Tag: nevin manimala

Annu Rev Vis Sci. 2022 Jun 21. doi: 10.1146/annurev-vision-100720-103343. Online ahead of print.

ABSTRACT

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID:35727961 | DOI:10.1146/annurev-vision-100720-103343

Brain. 2022 Jun 21:awac221. doi: 10.1093/brain/awac221. Online ahead of print.

ABSTRACT

The underlying pathophysiology of paediatric mild traumatic brain injury and the time-course for biological recovery remains widely debated, with clinical care principally informed by subjective self-report. Similarly, clinical evidence indicate that adolescence is a risk factor for prolonged recovery, but the impact of age-at-injury on biomarkers has not been determined in large, homogeneous samples. The current study collected diffusion magnetic resonance imaging data in consecutively recruited patients (N = 203; 8-18 years old) and age and sex-matched healthy controls (N = 170) in a prospective cohort design. Patients were evaluated sub-acutely (1-11 days post-injury) as well as at four months post-injury (early-chronic phase). Healthy participants were evaluated at similar times to control for neurodevelopment and practice effects. Clinical findings indicated persistent symptoms at four months for a significant minority of patients (22%), along with residual executive dysfunction and verbal memory deficits. Results indicated increased fractional anisotropy and reduced mean diffusivity for patients, with abnormalities persisting up to four months post-injury. Multicompartmental geometric models indicated that estimates of intracellular volume fractions were increased in patients, whereas estimates of free water fractions were decreased. Critically, unique areas of white matter pathology (increased free water fractions or increased neurite dispersion) were observed when standard assumptions regarding parallel diffusivity were altered in multicompartmental models to be more biologically plausible. Cross-validation analyses indicated that some diffusion findings were more reproducible when approximately 70% of the total sample (142 patients, 119 controls) were used in analyses, highlighting the need for large-sample sizes to detect abnormalities. Supervised machine learning approaches (random forests) indicated that diffusion abnormalities increased overall diagnostic accuracy (patients vs. controls) by approximately 10% after controlling for current clinical gold standards, with each diffusion metric accounting for only a few unique percentage points. In summary, current results suggest that novel multicompartmental models are more sensitive to paediatric mild traumatic brain injury pathology, and that this sensitivity is increased when using parameters that more accurately reflect diffusion in healthy tissue. Results also suggest that diffusion data may be insufficient to achieve a high degree of objective diagnostic accuracy in patients when used in isolation, which is to be expected given known heterogeneities in pathophysiology, mechanism of injury, and even criteria for diagnoses. Finally, current results suggest ongoing clinical and physiological recovery at four months post-injury.

PMID:35727944 | DOI:10.1093/brain/awac221

Pediatr Emerg Care. 2022 Jun 22. doi: 10.1097/PEC.0000000000002789. Online ahead of print.

ABSTRACT

OBJECTIVE: The Transport Risk Assessment in Pediatrics (TRAP) and Transport Pediatric Early Warning Scores (T-PEWS) are transport-specific pediatric illness severity scores that are adjunct assessment tools for determining disposition of transported patients. We hypothesized that these scores would predict the risk of clinical deterioration in transported patients admitted to general pediatric wards.

METHODS: Activation of a rapid response team (RRT) in the first 24 hours of admission was used as a marker of deterioration. All pediatric transports between March 2017 and February 2020 admitted via critical care transport were included. Transports to the emergency department (ED) were excluded. This retrospective chart review evaluated TRAP and T-PEWS scores at 3 points: (1) arrival of transport team at referring hospital, (2) admission to the children’s hospital, and (3) RRT activation, if occurring within 24 hours of admission.

RESULTS: There were 1137 team transports during this period. Three hundred ninety-nine patients transported to the ED were excluded, leaving 738 included patients; 405 (55%) admitted to the general wards and 333 (45%) admitted to the pediatric intensive care unit. Twenty-five patients admitted to the wards (6%) had an RRT activation within 24 hours of admission. Statistical analysis used 2-sample t tests. There was a statistically significant difference in scores for ward admissions between those who had RRT activation and those who did not.

CONCLUSIONS: Both TRAP and T-PEWS can be used to predict the risk of clinical deterioration in transported patients admitted to general wards. These scores may assist in assessing which patients admitted to the wards need closer observation.

PMID:35727913 | DOI:10.1097/PEC.0000000000002789

BMJ. 2021 Jun 14;373:n1435. doi: 10.1136/bmj.n1435.

ABSTRACT

OBJECTIVE: To quantify the background incidence rates of 15 prespecified adverse events of special interest (AESIs) associated with covid-19 vaccines.

