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We aimed to investigate the effect of basic fitness function according to whole-body vibration (WBV) stimulus with slope during deadlift in adults. A total of 15 subjects performed deadlift exercise with a sound-wave vibrator. The subjects consisted of three groups: no slope and WBV group (control), WBV only group (group 1), and slope and WBV group (group 2). Slope was set at $5^{\circ }$, and the frequency and amplitude of WBV were 10Hz and 5mm, respectively. The participants performed Romanian deadlift 2 days a week for 4 weeks, including 10 trials per set and five sets a day. We measured basic fitness function factor including the isokinetic muscle contraction test using biodex system3. All groups showed an increase in strength of approximately $>20$%. Group 2 showed the highest increase. Moreover, maximal peak torque of the lumbar joint showed an increase trend similar to that of back muscle strength. An increase of 15.72%, 24.86%, and 51.44% was noted in the control, group 1, and group 2, respectively. The findings indicate that WBV exercise with slope is the most efficient exercise protocol for improving muscle function of the trunk. WBV with slope could help stimulate trunk muscles more and efficiently, could result in a more positive effect on muscle function compared with WBV only, and could be included in an exercise program for efficient patient rehabilitation.

Healthcare improvement relating to basic hospital service attributes is one of the most fundamental driving forces to uplifting in economic and social transition for developing countries in an emerging context. The purpose of this paper is to measure patients’ trio need satisfaction toward doctor service quality (DSQ), nurse caring quality (NCQ), and hospital environment quality (HEQ) and compare the effects of perceived, expected, and service quality gap on patient satisfaction. Multiple regression analysis was used to explain the patient satisfaction. The result shows that the perceived service quality (67.3%) and the service quality gap (50.8%) can better explain patient satisfaction than the expected service quality (3.0%). Both perception and gap model show that DSQ, NCQ, and HEQ are significant predictors of patient satisfaction, where NCQ is the most important dimension in explaining patient satisfaction. This study focuses on patient need satisfaction research by being one of the few empirical studies relating to the most basic hospital service quality attributes, contributing to the development of hospital service quality in a developing context of Bangladesh.

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions. Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowledge from EHRs.

The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting disease status with raw input data. However, the “black-box” nature of deep learning and the highreliability requirement of biomedical applications have created new challenges regarding the existence of confounding factors. In this paper, with a brief argument that inappropriate handling of confounding factors will lead to models’ sub-optimal performance in real-world applications, we present an efficient method that can remove the inuences of confounding factors such as age or gender to improve the across-cohort prediction accuracy of neural networks. One distinct advantage of our method is that it only requires minimal changes of the baseline model’s architecture so that it can be plugged into most of the existing neural networks. We conduct experiments across CT-scan, MRA, and EEG brain wave with convolutional neural networks and LSTM to verify the efficiency of our method.

Clinical decisions to treat and diagnose patients are affected by implicit biases formed by racism, ableism, sexism, and other stereotypes. These biases reflect broader systemic discrimination in healthcare and risk marginalizing already disadvantaged groups. Existing methods for measuring implicit biases require controlled randomized testing and only capture individual attitudes rather than outcomes. However, the ”big-data” revolution has led to the availability of large observational medical datasets, like EHRs and biobanks, that provide the opportunity to investigate discrepancies in patient health outcomes. In this work, we propose a causal inference approach to detect the effect of clinician implicit biases on patient outcomes in large-scale medical data. Specifically, our method uses proximal mediation to disentangle pathway-specific effects of a patient’s sociodemographic attribute on a clinician’s diagnosis decision. We test our method on real-world data from the UK Biobank. Our work can serve as a tool that initiates conversation and brings awareness to unequal health outcomes caused by implicit biases.