Drawing-Based Automatic Dementia Screening Using Gaussian Process Markov Chains

Screening tests play an important role for early detection of dementia. Among those widely used screening tests, drawing tests have gained much attention in clinical psychology. Traditional evaluation of drawing tests totally relies on the appearance of drawn picture, but does not consider any time-dependent behaviour. We demonstrated that the processing speed and direction can reflect the decline of cognitive function, and thus may be useful for disease screening. We proposed a model of Gaussian process Markov chains (GPMC) to study the complex associations within the drawing data. Specifically, we modeled the process of drawing in a state-space form, where a drawing state is composed of drawing direction and velocity with consideration of the processing time. For temporal modeling, our scope focused more on discrete-time Markov chains on continuous state space. Because of the short processing time of picture drawing, we applied higher-order of Markov chains to model long-term temporal correlation across drawing states. Gaussian process regression was used for universal function approximation to flexibly infer the state transition function. With Gaussian process prior to the distribution of function space, we could encode high-level function properties such as noisiness, smoothness and periodicity. We also derived an efficient training mechanism for complex Gaussian process regression on bivariate Markov chains. With GPMC, we present an optimal decision rule based on Bayesian decision theory. We applied our proposed method to a drawing test for dementia screening, i.e. interlocking pentagon-drawing test. We tested our models with 256 subjects who are aged from 65 to 95. Finally, comparing to the traditional methods, our models showed remarkable improvement in drawing test for dementia screening

Data Visualization on Global Trends on Cancer Incidence An Application of IBM Watson Analytics

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across over a hundred of cancer registries worldwide. In this study, we present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. We included 26 cancers from different geographic regions. An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, we can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is assessable by any computer connected to the Internet

Social Media as a Tool to Look for People with Dementia Who Become Lost: Factors That Matter

This research explored how social media were used to look for people with dementia who went lost, and investigated what features of social media usage were associated with the outcomes of finding. Tweets that were disseminated to find missing people with dementia were collected and clustered by cases. Ten cases were selected as sample cases and traced for the outcomes of finding. Information of the Twitter users who tweeted and retweeted were retrieved and categorized. Descriptive analysis was applied to examine the lost cases and features of social media usage; T-test and chi-square analysis were conducted between outcomes of the lost incidents and key features of tweets and Twitter users. Results indicated that there was no significant association between the average number of tweets and retweets and the outcomes of finding, but social media users, especially the ones with a larger group of followers (audience), such as the media, should be encouraged to spread such information. However, a code of conduct is needed to ensure social media are not abused

A Twitter Dataset on Tweets about People who Got Lost due to Dementia

This is the dataset used and analyzed in the paper "How can we Better Use Twitter to find a Person who Got Lost due to Dementia?".<div><br></div><div>A total of five tables are included. </div><div>1. raw_tweets.rds: All tweets that mentioned (i) "Dementia" or "Alzheimer"; and (ii) "Lost" or "Missing", which were crawled from Twitter from April to May 2017. </div><div>2. raw_userinfo.rds: The corresponding Twitter user info of Tweets.</div><div>3. filtered_tweets.csv: Tweets that were included in the study. Details (age, gender, place, etc.) of the corresponding lost person mentioned in each tweet were appended in this table. </div><div>4. filtered_userinfo.csv: The corresponding Twitter user info of Tweets that were included in the study. Occupation (police / media / others) of each user were appended in this table. </div><div>5. cleansed_lostcases.csv: A cleansed table that shows several features of the lost cases.<br></div