Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities.

BACKGROUND: Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based methods, suffer from drawbacks such as long training times, high model complexity, and poor scalability. METHOD: This paper proposes IMS2Trans, a novel lightweight scalable Swin Transformer network by utilizing a single encoder to extract latent feature maps from all available modalities. This unified feature extraction process enables efficient information sharing and fusion among the modalities, resulting in efficiency without compromising segmentation performance even in the presence of missing modalities. RESULTS: Two datasets, BraTS 2018 and BraTS 2020, containing incomplete modalities for brain tumor segmentation are evaluated against popular benchmarks. On the BraTS 2018 dataset, our model achieved higher average Dice similarity coefficient (DSC) scores for the whole tumor, tumor core, and enhancing tumor regions (86.57, 75.67, and 58.28, respectively), in comparison with a state-of-the-art model, i.e. mmFormer (86.45, 75.51, and 57.79, respectively). Similarly, on the BraTS 2020 dataset, our model scored higher DSC scores in these three brain tumor regions (87.33, 79.09, and 62.11, respectively) compared to mmFormer (86.17, 78.34, and 60.36, respectively). We also conducted a Wilcoxon test on the experimental results, and the generated p-value confirmed that our model's performance was statistically significant. Moreover, our model exhibits significantly reduced complexity with only 4.47 M parameters, 121.89G FLOPs, and a model size of 77.13 MB, whereas mmFormer comprises 34.96 M parameters, 265.79 G FLOPs, and a model size of 559.74 MB. These indicate our model, being light-weighted with significantly reduced parameters, is still able to achieve better performance than a state-of-the-art model. CONCLUSION: By leveraging a single encoder for processing the available modalities, IMS2Trans offers notable scalability advantages over methods that rely on multiple encoders. This streamlined approach eliminates the need for maintaining separate encoders for each modality, resulting in a lightweight and scalable network architecture. The source code of IMS2Trans and the associated weights are both publicly available at https://github.com/hudscomdz/IMS2Trans.

Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics.

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Does Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19.

Fake news is being generated in different languages, yet existing studies are dominated by English news. The analysis of fake news content has focused on lexical and stylometric features, giving little attention to semantic features. A few studies involving semantic features have either used them as the inputs to classifiers with no interpretations, or treated them in isolation. This research aims to investigate both thematic and emotional characteristics of fake news at different levels and compare them between different languages for the first time. It extends a state-of-the-art topic modeling technique to extract news topics and introduces a divergence measure to assess the importance of thematic characteristics for identifying fake news. We further examine associations of the thematic and emotional characteristics of fake news. The empirical findings have implications for developing both general and language-specific countermeasures for fake news.

Patient Trust in Physicians Matters-Understanding the Role of a Mobile Patient Education System and Patient-Physician Communication in Improving Patient Adherence Behavior: Field Study.

BACKGROUND: The ultimate goal of any prescribed medical therapy is to achieve desired outcomes of patient care. However, patient nonadherence has long been a major problem detrimental to patient health and, thus, is a concern for all health care providers. Moreover, nonadherence is extremely costly for global medical systems because of unnecessary complications and expenses. Traditional patient education programs often serve as an intervention tool to increase patients' self-care awareness, disease knowledge, and motivation to change patient behaviors for better adherence. Patient trust in physicians, patient-physician relationships, and quality of communication have also been identified as critical factors influencing patient adherence. However, little is known about how mobile patient education technologies help foster patient adherence. OBJECTIVE: This study aimed to empirically investigate whether and how a mobile patient education system (MPES) juxtaposed with patient trust can increase patient adherence to prescribed medical therapies. METHODS: This study was conducted based on a field survey of 125 patients in multiple states in the United States who have used an innovative mobile health care system for their health care education and information seeking. Partial least squares techniques were used to analyze the collected data. RESULTS: The results revealed that patient-physician communication and the use of an MPES significantly increase patients' trust in their physicians. Furthermore, patient trust has a prominent effect on patient attitude toward treatment adherence, which in turn influences patients' behavioral intention and actual adherence behavior. Based on the theory of planned behavior, the results also indicated that behavioral intention, response efficacy, and self-efficacy positively influenced patients' actual treatment adherence behavior, whereas descriptive norms and subjective norms do not play a role in this process. CONCLUSIONS: Our study is one of the first that examines the relationship between patients who actively use an MPES and their trust in their physicians. This study contributes to this context by enriching the trust literature, addressing the call to identify key patient-centered technology determinants of trust, advancing the understanding of patient adherence mechanisms, adding a new explanation of the influence of education mechanisms delivered via mobile devices on patient adherence, and confirming that the theory of planned behavior holds in this patient adherence context.

Use of Mobile Apps for Visual Acuity Assessment: Systematic Review and Meta-analysis.

