Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

Alzheimer’s disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer’s disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.

Patients with chronic insomnia are prone to long-term use of hypnotics. Reported risk factors include aging, female sex, and comorbid psychiatric disorders. However, most previous studies have been cross-sectional cohort studies. We conducted a retrospective cohort study using medical service payment data for 330,000 people to determine the duration of prescription of hypnotics and the risk factors for long-term use. We followed up 3981 patients (2382 M, 1599 F, age 40.3 ± 12.4 years) who were prescribed hypnotics for the first time between April 2005 and March 2008. Of these 3981 patients, 59.6% were prescribed hypnotics for only 1 month, 11.3% were prescribed hypnotics for 2 consecutive months, and 10.1% of patients continued receiving prescriptions for the entire 12-month observation period. In multiple logistic time-dependent Cox analyses, use of antidepressants, mean dose of hypnotics, and advanced age were significantly associated with long-term use of hypnotics (p < 0.01). In an analysis of the association between long-term use of hypnotics and prescribed dosage, high monthly dose, advanced age, and department of first visit were significantly associated with long-term use (p < 0.01). These clinical indicators may be effective for early identification of patients with insomnia who are at high risk of developing physical dependence on hypnotics.

There is a dearth of sleep questionnaires with few items and confirmed reliability and validity that can be used for the early detection of sleep problems in children. The aim of this study was to develop a questionnaire with few items and assess its reliability and validity in both children at high risk of sleep disorders and a community population. Data for analysis were derived from two populations targeted by the Children’s Sleep Habits Questionnaire (CSHQ): 178 children attending elementary school and 432 children who visited a pediatric psychiatric hospital (aged 6–12 years). The new questionnaire was constructed as a subset of the CSHQ. The newly developed short version of the sleep questionnaire for children (19 items) had an acceptable internal consistency (0.65). Using the cutoff value of the CSHQ, the total score of the new questionnaire was confirmed to have discriminant validity (27.2 ± 3.9 vs. 22.0 ± 2.1, p < 0.001) and yielded a sensitivity of 0.83 and specificity of 0.78 by receiver operator characteristic curve analysis. Total score of the new questionnaire was significantly correlated with total score (r = 0.81, p < 0.001) and each subscale score (r = 0.29–0.65, p < 0.001) of the CSHQ. The new questionnaire demonstrated an adequate reliability and validity in both high-risk children and a community population, as well as similar screening ability to the CSHQ. It could thus be a convenient instrument to detect sleep problems in children.