Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach.

BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge. METHODS: Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021. Sociodemographic variables, admission history, and clinical information were used as potential predictors. The performance was compared to LASSO regression, as well as the HOSPITAL and LACE risk score indices. Important risk factors identified by the gradient-boosting machine model were explored using Shapley values. RESULTS: The models performed well, especially for the mortality outcomes. Area under the receiver operating characteristic curve values were 0.70 for readmission and 0.87-0.90 for mortality using the full gradient boosting machine algorithms. Among the top predictors for 30-day and 180-day readmission were increased red cell distribution width, old age (especially above 80 years), high measured troponin and urea levels, not being married or in a relationship, and low albumin levels. For mortality, these included increased red cell distribution width, old age (especially older than 70 years), high measured troponin and urea levels, high neutrophil and monocyte counts, and low eosinophil and lymphocyte counts. The Shapley values gave clear insight into the dynamics of decision-tree-based models. CONCLUSIONS: We demonstrated an explainable predictive algorithm to identify cardiovascular patients who are at high risk of readmission or death at discharge from the hospital and identified key risk factors.

Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study.

BACKGROUND: The narrative free-text data in electronic medical records (EMRs) contain valuable clinical information for analysis and research to inform better patient care. However, the release of free text for secondary use is hindered by concerns surrounding personally identifiable information (PII), as protecting individuals' privacy is paramount. Therefore, it is necessary to deidentify free text to remove PII. Manual deidentification is a time-consuming and labor-intensive process. Numerous automated deidentification approaches and systems have been attempted to overcome this challenge over the past decade. OBJECTIVE: We sought to develop an accurate, web-based system deidentifying free text (DEFT), which can be readily and easily adopted in real-world settings for deidentification of free text in EMRs. The system has several key features including a simple and task-focused web user interface, customized PII types, use of a state-of-the-art deep learning model for tagging PII from free text, preannotation by an interactive learning loop, rapid manual annotation with autosave, support for project management and team collaboration, user access control, and central data storage. METHODS: DEFT comprises frontend and backend modules and communicates with central data storage through a filesystem path access. The frontend web user interface provides end users with a user-friendly workspace for managing and annotating free text. The backend module processes the requests from the frontend and performs relevant persistence operations. DEFT manages the deidentification workflow as a project, which can contain one or more data sets. Customized PII types and user access control can also be configured. The deep learning model is based on a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) with RoBERTa as the word embedding layer. The interactive learning loop is further integrated into DEFT to speed up the deidentification process and increase its performance over time. RESULTS: DEFT has many advantages over existing deidentification systems in terms of its support for project management, user access control, data management, and an interactive learning process. Experimental results from DEFT on the 2014 i2b2 data set obtained the highest performance compared to 5 benchmark models in terms of microaverage strict entity-level recall and F1-scores of 0.9563 and 0.9627, respectively. In a real-world use case of deidentifying clinical notes, extracted from 1 referral hospital in Sydney, New South Wales, Australia, DEFT achieved a high microaverage strict entity-level F1-score of 0.9507 on a corpus of 600 annotated clinical notes. Moreover, the manual annotation process with preannotation demonstrated a 43% increase in work efficiency compared to the process without preannotation. CONCLUSIONS: DEFT is designed for health domain researchers and data custodians to easily deidentify free text in EMRs. DEFT supports an interactive learning loop and end users with minimal technical knowledge can perform the deidentification work with only a shallow learning curve.

GrainGenes: a data-rich repository for small grains genetics and genomics.

As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.

GrainGenes: Tools and Content to Assist Breeders Improving Oat Quality.

GrainGenes is the USDA-ARS database and Web resource for wheat, barley, oat, rye, and their relatives. As a community Web hub and database for small grains, GrainGenes strives to provide resources for researchers, students, and plant breeders to improve traits such as quality, yield, and disease resistance. Quantitative trait loci (QTL), genes, and genetic maps for quality attributes in GrainGenes represent the historical approach to mapping genes for groat percentage, test weight, protein, fat, and ß-glucan content in oat (Avena spp.). Genetic maps are viewable in CMap, the comparative mapping tool that enables researchers to take advantage of highly populated consensus maps to increase the marker density around their genes-of-interest. GrainGenes hosts over 50 genome browsers and is launching an effort for community curation, including the manually curated tracks with beta-glucan QTL and significant markers found via GWAS and cloned cellulose synthase-like AsClF6 alleles.