BACKGROUND: Vision impairments (VIs) and blindness are major global public health issues. A visual acuity (VA) test is one of the most crucial standard psychophysical tests of visual function and has been widely used in a broad range of health care domains, especially in many clinical settings. In recent years, there has been increasing research on mobile app-based VA assessment designed to allow people to test their VA at any time and any location. OBJECTIVE: The goal of the review was to assess the accuracy and reliability of using mobile VA measurement apps. METHODS: We searched PubMed, Embase, Cochrane Library, and Google Scholar for relevant articles on mobile apps for VA assessment published between January 1, 2008, and July 1, 2020. Two researchers independently inspected and selected relevant studies. Eventually, we included 22 studies that assessed tablet or smartphone apps for VA measurement. We then analyzed sensitivity, specificity, and accuracy in the 6 papers we found through a meta-analysis. RESULTS: Most of the 22 selected studies can be considered of high quality based on the Quality Assessment of Diagnostic Accuracy Studies-2. In a meta-analysis of 6 studies involving 24,284 participants, we categorized the studies based on the age groups of the study participants (ie, aged 3-5 years, aged 6-22 years, and aged 55 years and older), examiner (ie, professional and nonprofessional examiners), and the type of mobile devices (ie, smartphone, iPad). In the group aged 3 to 5 years, the pooled sensitivity for VA app tests versus clinical VA tests was 0.87 (95% CI 0.79-0.93; P=.39), and the pooled specificity was 0.78 (95% CI 0.70-0.85; P=.37). In the group aged 6 to 22 years, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86 (95% CI 0.84-0.87; P<.001), and the pooled specificity for VA app tests versus clinical VA tests was 0.91 (95% CI 0.90-0.91; P=.27). In the group aged 55 years and older, the pooled sensitivity for VA app tests versus clinical VA tests was 0.85 (95% CI 0.55-0.98), and the pooled specificity for VA app tests versus clinical VA tests was 0.98 (95% CI 0.95-0.99). We found that the nonprofessional examiner group (AUC 0.93) had higher accuracy than the professional examiner group (AUC 0.87). In the iPad-based group, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86, and the pooled specificity was 0.79. In the smartphone-based group, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86 (P<.001), and the pooled specificity for VA app tests versus clinical VA tests was 0.91 (P<.001). CONCLUSIONS: In this study, we conducted a comprehensive review of the research on existing mobile apps for VA tests to investigate their diagnostic value and limitations. Evidence gained from this study suggests that mobile app-based VA tests can be useful for on-demand VI detection.

Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study.

BACKGROUND: Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients' mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients' adoption and use of MMHS; yet, there has been limited understanding of it. OBJECTIVE: To address the significant literature gap, this research aims to investigate both the antecedents of patients' privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS. METHODS: Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression. RESULTS: The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS. CONCLUSIONS: This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS.

Patients' compliance behavior in a personalized mobile patient education system (PMPES) setting: Rational, social, or personal choices?

OBJECTIVE: With the advancement of mobile technologies, patients can access medical and patient educational information anytime and anywhere. Computer-aided patient education has been advocated as a key means of interventions for improving patient knowledge and compliance (i.e., adherence). However, evidence of the efficacy of computer-aided patient education remains relatively limited. For example, little is known about how the latest mobile technologies influence patients' compliance intention and their actual compliance behavior. The objective of this study is to investigate patients' compliance intention and behavior using a personalized mobile patient education system (PMPES) as a novel technological intervention for patients based on rational choice theory (RCT) and the theory of planned behavior (TPB). MATERIALS AND METHODS: We conducted a field survey with 125 actual patients in U.S. who obtained their patient education through PMPES while seeking medical treatment advice from their doctors. We used partial least squares (PLS) regression path modeling to test our model. RESULTS: We found that, based on RCT, the benefits of compliance and cost/threat of noncompliance positively influenced intention toward treatment compliance; in contrast, costs of compliance negatively influenced intention toward treatment compliance. However, the benefits of noncompliance had no effect on intention toward treatment compliance. The results also indicated that intention toward treatment compliance, response efficacy, and self-efficacy related to TPB jointly influenced the degree of actual compliance behaviors. Social influence factors including subjective norms and descriptive norms had no influence on patients' actual treatment compliance behavior. CONCLUSION: Overall, the research model explains 69.2 % of the variance in patients' actual compliance behavior. We find our model robust in using RCT as a key theoretical lens for the assessment of patients' compliance intention to follow medical recommendations enabled by the PMPES and delivered to mobile devices. The factors associated with RCT and TPB jointly influence patients' actual compliance behavior. Future mobile patient education programs should consider patients' age groups, mixed-gender groups, different medical settings, and cross-cultural contexts.

What makes clinical documents helpful and engaging? An empirical investigation of experience sharing in an online medical community.

