New medical big data for P4 medicine on allergic conjunctivitis

Inomata, Takenori; Sung, Jaemyoung; Nakamura, Masahiro; Fujisawa, Kumiko; Muto, Kaori; Ebihara, Nobuyuki; Iwagami, Masao; Fujio, Kenta; Okumura, Yuichi; Okano, Mitsuhiro; Murakami, Akira

doi:10.1016/j.alit.2020.06.001

Cited by 29 publications

(50 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, due to the lack of understanding, the multicause of chronic obstructive pulmonary disease is that the environment, residence, and direct distribution have complex relationships. The benefits of medicine for chronic kidney disease patients and the type of treatment for dementia, as well as the possibilities for other diseases that need to be known by patients that will help patients according to the symptoms and risk factors of the intervention's social empowerment and rehabilitation of people with chronic obstructive pulmonary disease to reduce the body helping to create permanent solutions and prevent the escalation of social ills [ 68 ].…”

Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

show abstract

Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Another report on J-OSDI-related apps and questionnaires also showed matching test results 4 , which leaves the validity and reliability of MBI in a remote setting as the last remaining portion to assess the utilization of MBI/J-OSDI in a mobile health environment for DED diagnosis. Mobile health devices, including smartphones and smartwatches, are aptly positioned for a continuous, longitudinal evaluation of one's health conditions without the subjects leaving their daily activities 25 . Additionally, the now-commonplace biosignal sensors in mobile devices are further accelerating the revolution of the traditional facility-based healthcare paradigm toward a participatory, day-to-day healthcare within one's daily life.…”

Section: Discussionmentioning

confidence: 99%

“…The ease of use that this new test sequence offers align well with the pillars of P4 medicine-a medical model that advocates for the predictive, preventative, personalized, and participatory aspect of medicine. Such non-invasive tests that minimally affect one's daily activities could contribute in early diagnosis and self-management25 .…”

mentioning

confidence: 99%

Diagnostic ability of maximum blink interval together with Japanese version of Ocular Surface Disease Index score for dry eye disease

Hirosawa

Inomata

Sung

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Various symptoms of the dry eye disease (DED) interfere with the quality of life and reduce work productivity. Therefore, screening, prevention, and treatment of DED are important. We aimed to investigate the potential diagnostic ability of the maximum blink interval (MBI) (the length of time participants could keep their eyes open) with disease-specific questionnaire for DED. This cross-sectional study included 365 patients (252 with DED and 113 without DED) recruited between September 2017 and December 2019. Discriminant validity was assessed by comparing the non-DED and DED groups based on the MBI with a Japanese version of the Ocular Surface Disease Index (J-OSDI) and tear film breakup time (TFBUT) with J-OSDI classifications. The MBI with J-OSDI showed good discriminant validity by known-group comparisons. The positive and predictive values of MBI with J-OSDI were 96.0% (190/198 individuals) and 37.1% (62/167 individuals), respectively. The area under the receiver operating characteristic curve (AUC) of MBI with J-OSDI was 0.938 (95% confidence interval 0.904–0.971), the sensitivity was 75.4% (190/252 individuals), and the specificity was 92.9% (105/113 individuals), which are similar to the diagnostic ability of TFBUT with J-OSDI (AUC 0.954). In conclusion, MBI with J-OSDI may be a simple, non-invasive screening test for DED.

show abstract

“…Drawing a parallel to IoT, the Internet of Medical Things (IoMT) acts as a networking platform to communicate and share medical big data. The big data gathered from users, electronic medical records, and environmental/geographical reports from independent sources can be collected and analyzed by AI to implement core values of P4 medicine: predictive, preventive, personalized, and participatory medicine (Figure 1) 2,3) . This model will reduce the role of traditional facility-based care and introduce effective longitudinal patientand population-oriented care based on personalized, lifetime, and preventative interventions within one's daily life.…”

Section: Introduction Society 50 and P4 Medicinementioning

confidence: 99%

Cross-hierarchical Integrative Research Network for Heterogenetic Eye Disease Toward P4 Medicine: A Narrative Review

Inomata

Sung

Nakamura

et al. 2021

Juntendo Medical Journal

Self Cite

View full text Add to dashboard Cite

Society 5.0, a visionary human-centered societal model, fuels economic development and resolves long-standing social problems. The model establishes a technological foundation and social contract to integrate cyberspace into the physical (real) space fully. The medical infrastructure outlined by the model envisions a healthcare paradigm that revolves around preventative, lifelong patient-and population-centered care that functions seamlessly within one's daily life.In satisfying this goal, cross-hierarchical integrative data-driven biological research has received attention due to medical big data and artificial intelligence (AI) technologies, capable of highly accurate and rapid data analysis. However, the collection of big data has been a bottleneck, and the capability of AI analysis is not being utilized to its full potential. In solving this obstacle, we explore mobile health (mHealth) and multi-omics as two rich sources of medical big data. Additionally, we discuss the implications of cross-hierarchical integrative analysis that encompasses all levels of cellular function, from intracellular molecular dynamics to end-phenotypes. This is to understand ocular disease pathology and implement the pillars of P4 (predictive, personalized, preventative, participatory) medicine toward human-centered healthcare.Here, we discuss notable studies in utilizing mHealth to stratify subjective symptoms, presentations of dry eye disease, and employing multi-omics machine learning targeted at elucidating immunologic mechanisms of corneal allograft rejection and ocular inflammation. We also discuss the role of cross-hierarchical integrative data-driven research in promoting futureoriented healthcare envisioned by the Society 5.0 plan.

show abstract

New medical big data for P4 medicine on allergic conjunctivitis

Cited by 29 publications

References 91 publications

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Diagnostic ability of maximum blink interval together with Japanese version of Ocular Surface Disease Index score for dry eye disease

Cross-hierarchical Integrative Research Network for Heterogenetic Eye Disease Toward P4 Medicine: A Narrative Review

Contact Info

Product

Resources

About