Automatic selection of spoken language biomarkers for dementia detection.

This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-step feature selection (FS) approach: Step 1 utilizes filter methods to pre-screen features, and Step 2 uses a novel feature ranking (FR) method, referred to as dual dropout ranking (DDR), to rank the screened features and select spoken language biomarkers. The proposed DDR is based on a dual-net architecture that separates FS and dementia detection into two neural networks (namely, the operator and selector). The operator is trained on features obtained from the selector to reduce classification or regression loss. The selector is optimized to predict the operator's performance based on automatic regularization. Results show that the approach significantly reduces feature dimensionality while identifying small feature subsets that achieve comparable or superior performance compared with the full, default feature set. The Python codes are available at https://github.com/kexquan/dual-dropout-ranking.

How can we better use Twitter to find a person who got lost due to dementia?

Twitter is a social media platform for online message sharing. The aim of this study is to evaluate the effectiveness of using Twitter to search for people who got lost due to dementia. The online messages on Twitter, i.e., tweets, were collected through an Application Programming Interface. Contents of the tweets were analysed. The personal characteristics, features of tweets and types of Twitter users were collected to investigate their associations with whether a person can be found within a month. Logistic regression was used to identify the features that were useful in finding the missing people. Results showed that the young age of the persons with dementia who got lost, having tweets posted by police departments, and having tweets with photos can increase the chance of being found. Social media is reshaping the human communication pathway, which may lead to future needs on a new patient-care model.

Cardiovascular mortality in hypertensive patients newly prescribed perindopril vs. lisinopril: a 5-year cohort study of 15,622 Chinese subjects.

BACKGROUND: Perindopril and lisinopril are two common ACE inhibitors prescribed for management of hypertension. Few studies have evaluated their comparative effectiveness to reduce mortality. This study compared the all-cause and cardiovascular related mortality among patients newly prescribed ACE inhibitors. METHODS: All adult patients newly prescribed perindopril or lisinopril from 2001 to 2005 in all public clinics or hospitals in Hong Kong were retrospectively evaluated, and followed up until 2010. Patients prescribed the ACE inhibitors for less than a month were excluded. The all-cause mortality and cardiovascular-specific (i.e. coronary heart disease, heart failure and stroke) mortality were compared. Cox proportional hazard regression model was used to assess the mortality, controlling for age, sex, socioeconomic status, patient types, the presence of comorbidities, and medication adherence as measured by the proportion of days covered. An additional model using propensity scores was performed to minimize indication bias. RESULTS: A total of 15,622 patients were included in this study, in which 6910 were perindopril users and 8712 lisinopril users. The all-cause mortality (22.2% vs. 20.0%, p<0.005) and cardiovascular mortality (6.5% vs. 5.6%, p<0.005) were higher among lisinopril users than perindopril users. From regression analyses, lisinopril users were 1.09-fold (95% C.I. 1.01-1.16) and 1.18-fold (95% C.I. 1.02-1.35) more likely to die from any-cause and cardiovascular diseases, respectively. Age-stratified analysis showed that this significant difference was observed only among patients aged >70 years. The additional models controlled for propensity scores yielded comparable results. CONCLUSIONS: The long-term all-cause and cardiovascular related mortality rates of lisinopril users was significantly different from those of perindopril users. These findings showed that intra-class variation on mortality exists among ACE inhibitors among those aged 70 years or older. Future studies should consider a longer, large-scale randomized controlled trial to compare the effectiveness between different medications in the ACEI class, especially among the elderly.