1,841 research outputs found
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Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.
Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions
Permission based Mobile Malware Detection System using Machine Learning Techniques
Mobile technology has grown dramatically around the world. Nowadays smart mobile devices are ubiquitous, i.e. they serve multiple purposes such as personal mobile communication, data storage, multimedia and entertainment etc. They have become important part of life. Implementing secure mobile and wireless networks is crucial for enterprises operating in the Internet-based business environment. Mobile market share has grown significantly in past few years so that we need to think about mobile security. Mobile security can be compromised due to design flaws, vulnerabilities, and protocol failures in any mobile applications, viruses, spyware, malware and other threats. In this paper we will more focus on mobile malware. Many tools are available in the market to detect malware but new research trend in the mobile security is users should be aware of app before he/she installs from the app store. Hence we propose a novel approach for permission based mobile malware detection system. It is based on static analysis. It has 3 major parts in it 1) a signature database for storing analysis results of training and testing. 2) An Android client who is used by end users for making analysis requests, and 3) a central server plays important role as it communicates with both signature database and smartphone client. We can say that he is the manager of whole analysis process. It alerts user if the app is malicious or the benign based on it user can proceed whether to continue with it or not
Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol
The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies
to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques
allow processing of real-time observational information and continuously learning from data to build
understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s
native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk.
Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the
relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the
Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes
(France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will
be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the
ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations.
Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of
security regarding patients’ data. Several important ethical considerations related to EMA method must also be
considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on
daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences
in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a
participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring.
Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks
factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto
de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan
Nacional de Drogas (20151073), American Foundation for Suicide Prevention
(AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740
AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the
European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R)
and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy
in Madrid, Spain, The foundation de l’avenir, and the Fondation de
France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported
by Ministerio de Economía of Spain under projects: OTOSIS
(TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network
(TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with
the European Commission (ERDF) under projects ADVENTURE (TEC2015–
69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad
de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P.
Moreno-Muñoz has been supported by FPI grant BES-2016-07762
The future of laboratory medicine - A 2014 perspective.
Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine
Detecting Repackaged Android Applications Using Perceptual Hashing
The last decade has shown a steady rate of Android device dominance in market share and the emergence of hundreds of thousands of apps available to the public. Because of the ease of reverse engineering Android applications, repackaged malicious apps that clone existing code have become a severe problem in the marketplace. This research proposes a novel repackaged detection system based on perceptual hashes of vetted Android apps and their associated dynamic user interface (UI) behavior. Results show that an average hash approach produces 88% accuracy (indicating low false negative and false positive rates) in a sample set of 4878 Android apps, including 2151 repackaged apps. The approach is the first dynamic method proposed in the research community using image-based hashing techniques with reasonable performance to other known dynamic approaches and the possibility for practical implementation at scale for new applications entering the Android market
Assessing The Efficacy Of A Self-Administered Treatment For Social Anxiety Disorder In The Form Of A Gamified Mobile Application
Social anxiety disorder (SAD) is not only highly prevalent and impairing, but vastly undertreated. Because effective treatments exist for SAD but are not reaching many with the disorder, this study set out to determine whether a purely self-guided intervention could be made more effective through two novel mechanisms: (a) delivery of the treatment on a mobile smartphone; and (b) the gamification of the treatment. Utilizing a single-subject multiple baseline across participants design, the treatment was evaluated on a sample of undergraduate students (N = 10) who endorsed significant social anxiety. Participants completed assessments every four days during both baseline phase and treatment phases. Seven of ten participants completed all measures and were used in the final analysis. At the study’s conclusion, these participants showed a statistically significant mean decrease of 13, 95% CI [2.05, 23.94], t(7) = 2.907, p = .027, d = 1.461 on the BFNES, and a statistically significant mean decrease of 24.58, 95% CI [4.69, 44.46], t(7) = 3.024, p = .023, d = 1.288 on the LSAS-SR. Participants showed no statistically significant changes on the K10 or WHOQOL. These results suggest that this application may be effective as a stand-alone treatment for SAD
Building a Machine-learning Framework to Remotely Assess Parkinson's Disease Using Smartphones.
Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD
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SMOOTH (Self-Management of Open Online Trials in Health) analysis found improvements were needed for reporting methods of internet-based trials
Background
The growth of trials conducted over the internet has increased, but with little practical guidance for their conduct and it is sometimes challenging for researchers to adapt the conventions used in face-to-face trials and maintain the validity of the work.
Aim
To systematically explore existing self-recruited online randomized trials of self-management interventions and analyze the trials to assess their strengths and weaknesses, the quality of reporting and the involvement of lay persons as collaborators in the research process.
Methods
The Online Randomized Controlled Trials of Health Information Database (ORCHID) was used as the sampling frame to identify a subset of self-recruited online trials of self-management interventions. The authors cataloged what these online trials were assessing, appraised study quality, extracted information on how trials were run and assessed the potential for bias. We searched out how public and patient participation was integrated into online trial design and how this was reported. We recorded patterns of use for registration, reporting, settings, informed consent, public involvement, supplementary materials, and dissemination planning.
Results
The sample included 41 online trials published from 2002-2015. The barriers to replicability and risk of bias in online trials included inadequate reporting of blinding in 28/41 (68%) studies; high attrition rates with incomplete or unreported data in 30/41 (73%) of trials; and 26/41 (63%) of studies were at high risk for selection bias as trial registrations were unreported. The methods for (23/41, 56%) trials contained insufficient information to replicate the trial, 19/41 did not report piloting the intervention. Only 2/41 studies were cross-platform compatible. Public involvement was most common for advisory roles (n=9, 22%), and in the design, usability testing and piloting of user materials (n=9, 22%)
Conclusions
This study catalogs the state of online trials of self-management in the early 21st century and provides insights for online trials development as early as the protocol planning stage. Reporting of trials was generally poor and, in addition to recommending that authors report their trials in accordance with CONSORT guidelines, we make recommendations for researchers writing protocols, reporting on and evaluating online trials. The research highlights considerable room for improvement in trial registration, reporting of methods, data management plans, and public and patient involvement in self-recruited online trials of self-management interventions
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