Big Data Analytics in urology: the story so far and the road ahead

Hameed, B. M. Zeeshan; Dhavileswarapu, Aiswarya V. L. S.; Naik, Nithesh; Karimi, Hadis; Hegde, Padmaraj; Prasad, Bhavan; Somani, Bhaskar K.

doi:10.1177/1756287221998134

Cited by 10 publications

(10 citation statements)

References 46 publications

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“…Several usability studies have been summarised in Table 2 based on their intended use. [52], [54] [55], [56] [57]- [59] [60]- [62] [63], [64] [65], [66] [67], [68] [69]- [72] [73]- [75] [76]- [78] [79], [80] [81], [82] [83], [84] To keep up with the tremendous growth in biomedical and healthcare papers, we've undertaken a comprehensive assessment of healthcare analytics across five subfields as illustrated in Table 3. Big data analytics is being used in the field of bioinformatics because to its complicated and enormous datasets, according to the results of the study.…”

Section: Big Data In Healthcarementioning

confidence: 99%

Complex predictive analysis for health care: a comprehensive review

2023

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Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.

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Section: Big Data In Healthcarementioning

confidence: 99%

Complex predictive analysis for health care: a comprehensive review

2023

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“…AI, big data and other technology applications in urology 28 . Adapted from Hameed BMZ, Dhavileswarapu AVLS, Naik N, et al .…”

Section: Figurementioning

confidence: 99%

“…A systematic review found that, in comparison with traditional clinical diagnosis and statistical outcome predictions, AI was found to be superior in 71.8% of studies 27 . In particular, AI and digital health are revolutionising the predictive prognosis, recurrence and survival outcomes for prostate cancer, reducing the risks of variability in observer‐dependent Gleason grading and improving reliability of biopsy specimen analysis 28 …”

Section: Digital Services and Urologymentioning

confidence: 99%

The digital future of men's health

Ringrose

Wang

2022

Trends Urol & Men's Health

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“…Students should be provided with courses focusing more on quantitative skills, incorporating machine learning and artificial intelligence, mathematical modeling, computation and simulation, and big data [25]. This is true both at the undergraduate and postgraduate level, with several medical specializations (like radiology, oncology, physical medicine, rehabilitation, rheumatology, cardiology, or urology, among others) expanding current knowledge levels, skills, and competencies [26,27]. Attempts to redesign specialist syllabus are very recent, but it can be anticipated that they will have a great impact on future medical practice.…”

Section: Integrating and Implementing Big Data Teaching Into The Medical Syllabus: Difficulties And Challengesmentioning

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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Big Data Analytics in urology: the story so far and the road ahead

Cited by 10 publications

References 46 publications

Complex predictive analysis for health care: a comprehensive review

Complex predictive analysis for health care: a comprehensive review

The digital future of men's health

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

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