BACKGROUND: Social media have emerged as a platform for experience and knowledge sharing in the medical community. The online medical community is garnering increasing research attention; however, there is a lack of understanding of what factors influence the helpfulness and engagement of experience sharing in the community. METHODS: Clinical documents manifest physicians' experience and knowledge. This study fills the knowledge gap by investigating what elements of clinical documents contribute to the helpfulness of sharing clinical documents online and what influence member engagement. Clinical documents follow certain architecture to specify their structure and semantics for exchange (e.g., HL7 C-CDA). Accordingly, the structural elements of clinical documents may influence document helpfulness for the online community. Member engagement is one of the indicators of community success. We collected 6514 clinical documents from a real-world online medical community, and normalized them with the structural elements of HL7 C-CDA. We performed regression analyses to identify the structural elements that have significant impacts on document helpfulness and member engagement. RESULTS: The results show that some structural elements of clinical documents such as assessment, chief complaints, medications, physical exams, procedures, results, and vital signs sections have positive effects whereas assessment and plan, general status, history and past illness of patients, instructions, problem and review of systems have negative effects on the helpfulness of clinical documents. The results also reveal that structural elements such as family history, history of past illness, medication, physical exam, review of systems, and vital signs positively; whereas assessment, assessment and plan, instruction, and result negatively; influence member engagement. CONCLUSIONS: The findings provide guide on how to improve the effectiveness of sharing clinical experience online. The new and in-depth insights may contribute to the success of online medical communities and the quality of medical decisions.

A Stroke Risk Detection: Improving Hybrid Feature Selection Method.

BACKGROUND: Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. OBJECTIVE: This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. METHODS: WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. RESULTS: This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. CONCLUSIONS: This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

HARNESSING SOCIAL MEDIA FOR HEALTH INFORMATION MANAGEMENT.

The remarkable upsurge of social media has dramatic impacts on health care research and practice in the past decade. Social media are reshaping health information management in a variety of ways, ranging from providing cost-effective ways to improve clinician-patient communication and exchange health-related information and experience, to enabling the discovery of new medical knowledge and information. Despite some demonstrated initial success, social media use and analytics for improving health as a research field is still at its infancy. Information systems researchers can potentially play a key role in advancing the field. This study proposes a conceptual framework for social media-based health information management by drawing on multi-disciplinary research. With the guidance of the framework, this research presents related research challenges, identifies important yet under-explored research issues, and discusses promising directions for future research.

A Study on the Model of Detecting the Liquid Level of Sealed Containers Based on Kirchhoff Approximation Theory.

By simulating the sound field of a round piston transducer with the Kirchhoff integral theorem and analyzing the shape of ultrasound beams and propagation characteristics in a metal container wall, this study presents a model for calculating the echo sound pressure by using the Kirchhoff paraxial approximation theory, based on which and according to different ultrasonic impedance between gas and liquid media, a method for detecting the liquid level from outside of sealed containers is proposed. Then, the proposed method is evaluated through two groups of experiments. In the first group, three kinds of liquid media with different ultrasonic impedance are used as detected objects; the echo sound pressure is calculated by using the proposed model under conditions of four sets of different wall thicknesses. The changing characteristics of the echo sound pressure in the entire detection process are analyzed, and the effects of different ultrasonic impedance of liquids on the echo sound pressure are compared. In the second group, taking water as an example, two transducers with different radii are selected to measure the liquid level under four sets of wall thickness. Combining with sound field characteristics, the influence of different size transducers on the pressure calculation and detection resolution are discussed and analyzed. Finally, the experimental results indicate that measurement uncertainly is better than ±5 mm, which meets the industrial inspection requirements.

A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management.

The use of mobile and wireless technologies and wearable devices for improving health care processes and outcomes (mHealth) is promising for health promotion among patients with chronic diseases such as obesity and diabetes. This study comprehensively examined published mHealth intervention studies for obesity and diabetes treatment and management to assess their effectiveness and provide recommendations for future research. We systematically searched PubMed for mHealth-related studies on diabetes and obesity treatment and management published during 2000-2016. Relevant information was extracted and analyzed. Twenty-four studies met inclusion criteria and varied in terms of sample size, ethnicity, gender, and age of the participating patients and length of follow-up. The mHealth interventions were categorized into 3 types: mobile phone text messaging, wearable or portable monitoring devices, and applications running on smartphones. Primary outcomes included weight loss (an average loss ranging from -1.97 kg in 16 wk to -7.1 kg in 5 wk) or maintenance and blood glucose reduction (an average decrease of glycated hemoglobin ranging from -0.4% in 10 mo to -1.9% in 12 mo); main secondary outcomes included behavior changes and patient perceptions such as self-efficacy and acceptability of the intervention programs. More than 50% of studies reported positive effects of interventions based on primary outcomes. The duration or length of intervention ranged from 1 wk to 24 mo. However, most studies included small samples and short intervention periods and did not use rigorous data collection or analytic approaches. Although some studies suggest that mHealth interventions are effective and promising, most are pilot studies or have limitations in their study designs. There is an essential need for future studies that use larger study samples, longer intervention (≥ 6 mo) and follow-up periods (≥ 6 mo), and integrative and personalized innovative mobile technologies to provide comprehensive and sustainable support for patients and health service providers.

