Reducing Grip Uncertainty During Initial Prosthetic Hand Use Improves Eye-Hand Coordination and Lowers Mental Workload

The reliance on vision to control a myoelectric prosthesis is cognitively burdensome and contributes to device abandonment. The feeling of uncertainty when gripping an object is thought to be the cause of this overreliance on vision in hand-related actions. We explored if experimentally reducing grip uncertainty alters the visuomotor control and mental workload experienced during initial prosthesis use. In a repeated measures design, twenty-one able-bodied participants took part in a pouring task across three conditions: (a) using their anatomical hand, (b) using a myoelectric prosthetic hand simulator, and (c) using a myoelectric prosthetic hand simulator with Velcro attached to reduce grip uncertainty. Performance, gaze behaviour (using mobile eye-tracking) and self-reported mental workload, was measured. Results showed that using a prosthesis (with or without Velcro) slowed task performance, impaired typical eye-hand coordination and increased mental workload compared to anatomic hand control. However, when using the prosthesis with Velcro, participants displayed better prosthesis control, more effective eye-hand coordination and reduced mental workload compared to when using the prosthesis without Velcro. These positive results indicate that reducing grip uncertainty could be a useful tool for encouraging more effective prosthesis control strategies in the early stages of prosthetic hand learning.

Discrete Generalized Inverted Exponential Distribution: Case Study Color Image Segmentation

We present in this paper a discrete analogue of the continuous generalized inverted exponential distribution denoted by discrete generalized inverted exponential (DGIE) distribution. Since, it is cumbersome or difficult to measure a large number of observations in reality on a continuous scale in the area of reliability analysis. Yet, there are a number of discrete distributions in the literature; however, these distributions have certain difficulties in properly fitting a large amount of data in a variety of fields. The presented $\mathbf{DGIE}\left(\beta ,\theta \right)$ has shown the efficiency in fitting data better than some existing distribution. In this study, some basic distributional properties, moments, probability function, reliability indices, characteristic function, and the order statistics of the new DGIE are discussed. Estimation of the parameters is illustrated using the moment's method as well as the maximum likelihood method. Simulations are used to show the performance of the estimated parameters. The model with two real data sets is also examined. In addition, the developed DGIE is applied as color image segmentation which aims to cluster the pixels into their groups. To evaluate the performance of DGIE, a set of six color images is used, as well as it is compared with other image segmentation methods including Gaussian mixture model, K-means, and Fuzzy subspace clustering. The DGIE provides higher performance than other competitive methods.

Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.

The Association Between Socioeconomic Status, Sex, Race / Ethnicity and In-Hospital Mortality Among Patients Hospitalized for Heart Failure

BACKGROUND

The association between socioeconomic status (SES), sex, race / ethnicity and outcomes during hospitalization for heart failure (HF) has not previously been investigated.

METHODS AND RESULTS

We analyzed HF hospitalizations in the United States National Inpatient Sample between 2015 and 2017. Using a hierarchical, multivariable Poisson regression model to adjust for hospital- and patient-level factors, we assessed the association between SES, sex, and race / ethnicity and all-cause in-hospital mortality. We estimated the direct costs (USD) across SES groups. Among 4,287,478 HF hospitalizations, 40.8% were in high SES, 48.7% in female, and 70.0% in White patients. Relative to these comparators, low SES (homelessness or lowest quartile of median neighborhood income) (relative risk [RR] 1.02, 95% confidence interval [CI] 1.00-1.05) and male sex (RR 1.09, 95% CI 1.07-1.11) were associated with increased risk, whereas Black (RR 0.79, 95% CI 0.76-0.81) and Hispanic (RR 0.90, 95% CI 0.86-0.93) race / ethnicity were associated with a decreased risk of in-hospital mortality (5.1% of all hospitalizations). There were significant interactions between race / ethnicity and both, SES (P < .01) and sex (P = .04), such that racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients. The median direct cost of admission was lower in low vs high SES groups ($9324.60 vs $10,940.40), female vs male patients ($9866.60 vs $10,217.10), and Black vs White patients ($9077.20 vs $10,019.80). The median costs increased with SES in all demographic groups primarily related to greater procedural utilization.

CONCLUSIONS

SES, sex, and race / ethnicity were independently associated with in-hospital mortality during HF hospitalization, highlighting possible care disparities. Racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients.

