Artificial neural networks based early clinical prediction of mortality after spontaneous intracerebral hemorrhage

Abstract

Numerous outcome prediction models have been developed for mortality and functional outcome after spontaneous intracerebral haemorrhage (ICH). However, no outcome prediction model for ICH has considered the impact of care restriction. To develop and compare results of the artificial neural networks (ANN) and logistic regression (LR) models, based on initial clinical parameters, for prediction of mortality after spontaneous ICH. Analysis has been conducted on consecutive dataset of patients with spontaneous ICH, over 5-year period in tertiary care academic hospital. Patients older than 18 years were eligible for inclusion if they had been presented within 6 h from the start of symptoms and had evidence of spontaneous supratentorial ICH on initial brain computed tomography within 24 h. Initial clinical parameters have been used to develop LR and ANN prediction models for hospital mortality as outcome measure. Models have been accessed for discrimination and calibration abilities. We have analyzed 411 patients (199 males and 212 females) with spontaneous ICH, medically treated and not withdrawn from therapy, with average age of 67.35 years. From them, 256 (62.29%) patients died during hospital treatment and 155 (37.71%) patients survived. In the observed dataset, ANN model overall correctly classified outcome in 93.55% of patients, compared with 79.32% of correct classification for the LR model. Discrimination and calibration parameters indicate that both models show an adequate fit of expected and observed values, with superiority of ANN model. Our results favour the ANN model for prediction of mortality after spontaneous ICH. Further studies of the strengths and limitations of this method are needed with larger prospective samples.

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

“… 0·923 (0·020) Li´ [ 49 ] 2011 Primary ICH Score Discharge Age, Glucose, LDH, and white blood cell count. 0·745 (0·025) Lukic [ 33 ] 2012 Primary supratentorial medically treated ICH Equation Discharge Level of consciousness, GCS verbal response, age, gender, and pulse pressure. 0·856 (0·018) Lukic´ [ 26 ] 2012 Spontaneous supratentorial ICH ANN Discharge Age, gender, pulse pressure, mean arterial pressure, GCS (E/V/M), and consciousness.…”

“…However, several studies included further specifications for patient inclusion namely age [ 12 ], cerebral amyloid related angiopathy [ 13 ], deep location [ 14 – 17 ], lobar location [ 18 ], supratentorial bleeds [ 16 , 19 – 33 ], presence of intraventricular hemorrhage [ 16 , 34 , 35 ], African ethnicity [ 36 ], non-comatose patients [ 22 , 37 ], comatose patients [ 38 ], medically treated patients [ 22 – 25 , 27 , 33 , 38 – 40 ], surgically treated patients [ 21 ], oral anticoagulant related bleeds [ 41 ], hypertensive patients [ 19 , 36 ], and dialysis patients [ 40 ]. The majority of studies ( n = 40) recruited patients from hospitals or emergency rooms [ 10 – 14 , 16 – 22 , 26 , 29 – 31 , 36 , 40 – 61 ] but nine studies recruited patients from intensive care units [ 24 , 28 , 32 , 33 , 35 , 38 , 62 – 64 ], three studies recruited patients from stroke units [ 34 , 37 , 65 ], six studies recruited patients from neurology/neurosurgery departments [ 15 , 25 , 27 , 39 , 66 , 67 ], and one study recruited patients from both an intensive care unit and a stroke unit [ 23 ]. Most prognostic tools were derived from cohort studies, with the exceptions being registries [ 29 , …”

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

“…Further developments can also be performed in this field. For instance, the risk of a second stroke or even the risk of death after a stroke can be predicted using ANNs [31].…”

“…The two equations in (31) are update laws in the ILC framework, and they depend on the error at current iteration j. Concluding, solving the optimization problem defined in (17) at each iteration, results in the expression (15) of ANN training equations.…”

Intelligent Flow Friction Estimation

“…Nowadays, not only can the ANN approach be used in hydraulics and for simulation of fluid flow, but also it can be widely applied in the various branches of engineering, such as for the control systems [19,20], as an auxiliary tool in medicine [21][22][23][24][25], a flow pattern indicator for gas-liquid flow in a microchannel [26], and an extension of structural mechanics tools for fast determination of structural response [27]. Also combined neurofuzzy systems (NFS) approach can be used for different purposes such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, and social sciences [28].…”