The public's opinions on a new school meals policy for childhood obesity prevention in the U.S.: A social media analytics approach.

OBJECTIVES: This study investigates the public's opinions on a new school meals policy for childhood obesity prevention, discovers aspects concerning those opinions, and identifies possible gender and regional differences in the U.S. METHODS: We collected 14,317 relevant tweets from 11,715 users since the national policy enactment on Feb 9, 2010 through Dec 31, 2015. We applied opinion mining techniques to classify tweets into positive, negative, and neutral categories, and conducted content analysis to gain insights into aspects of opinions in terms of target, holder, source, and function. RESULTS: There were more negative tweets about the school meals policy than positive ones (16.8% vs. 12.9%), in addition to neutral tweets (70.3%). The main targets for negative opinions were campaign and food, and those for positive opinions were policy and health benefits. The opinion holders represent a wide range of policy stakeholders. The first-hand source dominated the opinions. Statement accounted for the function of most opinions. Females (62.5%) were more involved than males (37.5%), and people in the South and the West regions (64.2%) engaged themselves more than people in the Northeast and the Midwest (35.8%) of the U.S. CONCLUSIONS: Negative opinions about the school meals policy consistently outnumbered positive ones. The findings discovered the public's opinions for policy improvement, contributed to the evidence base of health benefits for policy promotion and community collaboration, and revealed interesting gender and regional differences in the opinions. The social media analytics offers significant methodological implications for discovering the public opinions on food policies.

Full-Length cDNA Cloning, Molecular Characterization and Differential Expression Analysis of Lysophospholipase I from Ovis aries.

Lysophospholipase I (LYPLA1) is an important protein with multiple functions. In this study, the full-length cDNA of the LYPLA1 gene from Ovis aries (OaLypla1) was cloned using primers and rapid amplification of cDNA ends (RACE) technology. The full-length OaLypla1 was 2457 bp with a 5'-untranslated region (UTR) of 24 bp, a 3'-UTR of 1740 bp with a poly (A) tail, and an open reading frame (ORF) of 693 bp encoding a protein of 230 amino acid residues with a predicted molecular weight of 24,625.78 Da. Phylogenetic analysis showed that the OaLypla1 protein shared a high amino acid identity with LYPLA1 of Bos taurus. The recombinant OaLypla1 protein was expressed and purified, and its phospholipase activity was identified. Monoclonal antibodies (mAb) against OaLypla1 that bound native OaLypla1 were generated. Real-time PCR analysis revealed that OaLypla1 was constitutively expressed in the liver, spleen, lung, kidney, and white blood cells of sheep, with the highest level in the kidney. Additionally, the mRNA levels of OaLypla1 in the buffy coats of sheep challenged with virulent or avirulent Brucella strains were down-regulated compared to untreated sheep. The results suggest that OaLypla1 may have an important physiological role in the host response to bacteria. The function of OaLypla1 in the host response to bacterial infection requires further study in the future.

A prototype evaluation of a computer-assisted physical therapy system for osteoarthritis.

PURPOSE: Osteoarthritis is a common locomotor disorder affecting many older people and has become a growing public health problem. Adherence to exercise is a key to the successful recovery of patients but is often a major problem for older adults with osteoarthritis. The proposed pilot study is aimed to explore the feasibility and acceptance of an interactive computer assisted physical therapy prototype for aging adults with osteoarthritis. METHODOLOGY: The prototype comprised of a laptop, a wireless keypad, and software designed to deliver customized exercise plans for each participant as prescribed by their physical therapists. Each participant used a wireless keypad as a remote control to navigate through the software that provided exercise instructions in the form of text and videos. Fifteen participants were enrolled in the study during their regular visit to an outpatient physical therapy center. They were provided with a 15-minute instruction session first, then they were asked to use the system unassisted. RESULTS: Ninety percent of the patients expressed no difficulty in using the prototype system in this study. All participants fully understood the instructions given by the exercise videos and expressed their intention to use the system unassisted at home. CONCLUSION: The presented computer-assisted physical therapy prototype may play an important role in the management of musculoskeletal conditions in patients that require home based exercise therapy. Further research is warranted to evaluate the impact of the proposed computerized therapy system on patients with various musculoskeletal conditions.