Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention

Abstract

Background The prognosis for patients with severe acute lower intestinal bleeding (ALIB) may be assessed by complex artificial neural networks (ANNs) or user-friendly regression-based models. Comparisons between these modalities are limited, and predicting the need for surgical intervention remains elusive. We hypothesized that ANNs would outperform the Strate rule to predict severe bleeding and would also predict the need for surgical intervention. Methods We performed a 4-y retrospective analysis of 147 adult patients who underwent endoscopy, angiography, or surgery for ALIB. Baseline characteristics, Strate risk factors, management parameters, and outcomes were analyzed. The primary outcomes were severe bleeding and surgical intervention. ANNs were created in SPSS. Models were compared by area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals. Results The number of Strate risk factors for each patient correlated significantly with the outcome of severe bleeding (r = 0.29, P < 0.001). However, the Strate model was less accurate than an ANN (AUROC 0.66 [0.57–0.75] versus 0.98 [0.95–1.00], respectively) which incorporated six variables present on admission: hemoglobin, systolic blood pressure, outpatient prescription for Aspirin 325 mg daily, Charlson comorbidity index, base deficit ≥5 mEq/L, and international normalized ratio ≥1.5. A similar ANN including hemoglobin nadir and the occurrence of a 20% decrease in hematocrit was effective in predicting the need for surgery (AUROC 0.95 [0.90–1.00]). Conclusions The Strate prediction rule effectively stratified risk for severe ALIB, but was less accurate than an ANN. A separate ANN accurately predicted the need for surgery by combining risk factors for severe bleeding with parameters quantifying blood loss. Optimal prognostication may be achieved by integrating pragmatic regression-based calculators for quick decisions at the bedside and highly accurate ANNs when time and resources permit.

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

“…As described above, MLP was employed for the construction of ANN. The MLP consists of an input layer of nodes containing information, such as risk factors, followed by a hidden layer of nodes that interact with the input variables that are finally transferred to the output layer [ 21 , 28 ]. In the input layer, the number of neurons depends on the number of independent variables, whereas the number of neurons in the output layer correlates with the number of values that need to be predicted [ 21 , 28 ].…”

“…The MLP consists of an input layer of nodes containing information, such as risk factors, followed by a hidden layer of nodes that interact with the input variables that are finally transferred to the output layer [ 21 , 28 ]. In the input layer, the number of neurons depends on the number of independent variables, whereas the number of neurons in the output layer correlates with the number of values that need to be predicted [ 21 , 28 ]. The steps of MLP are summarised as follows [ 21 , 28 ]: (1) data is provided to input layer; (2) input layer produces a predicted output layer, which is subtracted from actual output, and error value is estimated; (3) a back propagation adjusts the weights between output and hidden layer nodes, which works backwards through network; (4) when a back propagation is finished, the process starts again; and (5) this process is repeated until error is minimised.…”

“…Although there are several algorithms for constructing ANNs, we employed a multilayer perceptron (MLP) in this study. MLP is one of the typical supervised learning algorithms in which a small number of parameters can be used to predict outcomes [ 21 , 22 ]. In addition, MLP can be performed by packaging software, such as SPSS (IBM, Tokyo, Japan) and JMP (SAS Institute, Inc., Cary, NC, USA) [ 4 – 6 , 23 , 24 ].…”

Advancing care for acute gastrointestinal bleeding using artificial intelligence

“…However, no prospective trials apply the score to test its performance in affecting differences in healthcare utilization. Machine learning scores, particularly neural network models, appear to perform better than clinical risk scores for patients with lower gastrointestinal bleeding 13–15 …”

Response to Er and Tez: the use of artificial neural networks in medical prognostication

“…Even so, the ANN demonstrated considerable accuracy in predicting severe lower intestinal bleeding using variables present on admission. 2…”