Detection of cognitive impairment using a machine-learning algorithm [Corrigendum]

This article refers to:

Detection of cognitive impairment using a machine-learning algorithm

Youn YC, Choi SH, Shin HW, et al. Neuropsychiatr Dis Treat. 2018;14:2939–2945.

On page 2944, under Supplementary materials section, the correct Table S2 should read as follows:

Table S2 The TensorFlow code using CRE DOS data to predict CI

import numpy as np import tensorflow as tf # each set was developed to create feature (x_data) and outcome (y_data) variables xy = np.loadtxt(‘CRCD_KDSQ_MMSE_train.txt’, unpack=True, dtype=‘float32’) x_data = xy[0:-1] y_data = xy[-1] # model training with the training dataset X = tf.placeholder(tf.float32) Y= tf.placeholder(tf.float32) W = tf.Variable(tf.random_uniform([1, len(x_data)], -1.0, 1.0)) h = tf.matmul(W, X) hypothesis = tf.div(1., 1. + tf.exp(-h)) cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis)) a = tf.Variable(0.15) optimizer = tf.train.gradientDescentOptimizer(a) train = optimizer.minimize(cost) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) for step in range(10501): sess.run(train, feed_dict={X: x_data, Y: y_data}) if (step % 20) == 0: print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}), sess.run(W)) # calculation of the accuracy with the test dataset xy = np.loadtxt(‘CRCD_KDSQ_MMSE_test.txt’, unpack=True, dtype=‘float32’) x_data = xy[0:-1] y_data = xy[-1] print (sess.run(hypothesis, feed_dict={X: x_data, Y: y_data})) answer = tf.equal(tf.floor(hypothesis +0.4), Y) accuracy = tf.reduce_mean(tf.cast(answer, “float”)) print (“Accuracy: ”, accuracy.eval(session=sess, feed_dict={X: x_data, Y: y_data})) sess.close()

Abbreviations: CI, cognitive impairment; CREDOS, Clinical Research Center for Dementia of South Korea.