Herramientas de sistemas inteligentes en el diagnóstico de los síndromes coronarios agudos: una revisión sistemática

Díaz, John Jaime Sprockel; Tejeda, Miguel; Yate, José; Diaztagle, Juan José; González, Enríque

doi:10.1016/j.acmx.2017.03.002

Cited by 3 publications

(4 citation statements)

References 42 publications

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“…Many reviews included data collected from electronic medical records, hospital information systems, or any databank that used individual patient data to create predictive models or evaluate collective patterns [12,13,[16][17][18][19][20][21][24][25][26][27]30,[33][34][35]37,38,40,[42][43][44][45]. Additionally, four reviews included primary studies based on imaging datasets and databanks, assessing different parameters of accuracy [15,29,31,36].…”

Section: Data Sources and Purposes Of Included Studiesmentioning

confidence: 99%

“…Most of the studies assessed the effects of big data analytics on noncommunicable diseases [12][13][14][15]17,21,22,24,27,31,32,34,36,38,[40][41][42][43][44]. Furthermore, three reviews covered mental health, associated with the indicator "suicide mortality rate" [19,25,45]; three studies were related to the indicator "probability of dying from any of cardiovascular, cancer, diabetes, or chronic renal disease" [16,18,20,28,29]; and two studies were related to the indicator "proportion of bloodstream infections due to antimicrobial-resistant organisms" [26,33].…”

Section: Who Indicators and Core Prioritiesmentioning

confidence: 99%

“…Six studies described the effect of big data analytics in cardiology [13,16,21,42] and neurology [43,44]. One review assessed the use of ML techniques for predicting cardiac arrest [42].…”

Section: Cardiovascular and Neurological Conditionsmentioning

confidence: 99%

“…Similarly, two studies assessed the use of intelligent systems in diagnosing acute coronary syndrome and heart failure [13,21], demonstrating high accuracy levels using several methods such as SVM, feature selection, and neural networks. These studies also described useful clinical features for creating prediction and diagnostic models, such as patient clinical data, electrocardiogram characteristics, and cardiac biomarkers.…”

Section: Cardiovascular and Neurological Conditionsmentioning

confidence: 99%

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Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

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Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

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Section: Data Sources and Purposes Of Included Studiesmentioning

confidence: 99%

Section: Who Indicators and Core Prioritiesmentioning

confidence: 99%

“…Six studies described the effect of big data analytics in cardiology [13,16,21,42] and neurology [43,44]. One review assessed the use of ML techniques for predicting cardiac arrest [42].…”

Section: Cardiovascular and Neurological Conditionsmentioning

confidence: 99%

Section: Cardiovascular and Neurological Conditionsmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

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Big data analytics for health

Rayan

Tsagkaris²,

Zafar

et al. 2022

Big Data Analytics for Healthcare

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Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study

et al. 2022

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Artificial intelligence (AI) centred diagnostic systems are increasingly recognised as robust solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in secondary research studies regarding these technologies in order to influence key clinical and policymaking decisions. It is therefore essential that these studies accurately appraise methodological quality and risk of bias within shortlisted trials and reports. In order to assess whether this critical step is performed, we undertook a meta-research study evaluating adherence to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool within AI diagnostic accuracy systematic reviews. A literature search was conducted on all studies published from 2000 to December 2020. Of 50 included reviews, 36 performed the quality assessment, of which 27 utilised the QUADAS-2 tool. Bias was reported across all four domains of QUADAS-2. Two hundred forty-three of 423 studies (57.5%) across all systematic reviews utilising QUADAS-2 reported a high or unclear risk of bias in the patient selection domain, 110 (26%) reported a high or unclear risk of bias in the index test domain, 121 (28.6%) in the reference standard domain and 157 (37.1%) in the flow and timing domain. This study demonstrates the incomplete uptake of quality assessment tools in reviews of AI-based diagnostic accuracy studies and highlights inconsistent reporting across all domains of quality assessment. Poor standards of reporting act as barriers to clinical implementation. The creation of an AI-specific extension for quality assessment tools of diagnostic accuracy AI studies may facilitate the safe translation of AI tools into clinical practice.

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Herramientas de sistemas inteligentes en el diagnóstico de los síndromes coronarios agudos: una revisión sistemática

Abstract: Extensive evidence was found that shows that using intelligent systems tools achieves a greater degree of accuracy than some clinical algorithms or scales and, thus, should be considered appropriate tools for supporting diagnostic decisions of acute coronary syndromes.

Cited by 3 publications

References 42 publications

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Big data analytics for health

Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study

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