for low similarity and +1.31% in F1 (96.34%) for no similarity. These results demonstrate the importance of using contextual information in the UVA problem.

PMID:36108322 | PMC:PMC9455675 | DOI:10.1145/3485447.3511946

Distrib Parallel Databases. 2022;40(2-3):409-440. doi: 10.1007/s10619-022-07414-w. Epub 2022 Jul 16.

ABSTRACT

The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available. In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our experiments with real-world datasets across several scientific domains, including the official bioinformatics Question Answering over Linked Data (QALD) challenge, as well as the CORDIS dataset of European projects, show that Bio-SODA outperforms publicly available KGQA systems by an F1-score of least 20% and by an even higher factor on more complex bioinformatics datasets. Finally, we introduce Bio-SODA UX, a graphical user interface designed to assist users in the exploration of large knowledge graphs and in dynamically disambiguating natural language questions that target the data available in these graphs.

PMID:36097541 | PMC:PMC9458692 | DOI:10.1007/s10619-022-07414-w

eNeuro. 2022 Sep 21;9(5):ENEURO.0116-22.2022. doi: 10.1523/ENEURO.0116-22.2022. Print 2022 Sep-Oct.

ABSTRACT

To understand a dialogue, we need to know the topics that are being discussed. This enables us to integrate our knowledge of what was said previously to interpret the current dialogue. This study involved a large-scale behavioral experiment conducted online and a separate fMRI experiment, both testing human participants. In both, we selectively manipulated knowledge about the narrative content of dialogues presented in short videos. The clips were scenes from situation comedies that were split into two parts. The speech in the part 1 clips could either be presented normally or spectrally rotated to render it unintelligible. The part 2 clips that concluded the scenes were always presented normally. The behavioral experiment showed that knowledge of the preceding narrative boosted memory for the part 2 clips as well as increased the intersubject semantic similarity of recalled descriptions of the dialogues. The fMRI experiment replicated the finding that prior knowledge improved memory for the conclusions of the dialogues. Furthermore, prior knowledge strengthened temporal intersubject correlations in brain regions including the left angular gyrus and inferior frontal gyrus. Together, these findings show that (1) prior knowledge constrains the interpretation of a dialogue to be more similar across individuals; and (2), consistent with this, the activation of brain regions involved in semantic control processing is also more similar between individuals who share the same prior knowledge. Processing in these regions likely supports the activation and integration of prior knowledge, which helps people to better understand and remember dialogues as they unfold.

PMID:36096648 | PMC:PMC9491346 | DOI:10.1523/ENEURO.0116-22.2022

J Biomed Inform. 2022 Sep 8:104201. doi: 10.1016/j.jbi.2022.104201. Online ahead of print.

ABSTRACT

BACKGROUND: Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated.

OBJECTIVE: To develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification.

METHODS: We developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification.

RESULTS: A beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification.

CONCLUSION: The FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

PMID:36089199 | DOI:10.1016/j.jbi.2022.104201

Q J Exp Psychol (Hove). 2022 Sep 3:17470218221125425. doi: 10.1177/17470218221125425. Online ahead of print.

ABSTRACT

The role of meaning facets based on sensorimotor experiences is well-investigated in comprehension but has received little attention in language production research. In two experiments, we investigated whether experiential traces of space influenced lexical choices when participants completed visually-presented sentence fragments (e.g., 'You are at the sea and you see a ...') with spoken nouns (e.g., 'dolphin', 'palm tree'). The words were presented consecutively in an ascending or descending direction, starting from the center of the screen. These physical spatial cues did not influence lexical choices. However, the produced nouns met the spatial characteristics of the broader sentence contexts such that the typical spatial locations of the produced noun referents were predicted by the location of the situations described by the sentence fragments (i.e., upper or lower sphere). By including distributional semantic similarity measures derived from computing cosine values between sentence nouns and produced nouns using a web-based text corpus, we show that the meaning dimension of 'location in space' guides lexical selection during speaking. We discuss the relation of this spatial meaning dimension to accounts of experientially grounded and usage-based theories of language processing and their combination in hybrid approaches. In doing so, we contribute to a more comprehensive understanding of the many facets of meaning processing during language production and their impact on the words we select to express verbal messages.

PMID:36062350 | DOI:10.1177/17470218221125425

Front Genet. 2022 Aug 19;13:985500. doi: 10.3389/fgene.2022.985500. eCollection 2022.

ABSTRACT

New technologies, such as next-generation sequencing, have advanced the ability to diagnose diseases and improve prognosis but require the identification of thousands of variants in each report based on several databases scattered across places. Curating an integrated interpretation database is time-consuming, costly, and needs regular update. On the other hand, the automatic curation of knowledge sources always results in overloaded information. In this study, an automated pipeline was proposed to create an integrated visual single-nucleotide polymorphism (SNP) interpretation tool called SNPMap. SNPMap pipelines periodically obtained SNP-related information from LitVar, PubTator, and GWAS Catalog API tools and presented it to the user after extraction, integration, and visualization. Keywords and their semantic relations to each SNP are rendered into two graphs, with their significance represented by the size/width of circles/lines. Moreover, the most related SNPs for each keyword that appeared in SNPMap were calculated and sorted. SNPMap retains the advantage of an automatic process while assisting users in accessing more lucid and detailed information through visualization and integration with other materials.

PMID:36061173 | PMC:PMC9437274 | DOI:10.3389/fgene.2022.985500

World Wide Web. 2022 Aug 27:1-18. doi: 10.1007/s11280-022-01013-6. Online ahead of print.

