Predicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methods

Çelik, Güner; Baykan, Ömer Kaan; Kara, Yakup; Tireli, Hülya

doi:10.1016/j.jstrokecerebrovasdis.2013.12.018

Cited by 32 publications

(30 citation statements)

References 18 publications

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“… 0·805 (0·036) Broderick´ [ 44 ] 1993 Spontaneous ICH Equation 30 days Hematoma size, IVH volume, GCS, and surgery. – Celik [ 68 ] 2014 Spontaneous ICH ANN 10 days Age, gender, hypertension, diabetes, smoking, mean blood pressure, Scandinavian Stroke Scale score, pulse pressure, localization of hemorrhage (including infratentorial), volume of hemorrhage, ventricular drainage, and midline shift. – Cerillo [ 21 ] 1981 Operated supratentorial ICH Equation Discharge Age, mode of onset, site of hemorrhage, level of consciousness, time from onset to surgery, congestive heart failure/coronary artery disease, and diabetes/uremia.…”

Section: Resultsmentioning

confidence: 99%

“…Thirteen studies were multicentric [ 13 , 24 , 29 , 30 , 35 – 37 , 42 , 43 , 51 , 55 , 59 , 62 ], with two studies involving more than two countries [ 42 , 55 ]. The sampling method was not reported or unclear for 18 studies [ 12 , 15 , 17 , 21 , 23 , 24 , 26 , 29 , 30 , 32 , 36 , 42 , 54 , 61 , 63 , 66 – 68 ], being consecutive for all others.…”

Section: Resultsmentioning

confidence: 99%

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Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis

Gregório

Pipa

Cavaleiro

et al. 2018

BMC Med Res Methodol

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BackgroundPrognostic tools for intracerebral hemorrhage (ICH) patients are potentially useful for ascertaining prognosis and recommended in guidelines to facilitate streamline assessment and communication between providers. In this systematic review with meta-analysis we identified and characterized all existing prognostic tools for this population, performed a methodological evaluation of the conducting and reporting of such studies and compared different methods of prognostic tool derivation in terms of discrimination for mortality and functional outcome prediction.MethodsPubMed, ISI, Scopus and CENTRAL were searched up to 15th September 2016, with additional studies identified using reference check. Two reviewers independently extracted data regarding the population studied, process of tool derivation, included predictors and discrimination (c statistic) using a predesignated spreadsheet based in the CHARMS checklist. Disagreements were solved by consensus. C statistics were pooled using robust variance estimation and meta-regression was applied for group comparisons using random effect models.ResultsFifty nine studies were retrieved, including 48,133 patients and reporting on the derivation of 72 prognostic tools. Data on discrimination (c statistic) was available for 53 tools, 38 focusing on mortality and 15 focusing on functional outcome. Discrimination was high for both outcomes, with a pooled c statistic of 0.88 for mortality and 0.87 for functional outcome. Forty three tools were regression based and nine tools were derived using machine learning algorithms, with no differences found between the two methods in terms of discrimination (p = 0.490). Several methodological issues however were identified, relating to handling of missing data, low number of events per variable, insufficient length of follow-up, absence of blinding, infrequent use of internal validation, and underreporting of important model performance measures.ConclusionsPrognostic tools for ICH discriminated well for mortality and functional outcome in derivation studies but methodological issues require confirmation of these findings in validation studies. Logistic regression based risk scores are particularly promising given their good performance and ease of application.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0613-8) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis

Gregório

Pipa

Cavaleiro

et al. 2018

BMC Med Res Methodol

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“…Sin embargo, cuando se compararon los resultados de los métodos en la fase de prueba, la mejor clasificación, especificidad, sensibilidad y precisión del 87.8 % fue alcanzado por el modelo MDA para el grupo hemorrágico. La mejor clasificación, especificidad 75.9 %, sensibilidad 85.9 % y precisión 80.9 % para el grupo isquémico fue alcanzado por la técnica LRA [13].…”

Section: Estado Del Arteunclassified

Algoritmos de aprendizaje automático de vanguardia para el diagnóstico de enfermedades

Rodríguez¹,

Biswal²,

Cruz³

2019

RCS

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Resumen. En el presente trabajo se analizaron artículos sobre el uso de sistemas de aprendizaje automático utilizados para el diagnóstico de enfermedades como: cáncer de mama, cáncer de próstata, cardiovasculares, hipertensión, Parkinson, infartos, Artritis reumatoide, Triage, etc. También para la predicción de mortalidad en los hospitales y supervivencia posterior a eventos cardiovasculares, De los cuales se seleccionaron los más relevantes ya que en ellos los autores comparan el uso de diferentes algoritmos y herramientas de desarrollo dando su punto de vista de los másóptimos y con mayoríndice de exito para realizar diagnósticos basados en base de datos, antecedentes e imágenes de acuerdo sus necesidades y tipos de enfermedades.Palabras clave: aprendizaje automático, algoritmos, médico, diagnóstico.Abstract. In the present work, articles regarding the use of machine learning systems used for the diagnosis of diseases such as breast cancer, prostate cancer, cardiovascular diseases, hypertension, Parkinson's, heart attacks, rheumatoid arthritis, triage, etc. were analyzed. Also for the prediction of the mortality and survival rate after cardiovascular events, the most relevant cases were selected where the authors compare the use of different algorithms and development tools. giving their point of view of the most optimal and with a higher rate of satisfaction to make diagnoses based on data base, background and images according to their needs and types of diseases.

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“…In medicine, they have been proposed as a viable computational tool in order to provide support in clinical decision-making, classification, and prediction of cognitive functioning [18]. Their clinical applications include diagnosis and outcome prediction in a wide range of diseases, such as cancer [19], diabetes [20], or acute stroke mortality [21]. Regarding other neurological disorders, ANNs have been recently applied for classifying different multiple sclerosis courses [22] and for white matter lesion segmentation [23] by using neuroimaging data.…”

Section: Etiologymentioning

confidence: 99%

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

et al. 2018

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Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.

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Predicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methods

Cited by 32 publications

References 18 publications

Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis

Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis

Algoritmos de aprendizaje automático de vanguardia para el diagnóstico de enfermedades

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

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