Data-driven ICU management: Using Big Data and algorithms to improve outcomes

Carra, Giorgia; Salluh, Jorge I.; Ramos, Fernando José da Silva; Meyfroidt, Geert

doi:10.1016/j.jcrc.2020.09.002

Cited by 26 publications

(22 citation statements)

References 42 publications

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“…These are "the internal coefficients or weights within the algorithm which, at the beginning of the training phase, are normally assigned random values and then adjusted" during the model development process. 23…”

Section: Parametersmentioning

confidence: 99%

Systematic review of machine learning models for personalised dosing of heparin

Falconer

Abdel-Hafez

Scott

et al. 2021

Brit J Clinical Pharma

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Section: Parametersmentioning

confidence: 99%

Systematic review of machine learning models for personalised dosing of heparin

Falconer

Abdel-Hafez

Scott

et al. 2021

Brit J Clinical Pharma

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“…Cellular and molecular biology will be shifted into clinical practice by adding "omic" data to the equation. 32 Genomics, epigenomics, transcriptomics, metabolomics and proteonomics will probably play a significant role in sepsis, by subtyping the disease and taking individualization to a whole new level. 33 Aiming for a patient-tailored treatment will be the goal.…”

Section: Improving Critical Care: From Protocols To Advanced Algorithmsmentioning

confidence: 99%

Artificial intelligence: what should an intensivist have in mind in the beginning of the new era?

Filho¹

2021

APIC

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A new era is coming for medicine, and for critical care in particular. The intensive care unit is at the edge of being completely changed by artificial intelligence, and many challenges are ahead of the intensive care physician. This article aims to address the benefits and difficulties that big data will bring to clinicians, and to provide an overview on the subject. Key words: Big Data; Artificial intelligence; ICU; Critical Care; Black box Citation: Filho LACB. Artificial intelligence: what should an intensivist have in mind in the beginning of the new era. Anaesth. Pain intensive care 2021;25(1):8-12. DOI: 10.35975/apic.v25i1.1428 Received: 10 December 2020, Reviewed: 3 January 2021, Accepted: 8 January 2021

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“…These datasets can be used to improve diagnosis, inform preventative medicine practices and reduce adverse effects of drugs and other treatments. The impact of big data is visible across a variety of clinical settings and fields, including intensive care ( Carra et al., 2020 ), emergency departments, cardiovascular diseases ( Leopold et al., 2020 ), mental health ( Simon, 2019 ), oncology ( Patel et al., 2018 ), paediatrics ( Li et al., 2020 ), psychiatry ( Weissman, 2020 ), preventive care ( Batarseh et al., 2020 ), ophthalmology ( Brown et al., 2018 ), dementia ( Ienca et al., 2018 ), diabetes ( Musacchio et al., 2020 ) and asthma ( Tang et al., 2020 ). In the opinion of Edwards and Veale, as quoted by Professor Mitrou “algorithms increasingly regulate our lives, as they enable or support decisions that they are vital of our welfare and freedoms” ( Mitrou, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Hassan

Dhali

Zaman

et al. 2021

Heliyon

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Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

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Data-driven ICU management: Using Big Data and algorithms to improve outcomes

Cited by 26 publications

References 42 publications

Systematic review of machine learning models for personalised dosing of heparin

Systematic review of machine learning models for personalised dosing of heparin

Artificial intelligence: what should an intensivist have in mind in the beginning of the new era?

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

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