Artificial Intelligence in Acute Kidney Injury Risk Prediction

Gameiro, Joana; Branco, Tiago Pugliese; Lopes, José António

doi:10.3390/jcm9030678

Cited by 53 publications

(43 citation statements)

References 120 publications

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“…In medicine, deep learning has led to advances such as a tool that predicts diabetic retinopathy using retinal fundus photographs and a test that distinguishes COVID-19 from community-acquired pneumonia using chest computed tomography imaging [ [2] , [3] , [4] ]. Machine learning algorithms using patient data from the electronic medical record have been designed to predict acute kidney injury, cancer mortality rate, and prognosis following solid organ transplantation [ [5] , [6] , [7] ]. New applications are sure to emerge as this technology matures [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Modern Internet Search Analytics and Total Joint Arthroplasty: What Are Patients Asking and Reading Online?

Shen

Driscoll

Islam

et al. 2021

The Journal of Arthroplasty

100

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Introduction Patients considering total joint arthroplasty often search for information online regarding surgery, however little is known about the specific topics that patients search for and the nature of the information provided. Google compiles frequently asked questions associated with a search term using machine learning and natural language processing. Links to individual websites are provided to answer each question. Analysis of this data may help improve understanding of patient concerns and inform more effective counseling. Methods Search terms were entered into Google for total hip and total knee arthroplasty. Frequently asked questions and associated websites were extracted to a database using customized software. Questions were categorized by topic; websites were categorized by type. JAMA Benchmark Criteria were used to assess website quality. Pearson’s chi-square and Student’s t-tests were performed as appropriate. Results A total of 620 questions (305 TKA, 315 THA) were extracted with 602 associated websites. The most popular question topics were Specific Activities (23.5%), Indications/Management (15.6%), and Restrictions (13.4%). Questions related to Pain were more common in the TKA group (23.0% vs 2.5%, p<0.001) compared to THA. The most common website types were Academic (31.1%), Commercial (29.2%), and Social Media (17.1%). JAMA scores (0-4) were highest for Government websites (mean 3.92, p=0.005). Conclusion The most frequently asked questions on Google related to total joint arthroplasty are related to arthritis management, rehabilitation, and ability to perform specific tasks. A sizeable proportion of health information provided originate from non-academic, non-government sources (64.4%), with 17.1% from social media websites.

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Section: Introductionmentioning

confidence: 99%

Modern Internet Search Analytics and Total Joint Arthroplasty: What Are Patients Asking and Reading Online?

Shen

Driscoll

Islam

et al. 2021

The Journal of Arthroplasty

100

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“…Those needs that are classified as unmet require provision of some ample spaces for the purpose of imagination in relation to leveraging the strength associated with big data, as well as relevant artificial intelligence (AI) to improve the overall status of patients with kidney diseases [25]. In this article, we discuss the big data concepts in nephrology, describe the potential use of AI in nephrology and transplantation, and also encourage researchers and clinicians to submit their invaluable research, including original clinical research studies [26][27][28][29][30], database studies from registries [31][32][33], meta-analyses [34][35][36][37][38][39][40][41][42][43][44], and artificial intelligence research [25,[45][46][47][48] in nephrology and transplantation. Table 1 demonstrates known and commonly used databases that have provided big data in nephrology and transplantation [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

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“…The use of artificial intelligence algorithms and electronic alerts based on the patient health records has enhanced the development of predictive and risk stratification algorithms which have been reported to improve AKI detection [ 51 , 52 , 53 ].…”

Section: Managementmentioning

confidence: 99%

Management of Acute Kidney Injury Following Major Abdominal Surgery: A Contemporary Review

Gameiro

Fonseca

Marques

et al. 2020

JCM

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Acute kidney injury (AKI) is a frequent occurrence following major abdominal surgery and is independently associated with both in-hospital and long-term mortality, as well as with a higher risk of progressing to chronic kidney disease (CKD) and cardiovascular events. Postoperative AKI can account for up to 40% of in-hospital AKI cases. Given the differences in patient characteristics and the pathophysiology of postoperative AKI, it is inappropriate to assume that the management after noncardiac and nonvascular surgery are the same as those after cardiac and vascular surgery. This article provides a comprehensive review on the available evidence on the management of postoperative AKI in the setting of major abdominal surgery.

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Artificial Intelligence in Acute Kidney Injury Risk Prediction

Cited by 53 publications

References 120 publications

Modern Internet Search Analytics and Total Joint Arthroplasty: What Are Patients Asking and Reading Online?

Modern Internet Search Analytics and Total Joint Arthroplasty: What Are Patients Asking and Reading Online?

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Management of Acute Kidney Injury Following Major Abdominal Surgery: A Contemporary Review

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