Formation of a statewide community pharmacy practice-based research network: Pharmacist opinions on research participation and engagement.

OBJECTIVE: To gather the insights and opinions of pharmacist stakeholders to inform the creation of a community pharmacy practice-based research network (PBRN) in Pennsylvania. DESIGN: A stakeholder advisory board of pharmacists, patients, and researchers was established to guide this research. This was a qualitative study using a semistructured interview guide. SETTING AND PARTICIPANTS: Community pharmacists from the Pennsylvania Pharmacist Care Network. OUTCOME MEASURES: Themes were identified that describe pharmacist insights and opinions on research participation and preferences for engagement in the PBRN. RESULTS: A total of 16 pharmacists participated in the study. The pharmacists believed that participating in research would help demonstrate their value and commitment to improving patients' health. Enhancing patient-pharmacist relationships and driving innovation were additional benefits that were reported. The pharmacists believed that they could effectively leverage their relationships with patients to engage them in research opportunities. The pharmacists reported that they would like to share research ideas and successful research practices with other members of the PBRN. CONCLUSION: Gathering pharmacists' opinions on participating in research was an important step in developing a community pharmacy PBRN that meets stakeholder needs. The results of this study can help others who seek to form community pharmacy PBRNs that facilitate stakeholder-driven research.

Positive evidence for neonatal imitation: A general response, adaptive engagement.

The study employed four gestural models using frame-by-frame microanalytic methods, and followed how the behaviours unfolded over time. Forty-two human newborns (0-3 days) were examined for their imitation of tongue protrusion, 'head tilt with looking up', three-finger and two-finger gestures. The results showed that all three gesture groups were imitated. Results of the temporal analyses revealed an early and a later, second stage of responses. Later responses were characterized by a suppression of similar, but non-matching movements. Perinatal imitation is not a phenomenon served by a single underlying mechanism; it has at least two different stages. An early phase is followed by voluntary matching behaviour by the neonatal infant.

An evaluation of behaviour change techniques in health and lifestyle mobile applications.

Despite the current popularity and potential use of mobile applications (apps) in the area of behaviour change, health promotion, and well-being for young people, it is unclear whether their design is underpinned by theory-based behaviour change techniques. Understanding the design of these apps may improve the way they can be used to support young people's well-being.The objectives of this study were to investigate what behaviour change techniques are included in the content of health and lifestyle apps, and determine which of these are prominent in app design. Thirty of the top-listed health and lifestyle apps across three categories (physical activity, diet, and sleep) were freely downloaded from the two most popular app stores (GooglePlay™ and AppStore™). Selected apps were used by trained researchers and the features identified coded against the Behaviour Change Techniques Taxonomy 1, a systematic classification of techniques used in behaviour change interventions. It was found that 9 of the 93 behaviour change techniques listed in the Behaviour Change Techniques Taxonomy 1 were common across the chosen health and lifestyle apps. The app found to include the most behaviour change techniques had 20 (21%), while the app found to include the least had 1 behaviour change technique (1%). The most frequently used behaviour change techniques were related to goal setting and feedback. Entire categories in the Behaviour Change Techniques Taxonomy 1 were absent in the design of the selected apps.

GrainGenes: centralized small grain resources and digital platform for geneticists and breeders.