ABSTRACT

Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists' working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.

PMID:36060430 | PMC:PMC9417931 | DOI:10.1007/s11280-022-01013-6

Front Big Data. 2022 Aug 17;5:965715. doi: 10.3389/fdata.2022.965715. eCollection 2022.

ABSTRACT

Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.

PMID:36059922 | PMC:PMC9428292 | DOI:10.3389/fdata.2022.965715

Plant Phenomics. 2022 May 18;2022:9893639. doi: 10.34133/2022/9893639. eCollection 2022.

ABSTRACT

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

PMID:36059601 | PMC:PMC9394117 | DOI:10.34133/2022/9893639

Comput Intell Neurosci. 2022 Aug 17;2022:5764148. doi: 10.1155/2022/5764148. eCollection 2022.

ABSTRACT

This work intends to classify and integrate music genres and emotions to improve the quality of music education. This work proposes a web image education resource retrieval method based on semantic network and interactive image filtering for a music education environment. It makes a judgment on these music source data and then uses these extracted feature sequences as the emotions expressed in the model of the combination of Long Short-Term Memory (LSTM) and Attention Mechanism (AM), thus judging the emotion category of music. The emotion recognition accuracy has increased after improving LSTM-AM into the BiGR-AM model. The greater the difference between emotion genres is, the easier it is to analyze the feature sequence containing emotion features, and the higher the recognition accuracy is. The classification accuracy of the excited, relieved, relaxed, and sad emotions can reach 76.5%, 71.3%, 80.8%, and 73.4%, respectively. The proposed interactive filtering method based on a Convolutional Recurrent Neural Network can effectively classify and integrate music resources to improve the quality of music education.

PMID:36035856 | PMC:PMC9402344 | DOI:10.1155/2022/5764148

Front Plant Sci. 2022 Aug 11;13:972286. doi: 10.3389/fpls.2022.972286. eCollection 2022.

ABSTRACT

Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m2 of paddy fields. An improved model YOLO-Diseases and Pests Detection (YOLO-DPD) was proposed to detect three lesions of Cnaphalocrocis medinalis, Chilo suppressalis, and Ustilaginoidea virens on rice canopy. The residual feature augmentation method was used to narrow the semantic gap between different scale features of rice disease and pest images. The convolution block attention module was added into the backbone network to enhance the regional disease and pest features for suppressing the background noises. Our experiments demonstrated that the improved model YOLO-DPD could detect three species of disease and pest lesions on rice canopy at different image scales with an average precision of 92.24, 87.35 and 90.74%, respectively, and a mean average precision of 90.11%. Compared to RetinaNet, Faster R-CNN and Yolov4 models, the mean average precision of YOLO-DPD increased by 18.20, 6.98, 6.10%, respectively. The average detection time of each image is 47 ms. Our system has the advantages of unattended operation, high detection precision, objective results, and data traceability.

PMID:36035691 | PMC:PMC9403268 | DOI:10.3389/fpls.2022.972286

Comput Math Methods Med. 2022 Aug 17;2022:9093262. doi: 10.1155/2022/9093262. eCollection 2022.

ABSTRACT

As more drugs are developed and the incidence of polypharmacy increases, it is becoming critically important to anticipate potential DDIs before they occur in the clinic, along with those for which effects might go unobserved. However, traditional methods for DDI identification are unable to coalesce interaction mechanisms out of vast lists of potential or known DDIs, much less study them accurately. Computational methods have great promise but have realized only limited clinical utility. This work develops a rule-based inference framework to predict DDI mechanisms and support determination of their clinical relevance. Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. The framework was evaluated through a case study combining a chemotherapy agent, irinotecan, and a widely used antibiotic, levofloxacin. The mutual interactions identified indicate that our framework can effectively explore and explain the mechanisms of potential DDIs. This approach has the potential to improve drug discovery and design and to support rapid and cost-effective identification of DDIs along with their putative mechanisms, a key step in determining clinical relevance and supporting clinical decision-making.

PMID:36035294 | PMC:PMC9402322 | DOI:10.1155/2022/9093262

Math Biosci Eng. 2022 Jul 25;19(10):10445-10473. doi: 10.3934/mbe.2022489.

ABSTRACT

In clinical decision support, argumentation plays a key role while alternative reasons may be available to explain a given set of signs and symptoms, or alternative plans to treat a diagnosed disease. In literature, this key notion usually has closed boundary across approaches and lacks of openness and interoperability in Clinical Decision Support Systems (CDSSs) been built. In this paper, we propose a systematic approach for the representation of argumentation, their interpretation towards recommendation, and finally explanation in clinical decision support. A generic argumentation and recommendation scheme lays the foundation of the approach. On the basis of this, argumentation rules are represented using Resource Description Framework (RDF) for clinical guidelines, a rule engine developed for their interpretation, and recommendation rules represented using Semantic Web Rule Language (SWRL). A pair of proof knowledge graphs are made available in an integrated clinical decision environment to explain the argumentation and recommendation rationale, so that decision makers are informed of not just what are recommended but also why. A case study of triple assessment, a common procedure in the National Health Service of UK for women suspected of breast cancer, is used to demonstrate the feasibility of the approach. In conducting hypothesis testing, we evaluate the metrics of accuracy, variation, adherence, time, satisfaction, confidence, learning, and integration of the prototype CDSS developed for the case study in comparison with a conventional CDSS and also human clinicians without CDSS. The results are presented and discussed.

PMID:36032002 | DOI:10.3934/mbe.2022489