P681Acute Myocardial Infarction in patients with Leukaemia: A national analysis of prevalence, predictors and outcomes in United States hospitalisations (2004 to 2014)

Background

Patients with leukaemia are at increased risk of cardiovascular events. There is limited outcomes data for patients with a history of leukaemia who present with an acute myocardial infarction (AMI).

Purpose

To examine the prevalence and clinical characteristics of patients with leukaemia presenting with AMI, and evaluate differences in clinical outcomes according to the subtype of leukaemia in comparison to patients without leukaemia.

Methods

We analysed the Nationwide Inpatient Sample (2004–2014) for patients with a primary discharge diagnosis of AMI and concomitant leukaemia, and further stratified according to the subtype of leukaemia into 4 groups; AML; ALL; CML; and CLL. Multiple logistic regression was conducted to identify the association between leukaemia and major acute cardiovascular and cerebrovascular events (MACCE; composite of mortality, stroke and cardiac complications) and bleeding.

Results

Out of 6,750,927 AMI admissions, a total of 21,694 patients had a leukaemia diagnosis. The leukaemia group experienced higher rates of MACCE (11.8% vs. 7.8%), mortality (10.3% vs. 5.8%) and bleeding (5.6% vs. 5.3%). Following adjustments, leukaemia was independently associated with increased odds of MACCE (OR 1.26 [1.20,1.31]) and mortality (OR 1.43 [1.37,1.50]) without an increased risk of bleeding (OR 0.86 [0.81,0.92]). Acute myeloid leukaemia (AML) was associated with approximately three-fold risk of MACCE (RR 2.81 [2.51, 3.13]) and a four-fold risk of mortality (RR 3.75 [3.34, 4.22]) (Figure 1). Patients with leukaemia were less likely to undergo coronary angiography (CA) (48.5% vs. 64.5%) and percutaneous coronary intervention (PCI) (28.2% vs. 42.9%) compared to those without leukaemia.

Figure 1.Relative risk of adverse events

Conclusion

Patients with leukaemia, especially those with AML, are associated with poor clinical outcomes after AMI, and are less likely to receive CA and PCI compared to those without leukaemia. A multi-disciplinary approach between cardiologists and haematology oncologists may improve the outcomes of patients with leukaemia after AMI.

P3611The effect of concomitant AF on in-hospital clinical outcomes of NSTE-ACS related hospitalizations in the United States: an analysis of rates, trends and predictors from 2004 to 2014

Background

Atrial fibrillation (AF) is the most common arrhythmia in patients presenting with acute coronary syndrome (ACS).

Purpose

We sought to examine the rates, trends, and clinical outcomes of non-ST Elevation acute coronary syndrome (NSTE-ACS) related hospitalisations in the United States in patients with AF compared to those with sinus rhythm (SR).

Methods

We analysed the Nationwide Inpatient Sample (NIS) database from 2004 to 2014 for patients with a primary discharge diagnosis of NSTEMI or UA, and further stratified the cohort on the basis of diagnoses into SR and AF groups. Multivariate analysis was performed to identify the association between AF and MACCE (composite of mortality, stroke and cardiac complications), mortality, stroke, and bleeding.

Results

A total of 4,668,737 NSTE-ACS admissions were included in our analysis. The proportions of SR and AF groups were 82.4% (3,848,202) and 17.6% (820,535), respectively. The incidence of AF increased significantly over time from 16.5% in 2004 to 19.3% in 2014 (p<0.001). The AF group was at a greater risk of adverse outcomes with higher overall rates and adjusted relative risk of MACCE (12.9% vs. 5.3%; RR: 1.74 [1.72, 1.75]), mortality (6.5% vs. 3.3%. RR: 1.12 [1.11, 1.13]), stroke (2.7% vs. 1.5%; RR: 1.32 [1.30, 1.34]) and bleeding (14.7% vs. 8.8%; RR: 1.42 [1.41, 1.43]). The AF group was less likely to receive coronary angiography (47.1% vs. 58%) and PCI (18.7% vs. 32.6%) and more likely to undergo CABG (13.9% vs. 7.6%) in comparison to SR.

Figure 1. Crude rates of adverse events

Conclusion

The prevalence of concomitant Atrial Fibrillation amongst patients presenting with NSTE-ACS has increased over a decade. However, this high-risk group remains less likely to receive invasive coronary management for NSTE-ACS than patients with sinus rhythm, independent of their comorbidities, and are associated with worse clinical outcomes.