GrainGenes (https://wheat.pw.usda.gov or https://graingenes.org) is an international centralized repository for curated, peer-reviewed datasets useful to researchers working on wheat, barley, rye and oat. GrainGenes manages genomic, genetic, germplasm and phenotypic datasets through a dynamically generated web interface for facilitated data discovery. Since 1992, GrainGenes has served geneticists and breeders in both the public and private sectors on six continents. Recently, several new datasets were curated into the database along with new tools for analysis. The GrainGenes homepage was enhanced by making it more visually intuitive and by adding links to commonly used pages. Several genome assemblies and genomic tracks are displayed through the genome browsers at GrainGenes, including the Triticum aestivum (bread wheat) cv. 'Chinese Spring' IWGSC RefSeq v1.0 genome assembly, the Aegilops tauschii (D genome progenitor) Aet v4.0 genome assembly, the Triticum turgidum ssp. dicoccoides (wild emmer wheat) cv. 'Zavitan' WEWSeq v.1.0 genome assembly, a T. aestivum (bread wheat) pangenome, the Hordeum vulgare (barley) cv. 'Morex' IBSC genome assembly, the Secale cereale (rye) select 'Lo7' assembly, a partial hexaploid Avena sativa (oat) assembly and the Triticum durum cv. 'Svevo' (durum wheat) RefSeq Release 1.0 assembly. New genetic maps and markers were added and can be displayed through CMAP. Quantitative trait loci, genetic maps and genes from the Wheat Gene Catalogue are indexed and linked through the Wheat Information System (WheatIS) portal. Training videos were created to help users query and reach the data they need. GSP (Genome Specific Primers) and PIECE2 (Plant Intron Exon Comparison and Evolution) tools were implemented and are available to use. As more small grains reference sequences become available, GrainGenes will play an increasingly vital role in helping researchers improve crops.

Designing Online Interventions in Consideration of Young People's Concepts of Well-Being: Exploratory Qualitative Study.

BACKGROUND: A key challenge in developing online well-being interventions for young people is to ensure that they are based on theory and reflect adolescent concepts of well-being. OBJECTIVE: This exploratory qualitative study aimed to understand young people's concepts of well-being in Australia. METHODS: Data were collected via workshops at five sites across rural and metropolitan sites with 37 young people from 15 to 21 years of age, inclusive. Inductive, data-driven coding was then used to analyze transcripts and artifacts (ie, written or image data). RESULTS: Young adults' conceptions of well-being were diverse, personally contextualized, and shaped by ongoing individual experiences related to physical and mental health, along with ecological accounts acknowledging the role of family, community, and social factors. Key emerging themes were (1) positive emotions and enjoyable activities, (2) physical wellness, (3) relationships and social connectedness, (4) autonomy and control, (5) goals and purpose, (6) being engaged and challenged, and (7) self-esteem and confidence. Participants had no difficulty describing actions that led to positive well-being; however, they only considered their own well-being at times of stress. CONCLUSIONS: In this study, young people appeared to think mostly about their well-being at times of stress. The challenge for online interventions is to encourage young people to monitor well-being prior to it becoming compromised. A more proactive focus that links the overall concept of well-being to everyday, concrete actions and activities young people engage in, and that encourages the creation of routine good habits, may lead to better outcomes from online well-being interventions.

Facilitating mental health help-seeking by young adults with a dedicated online program: a feasibility study of Link.

OBJECTIVE: To explore the feasibility of a dedicated online youth mental health help-seeking intervention and to evaluate using a randomised controlled trial (RCT) study design in order to identify any modifications needed before commencement of the full-scale RCT. DESIGN: A pilot RCT with 1:1 randomisation to either the intervention or comparison arm. SETTING: An online study conducted Australia-wide. PARTICIPANTS: 18-25 year olds living in Australia were recruited via social media. INTERVENTION: Link is a dedicated online mental health help-seeking navigation tool that matches user's mental health issues, severity and service-type preferences (online, phone and face-to-face) with appropriate youth-friendly services. The comparison arm was usual help-seeking strategies with a link to Google.com. MAIN OUTCOME MEASURES: The primary outcome was the number of acceptability and feasibility criteria successfully met. Intervention and study design acceptability and feasibility were assessed by nine criteria. Secondary outcomes, via online surveys (at baseline, 1 week and 1 month) measured service use, help-seeking intentions, psychological distress, barriers to help-seeking, attitudes towards mental health help-seeking, mental health literacy, satisfaction and trust. RESULTS: Fifty-one participants were randomised (intervention: n=24; comparison: n=27). Three out of four of the intervention and two out of five of the study design criteria were met. Unmet criteria could be addressed by modifications to the study design. Qualitative analysis demonstrated that Link was useful to participants and may have increased their positive experiences towards help-seeking. There were no observable differences between arms in any outcome measures and no harms were detected. CONCLUSION: Generally, the Link intervention and study design were acceptable and feasible with modifications suggested for the four out of nine unmet criteria. The main trial will hence have shorter surveys and a simpler recruitment process, use positive affect as the primary outcome and will not link to Google.com for the comparison arm. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry, ACTRN12614000386639.