DESIGN: Multinational network cohort study.

SETTING: Electronic health records and health claims data from eight countries: Australia, France, Germany, Japan, the Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model.

PARTICIPANTS: 126 661 070 people observed for at least 365 days before 1 January 2017, 2018, or 2019 from 13 databases.

MAIN OUTCOME MEASURES: Events of interests were 15 prespecified AESIs (non-haemorrhagic and haemorrhagic stroke, acute myocardial infarction, deep vein thrombosis, pulmonary embolism, anaphylaxis, Bell’s palsy, myocarditis or pericarditis, narcolepsy, appendicitis, immune thrombocytopenia, disseminated intravascular coagulation, encephalomyelitis (including acute disseminated encephalomyelitis), Guillain-Barré syndrome, and transverse myelitis). Incidence rates of AESIs were stratified by age, sex, and database. Rates were pooled across databases using random effects meta-analyses and classified according to the frequency categories of the Council for International Organizations of Medical Sciences.

RESULTS: Background rates varied greatly between databases. Deep vein thrombosis ranged from 387 (95% confidence interval 370 to 404) per 100 000 person years in UK CPRD GOLD data to 1443 (1416 to 1470) per 100 000 person years in US IBM MarketScan Multi-State Medicaid data among women aged 65 to 74 years. Some AESIs increased with age. For example, myocardial infarction rates in men increased from 28 (27 to 29) per 100 000 person years among those aged 18-34 years to 1400 (1374 to 1427) per 100 000 person years in those older than 85 years in US Optum electronic health record data. Other AESIs were more common in young people. For example, rates of anaphylaxis among boys and men were 78 (75 to 80) per 100 000 person years in those aged 6-17 years and 8 (6 to 10) per 100 000 person years in those older than 85 years in Optum electronic health record data. Meta-analytic estimates of AESI rates were classified according to age and sex.

CONCLUSION: This study found large variations in the observed rates of AESIs by age group and sex, showing the need for stratification or standardisation before using background rates for safety surveillance. Considerable population level heterogeneity in AESI rates was found between databases.

PMID:35727911 | DOI:10.1136/bmj.n1435

Nephrology (Carlton). 2022 Jun 21. doi: 10.1111/nep.14080. Online ahead of print.

ABSTRACT

AIM: To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians.

METHODS: Concentration-time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each individual were derived from concentration-time profiles combined with population pharmacokinetic priors, with subsequent assessment for between-group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit.

RESULTS: No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between-subject variability in tacrolimus CL/F (SD=5.00 compared with 3.25 L/h/70kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance (p=0.07 and p=0.08).

CONCLUSION: There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between-subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.

PMID:35727904 | DOI:10.1111/nep.14080

PLoS Comput Biol. 2022 Jun 21;18(6):e1010163. doi: 10.1371/journal.pcbi.1010163. Online ahead of print.

ABSTRACT

Single-cell multi-omics assays offer unprecedented opportunities to explore epigenetic regulation at cellular level. However, high levels of technical noise and data sparsity frequently lead to a lack of statistical power in correlative analyses, identifying very few, if any, significant associations between different molecular layers. Here we propose SCRaPL, a novel computational tool that increases power by carefully modelling noise in the experimental systems. We show on real and simulated multi-omics single-cell data sets that SCRaPL achieves higher sensitivity and better robustness in identifying correlations, while maintaining a similar level of false positives as standard analyses based on Pearson and Spearman correlation.

PMID:35727848 | DOI:10.1371/journal.pcbi.1010163

JMIR Res Protoc. 2022 Jun 21;11(6):e38407. doi: 10.2196/38407.

ABSTRACT

BACKGROUND: The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA’s HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA’s career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands.

OBJECTIVE: The primary aim of this study is to analyze HIM job postings on a US national job recruiting website to determine whether the job postings align with recognized HIM domains, while the secondary aim is to evaluate the AHIMA career map to determine whether it aligns with the job postings.

METHODS: A national job recruitment website was mined electronically (web scraping) using the search term “health information management.” This cross-sectional inquiry evaluated job advertisements during a 2-week period in 2021. After the exclusion criteria, 691 job postings were analyzed. Data were evaluated with descriptive statistics and natural language processing (NLP). Soft cosine measures (SCM) were used to determine correlations between job postings and the AHIMA career map, curricular competencies, and curricular considerations. ANOVA was used to determine statistical significance.

