Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

Khamisy‐Farah, Rola; Furstenau, Leonardo Bertolin; Kong, Jude Dzevela; Wu, Jianhong; Bragazzi, Nicola Luigi

doi:10.3390/ijerph18105058

Cited by 19 publications

(12 citation statements)

References 63 publications

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“…More personalised strategies to initiate dietary behaviour changes may be more effective than public health campaigns, and rapid evolvement of technological tools and mobile internet accessibility now offers the opportunity to provide such personalised counselling through mobile applications (i.e., apps) [12][13][14][15][16]. Nowadays, women are shifting from traditional sources toward digital sources for pregnancy-related information and support.…”

Section: Introductionmentioning

confidence: 99%

Midwives’ Experiences with and Perspectives on Online (Nutritional) Counselling and mHealth Applications for Pregnant Women; an Explorative Qualitative Study

Wit

Lucassen

Beulen

et al. 2021

IJERPH

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Prenatal nutrition is a key predictor of early-life development. However, despite mass campaigns to stimulate healthy nutrition during pregnancy, the diet of Dutch pregnant women is often suboptimal. Innovative technologies offer an opportunity to develop tailored tools, which resulted in the release of various apps on healthy nutrition during pregnancy. As midwives act as primary contact for Dutch pregnant women, the goal was to explore the experiences and perspectives of midwives on (1) nutritional counselling during pregnancy, and (2) nutritional mHealth apps to support midwifery care. Analyses of eleven in-depth interviews indicated that nutritional counselling involved the referral to websites, a brochure, and an app developed by the Dutch Nutrition Centre. Midwives were aware of the existence of other nutritional mHealth apps but felt uncertain about their trustworthiness. Nevertheless, midwives were open towards the implementation of new tools providing that these are trustworthy, accessible, user-friendly, personalised, scientifically sound, and contain easy-digestible information. Midwives stressed the need for guidelines for professionals on the implementation of new tools. Involving midwives early-on in the development of future nutritional mHealth apps may facilitate better alignment with the needs and preferences of end-users and professionals, and thus increase the likelihood of successful implementation in midwifery practice.

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Section: Introductionmentioning

confidence: 99%

Midwives’ Experiences with and Perspectives on Online (Nutritional) Counselling and mHealth Applications for Pregnant Women; an Explorative Qualitative Study

Wit

Lucassen

Beulen

et al. 2021

IJERPH

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“…In order to make the most of the medical big data, related scholars have put forward the concept of disease monitoring, which is to monitor, prevent, and assist treatment of corresponding diseases based on medical big data. Obesity monitoring is one of them [ 2 ]. Obesity monitoring means to collect, check, and analyze data on the long-term dynamic distribution of obesity and internal influencing factors of patients and take measures to prevent and control obesity based on the collected data and analysis results.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren

Zhao

2021

Computational Intelligence and Neuroscience

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With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

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“…Big data are characterized by five Vs: namely, (I) velocity (referring to the speed and the pace at which they are generated); (II) volume (related to the unprecedented amount of data available); (III) variety (because they are inherently heterogeneous); (IV) veracity (referring to the data quality, accuracy, and reliability); and (V) value (when big data are properly processed and analyzed and become actionable, smart data). Based on the sources and channels that produce and release big data, they can be classified/categorized into: computational, digital, and molecular (generated by wet-lab, next-generation sequencing, and high-throughput technologies) [11,12]. Big data are leading innovation and designing a new era, the so-called disruptive or innovative era [11,12].…”

Section: Medical Education: An Overview Of Its History and Evolutionmentioning

confidence: 99%

“…Based on the sources and channels that produce and release big data, they can be classified/categorized into: computational, digital, and molecular (generated by wet-lab, next-generation sequencing, and high-throughput technologies) [11,12]. Big data are leading innovation and designing a new era, the so-called disruptive or innovative era [11,12].…”

Section: Medical Education: An Overview Of Its History and Evolutionmentioning

confidence: 99%

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

Khamisy‐Farah

Gilbey

Furstenau

et al. 2021

IJERPH

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Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.

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Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

Cited by 19 publications

References 63 publications

Midwives’ Experiences with and Perspectives on Online (Nutritional) Counselling and mHealth Applications for Pregnant Women; an Explorative Qualitative Study

Midwives’ Experiences with and Perspectives on Online (Nutritional) Counselling and mHealth Applications for Pregnant Women; an Explorative Qualitative Study

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

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