Efficacy of a Web-Based Guided Recommendation Service for a Curated List of Readily Available Mental Health and Well-Being Mobile Apps for Young People: Randomized Controlled Trial.

BACKGROUND: Mental disorders are highly prevalent for the people who are aged between 16 and 25 years and can permanently disrupt the development of these individuals. Easily available mobile health (mHealth) apps for mobile phones have great potential for the prevention and early intervention of mental disorders in young adults, but interventions are required that can help individuals to both identify high-quality mobile apps and use them to change health and lifestyle behavior. OBJECTIVES: The study aimed to assess the efficacy of a Web-based self-guided app recommendation service ("The Toolbox") in improving the well-being of young Australians aged between 16 and 25 years. The intervention was developed in collaboration with young adults and consists of a curated list of 46 readily available health and well-being apps, assessed and rated by professionals and young people. Participants are guided by an interactive quiz and subsequently receive recommendations for particular apps to download and use based on their personal goals. METHODS: The study was a waitlist, parallel-arm, randomized controlled trial. Our primary outcome measure was change in well-being as measured by the Mental Health Continuum-Short Form (MHC-SF). We also employed ecological momentary assessments (EMAs) to track mood, energy, rest, and sleep. Participants were recruited from the general Australian population, via several Web-based and community strategies. The study was conducted through a Web-based platform consisting of a landing Web page and capabilities to administer study measures at different time points. Web-based measurements were self-assessed at baseline and 4 weeks, and EMAs were collected repeatedly at regular weekly intervals or ad hoc when participants interacted with the study platform. Primary outcomes were analyzed using linear mixed-models and intention-to-treat (ITT) analysis. RESULTS: A total of 387 participants completed baseline scores and were randomized into the trial. Results demonstrated no significant effect of "The Toolbox" intervention on participant well-being at 4 weeks compared with the control group (P=.66). There were also no significant differences between the intervention and control groups at 4 weeks on any of the subscales of the MHC-SF (psychological: P=.95, social: P=.42, emotional: P=.95). Repeat engagement with the study platform resulted in a significant difference in mood, energy, rest, and sleep trajectories between intervention and control groups as measured by EMAs (P<.01). CONCLUSIONS: This was the first study to assess the effectiveness of a Web-based well-being intervention in a sample of young adults. The design of the intervention utilized expert rating of existing apps and end-user codesign approaches resulting in an app recommendation service. Our finding suggests that recommended readily available mental health and well-being apps may not lead to improvements in the well-being of a nonclinical sample of young people, but might halt a decline in mood, energy, rest, and sleep. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12614000710628; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366145 (Archived by WebCite at http://www.webcitation.org/ 6pWDsnKme).

The Triticeae Toolbox: Combining Phenotype and Genotype Data to Advance Small-Grains Breeding.

The Triticeae Toolbox (; T3) is the database schema enabling plant breeders and researchers to combine, visualize, and interrogate the wealth of phenotype and genotype data generated by the Triticeae Coordinated Agricultural Project (TCAP). T3 enables users to define specific data sets for download in formats compatible with the external tools TASSEL, Flapjack, and R; or to use by software residing on the T3 server for operations such as Genome Wide Association and Genomic Prediction. New T3 tools to assist plant breeders include a Selection Index Generator, analytical tools to compare phenotype trials using common or user-defined indices, and a histogram generator for nursery reports, with applications using the Android OS, and a Field Plot Layout Designer in development. Researchers using T3 will soon enjoy the ability to design training sets, define core germplasm sets, and perform multivariate analysis. An increased collaboration with GrainGenes and integration with the small grains reference sequence resources will place T3 in a pivotal role for on-the-fly data analysis, with instant access to the knowledge databases for wheat and barley. T3 software is available under the GNU General Public License and is freely downloadable.

An ancient yet flexible cis-regulatory architecture allows localized Hedgehog tuning by patched/Ptch1.