RESULTS: Of all the job postings, 29% (140/691) were in the Southeast, followed by the Midwest (140/691, 20%), West (131/691,19%), Northeast (94/691, 14%), and Southwest (73/691, 11%). The educational levels requested were evenly distributed between high school diploma (219/691, 31.7%), associate degree (269/691, 38.6%), or bachelor’s degree (225/691, 32.5%). A master’s degree was requested in only 8% (52/691) of the postings, with 72% (42/58) preferring one and 28% (16/58) requiring one. A Registered Health Information Technologist (RHIT) credential was the most commonly requested (207/691, 29.9%) in job postings, followed by Registered Health Information Administrator (RHIA; 180/691, 26%) credential. SCM scores were significantly higher in the informatics category compared to the coding and revenue cycle (P=.006) and data analytics categories (P<.001) but not significantly different from the information governance category (P=.85). The coding and revenue cycle category had a significantly higher SCM score compared to the data analytics category (P<.001). Additionally, the information governance category was significantly higher than the data analytics category (P<.001). SCM scores were significantly different between each competency category, except there were no differences in the average SCM score between the information protection and revenue cycle management categories (P=.96) and the information protection and data structure, content, and information governance categories (P=.31).

CONCLUSIONS: Industry job postings primarily sought a high school diploma and associate degrees, with a master’s degree a distant third. NLP analysis of job postings suggested that the correlation between the informatics category and job postings was higher than that of the coding, revenue cycle, and data analytics categories.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38407.

PMID:35727624 | DOI:10.2196/38407

JMIR Hum Factors. 2022 Jun 21;9(2):e35421. doi: 10.2196/35421.

ABSTRACT

The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI’s performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI’s ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians’ intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.

PMID:35727615 | DOI:10.2196/35421

JMIR Aging. 2022 Jun 21;5(2):e32790. doi: 10.2196/32790.

ABSTRACT

BACKGROUND: The Caregiver Advise Record Enable (CARE) Act is a state level law that requires hospitals to identify and educate caregivers (“family members or friends”) upon discharge.

OBJECTIVE: This study examined the association between the implementation of the CARE Act in a Pennsylvania health system and health service utilization (ie, reducing hospital readmission, emergency department [ED] visits, and mortality) for older adults with diabetes.

METHODS: The key elements of the CARE Act were implemented and applied to the patients discharged to home. The data between May and October 2017 were pulled from inpatient electronic health records. Likelihood-ratio chi-square tests and multivariate logistic regression models were used for statistical analysis.

RESULTS: The sample consisted of 2591 older inpatients with diabetes with a mean age of 74.6 (SD 7.1) years. Of the 2591 patients, 46.1% (n=1194) were female, 86.9% (n=2251) were White, 97.4% (n=2523) had type 2 diabetes, and 69.5% (n=1801) identified a caregiver. Of the 1801 caregivers identified, 399 (22.2%) received discharge education and training. We compared the differences in health service utilization between pre- and postimplementation of the CARE Act; however, no significance was found. No significant differences were detected from the bivariate analyses in any outcomes between individuals who identified a caregiver and those who declined to identify a caregiver. After adjusting for risk factors (multivariate analysis), those who identified a caregiver (12.2%, 219/1801) was associated with higher rates of 30-day hospital readmission than those who declined to identify a caregiver (9.9%, 78/790; odds ratio [OR] 1.38, 95% CI 1.04-1.87; P=.02). Significantly lower rates were detected in 7-day readmission (P=.02), as well as 7-day (P=.03) and 30-day (P=.01) ED visits, among patients with diabetes whose identified caregiver received education and training than those whose identified caregiver did not receive education and training in the bivariate analyses. However, after adjusting for risk factors, no significance was found in 7-day readmission (OR 0.53, 95% CI 0.27-1.05; P=.07), 7-day ED visit (OR 0.63, 95% CI 0.38-1.03; P=.07), and 30-day ED visit (OR 0.73, 95% CI 0.52-1.02; P=.07). No significant associations were found for other outcomes (ie, 30-day readmission and 7-day and 30-day mortality) in both the bivariate and multivariate analyses.

CONCLUSIONS: Our study found that the implementation of the CARE Act was associated with certain health service utilization. The identification of caregivers was associated with higher rates of 30-day hospital readmission in the multivariate analysis, whereas having identified caregivers who received discharge education was associated with lower rates of readmission and ED visit in the bivariate analysis.

PMID:35727611 | DOI:10.2196/32790

JAMA Netw Open. 2022 Jun 1;5(6):e2217653. doi: 10.1001/jamanetworkopen.2022.17653.

NO ABSTRACT

PMID:35727584 | DOI:10.1001/jamanetworkopen.2022.17653