Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD

Increased Prevalence in Alzheimer Disease in the Northeast Tennessee Region of the United States

This study describes the changes in prevalence odds ratios (PORs) for Alzheimer disease (AD) in the northeast Tennessee region (NTR) during a 3-year period, describes the statistical assessment process, and critically assesses the database from which the statistical association was derived. The article also examines several beliefs pertinent to the clinical management of AD in the NTR from the perspective of professionals delivering services. Methods We extracted prevalence data for NTR counties for 2013, 2014, and 2015 from the Centers for Medicare and Medicaid Services Geographic Variation Public Use File. We used the crude prevalence and the 2010 US Census Data fixed population for each county to compute the POR. The 2013 Economic Research Service Rural-Urban Continuum Codes were used to identify rural and urban counties in the NTR. We collected primary data on the perceived observation of the increasing prevalence in the NTR during the last 3 years and barriers to early diagnosis through an online survey from 44 experts and professionals working in AD-related fields within the NTR. Results The PORs of AD in rural counties in NTR increased by 18.3%, 4.7%, and 19% compared with urban counties for 2013, 2014, and 2015, respectively. The POR of AD for the entire NTR region increased by 22.7%, 22.5%, and 21.2% compared with other regions in Tennessee for 2013, 2014, and 2015, respectively. Compared with 2012, 68.4% of respondents currently work with more individuals with AD; 71.8% reported that the NTR has a higher number of late-stage diagnoses of AD. A total of 92.3% strongly agreed that early detection of AD is important, and 95% agreed that early diagnosis could prolong the lives of patients with AD; 51.2% were unaware of existing AD screening services. Reported barriers were denial, lack of patient awareness, inefficient screening methods, communication, and lack of community resources. Conclusions Increased prevalence of AD among inhabitants in the NTR and identified barriers to early screening or diagnosis in the management of AD were identified. Access to early screening techniques must be prioritized in deprived areas within the NTR. Healthcare providers and medical professionals in the NTR must be well equipped with the required training and resources to respond adequately to the increasing prevalence of AD

Adolescent Birth Rates and Rural⇓Urban Differences by Levels of Deprivation and Health Professional Shortage Areas in the United States, 2017–2018

Objectives. To examine the differences in adolescent birth rates by deprivation and Health Professional Shortage Areas (HPSAs) in rural and urban counties of the United States in 2017 and 2018. Methods. We analyzed available data on birth rates for females aged 15 to 19 years in the United States using the restricted-use natality files from the National Center for Health Statistics, American Community Survey 5-year population estimates, and the Area Health Resources Files. Results. Rural counties had an additional 7.8 births per 1000 females aged 15 to 19 years (b = 7.84; 95% confidence interval [CI] = 7.13, 8.55) compared with urban counties. Counties with the highest deprivation had an additional 23.1 births per 1000 females aged 15 to 19 years (b = 23.12; 95% CI = 22.30, 23.93), compared with less deprived counties. Rural counties with whole shortage designation had an additional 8.3 births per 1000 females aged 15 to 19 years (b = 8.27; 95% CI = 6.86, 9.67) compared with their urban counterparts. Conclusions. Rural communities across deprivation and HPSA categories showed disproportionately high adolescent birth rates. Future research should examine the extent to which contraceptive access differs among deprived and HPSA-designated rural communities and the impact of policies that may create barriers for rural communities