Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India

Bhagyashree, Sheshadri Iyengar Raghavan; Nagaraj, K.; Prince, Martin; Fall, Caroline H.D.; Krishna, Murali

doi:10.1007/s00127-017-1410-0

Cited by 43 publications

(23 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This includes amyloid PET imaging [12], MR imaging [13] and combined PET and MR imaging [14]. Non-imaging studies have generally focused on demographic data and cognitive measures [15], although more recent studies have utilized novel linguistic analysis [16] and unobtrusive monitoring of gait patterns over time [17]. …”

Section: Introductionmentioning

confidence: 99%

Technology and Dementia: The Future is Now

Astell

Bouranis

Hoey

et al. 2019

Dement Geriatr Cogn Disord

128

View full text Add to dashboard Cite

Background: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. Objectives: To summarise key areas of technology development in dementia and identify future directions and implications. Method: Members of the US Alzheimer’s Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. Results: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.

show abstract

Section: Introductionmentioning

confidence: 99%

Technology and Dementia: The Future is Now

Astell

Bouranis

Hoey

et al. 2019

Dement Geriatr Cogn Disord

128

View full text Add to dashboard Cite

show abstract

“… 24 Most notably, the use of machine learning techniques has garnered much attention in several major therapeutic areas, including diabetes, 20 , 30 cancer, 31 cardiology, 32 ophthalmology, 21 , 33 and psychiatry. 15 , 34 , 35 Two recent studies utilized machine learning in particularly novel ways – in one case, to evaluate the trend in sentiment toward papillomavirus vaccination using Twitter data, 36 and in the other case, to predict swine movements within a regional program to improve the control of infectious diseases in the US. 37 …”

Section: Discussionmentioning

confidence: 99%

Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency

Pyenson

Alston

Gomberg³

et al. 2019

JHEOR

View full text Add to dashboard Cite

Background: Exocrine pancreatic insufficiency (EPI) is a serious condition characterized by a lack of functional exocrine pancreatic enzymes and the resultant inability to properly digest nutrients. EPI can be caused by a variety of disorders, including chronic pancreatitis, pancreatic cancer, and celiac disease. EPI remains underdiagnosed because of the nonspecific nature of clinical symptoms, lack of an ideal diagnostic test, and the inability to easily identify affected patients using administrative claims data. Objectives: To develop a machine learning model that identifies patients in a commercial medical claims database who likely have EPI but are undiagnosed. Methods: A machine learning algorithm was developed in Scikit-learn, a Python module. The study population, selected from the 2014 Truven MarketScan® Commercial Claims Database, consisted of patients with EPI-prone conditions. Patients were labeled with 290 condition category flags and split into actual positive EPI cases, actual negative EPI cases, and unlabeled cases. The study population was then randomly divided into a training subset and a testing subset. The training subset was used to determine the performance metrics of 27 models and to select the highest performing model, and the testing subset was used to evaluate performance of the best machine learning model. Results: The study population consisted of 2088 actual positive EPI cases, 1077 actual negative EPI cases, and 437 530 unlabeled cases. In the best performing model, the precision, recall, and accuracy were 0.91, 0.80, and 0.86, respectively. The best-performing model estimated that the number of patients likely to have EPI was about 12 times the number of patients directly identified as EPI-positive through a claims analysis in the study population. The most important features in assigning EPI probability were the presence or absence of diagnosis codes related to pancreatic and digestive conditions. Conclusions: Machine learning techniques demonstrated high predictive power in identifying patients with EPI and could facilitate an enhanced understanding of its etiology and help to identify patients for possible diagnosis and treatment.

show abstract

“…The potential application where this vision-based learning can be implemented is radiology, ophthalmology, pathology and dermatology (Kulkarni et al, 2019). The AI is employed in the diagnosis of melanoma (Fuller et al, 2016), dementia (Bhagyashree et al, 2017), diabetic retinopathy (Gargeya and Leng, 2017), tuberculosis (Lakhani and Sundaram, 2017), and glaucoma (Kim et al, 2017).…”

Section: Diagnosismentioning

confidence: 99%

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

Elavarasan¹,

Pugazhendhi

2020

Science of The Total Environment

242

206

View full text Add to dashboard Cite

The technology shift occurred in society during the COVID-19 pandemic and its influence. • Technology-based strategies in controlling the pandemic and to support the society. • Key technologies include Artificial Intelligence and Machine learning, supply chain, communication and surveillance. • Potential roles of IoT, Drone &Robotic technologies and mobile applications. • Influence of existing technologies, challenges and future impacts on society.

show abstract

Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India

Cited by 43 publications

References 22 publications

Technology and Dementia: The Future is Now

Technology and Dementia: The Future is Now

Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency

Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic

Contact Info

Product

Resources

About