The Hedgehog signaling pathway is part of the ancient developmental-evolutionary animal toolkit. Frequently co-opted to pattern new structures, the pathway is conserved among eumetazoans yet flexible and pleiotropic in its effects. The Hedgehog receptor, Patched, is transcriptionally activated by Hedgehog, providing essential negative feedback in all tissues. Our locus-wide dissections of the cis-regulatory landscapes of fly patched and mouse Ptch1 reveal abundant, diverse enhancers with stage- and tissue-specific expression patterns. The seemingly simple, constitutive Hedgehog response of patched/Ptch1 is driven by a complex regulatory architecture, with batteries of context-specific enhancers engaged in promoter-specific interactions to tune signaling individually in each tissue, without disturbing patterning elsewhere. This structure-one of the oldest cis-regulatory features discovered in animal genomes-explains how patched/Ptch1 can drive dramatic adaptations in animal morphology while maintaining its essential core function. It may also suggest a general model for the evolutionary flexibility of conserved regulators and pathways.

Quantitative Trait Loci Associated with the Tocochromanol (Vitamin E) Pathway in Barley.

The Genome-Wide Association Studies approach was used to detect Quantitative Trait Loci associated with tocochromanol concentrations using a panel of 1,466 barley accessions. All major tocochromanol types- α-, ß-, δ-, γ-tocopherol and tocotrienol- were assayed. We found 13 single nucleotide polymorphisms associated with the concentration of one or more of these tocochromanol forms in barley, seven of which were within 2 cM of sequences homologous to cloned genes associated with tocochromanol production in barley and/or other plants. These associations confirmed a prior report based on bi-parental QTL mapping. This knowledge will aid future efforts to better understand the role of tocochromanols in barley, with specific reference to abiotic stress resistance. It will also be useful in developing barley varieties with higher tocochromanol concentrations, although at current recommended daily consumption amounts, barley would not be an effective sole source of vitamin E. However, it could be an important contributor in the context of whole grains in a balanced diet.

Development of an online well-being intervention for young people: an evaluation protocol.

BACKGROUND: Research has shown that improving well-being using positive mental health interventions can be useful for predicting and preventing mental illness. Implementing online interventions may be an effective way to reach young people, given their familiarity with technology. OBJECTIVE: This study will assess the effectiveness of a website called the "Online Wellbeing Centre (OWC)," designed for the support and improvement of mental health and well-being in young Australians aged between 16 and 25 years. As the active component of the study, the OWC will introduce a self-guided app recommendation service called "The Toolbox: The best apps for your brain and body" developed by ReachOut.com. The Toolbox is a responsive website that serves as a personalized, ongoing recommendation service for technology-based tools and apps to improve well-being. It allows users to personalize their experience according to their individual needs. METHODS: This study will be a two-arm, randomized controlled trial following a wait-list control design. The primary outcome will be changes in psychological well-being measured by the Mental Health Continuum Short Form. The secondary outcomes will be drawn from a subsample of participants and will include depression scores measured by the Center for Epidemiologic Studies Depression Scale, and quality of life measured by the Assessment of Quality of Life-four dimensions (AQOL-4D) index. Cost-effectiveness analysis will be conducted based on a primary outcome of cost per unique visit to the OWC. Utility-based outcomes will also be incorporated into the analysis allowing a secondary outcome to be cost per quality-adjusted life year gained (based on the AQOL-4D values). Resource use associated with both the intervention and control groups will be collected using a customized questionnaire. Online- and community-based recruitment strategies will be implemented, and the effectiveness of each approach will be analyzed. Participants will be recruited from the general Australian population and randomized online. The trial will last for 4 weeks. RESULTS: Small but clinically significant increases in well-being symptoms are expected to be detected in the intervention group compared with the control group. CONCLUSIONS: If this intervention proves to be effective, it will have an impact on the future design and implementation of online-based well-being interventions as a valid and cost-effective way to support mental health clinical treatment. Findings regarding recruitment effectiveness will also contribute to developing better ways to engage this population in research. CLINICALTRIAL: This study is registered in the Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12614000710628.

Genome evolution: How sister genes grow apart.

As genomes evolve, proteins with novel functions arise primarily from gene duplication and divergence events. A new study identifies several molecular mechanisms by which related transcription factors diverge over time to control new sets of target genes and novel cellular functions.