Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinelycollected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.

Alzheimers Disease Diagnosis Using Enhanced Inception Network Based on Brain Magnetic Resonance Image An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimeru0027s disease(AD). Nowadays, Alzheimeru0027s disease represents a significant public health concern and has been identified as a research priority. Most unfortunately, there is little chance of a cure for Alzheimeru0027s disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimeru0027s disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimeru0027s disease and normal, and the chance of it turning into Alzheimeru0027s disease is high. Therefore, if machines can automatically learn the characteristics of three kinds of human brain magnetic resonance(MR) images through deep learning, and help doctors to diagnose patients with mild cognitive impairment or Alzheimeru0027s disease accurately, it will be beneficial for the early diagnosis of Alzheimeru0027s disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimeru0027s disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.