Learning Predictive Linguistic Features for Alzheimer’s Disease and related Dementias using Verbal Utterances

Orimaye, Sylvester Olubolu; Wong, Jojo Sze-Meng; Golden, Karen Jennifer

doi:10.3115/v1/w14-3210

Cited by 85 publications

(84 citation statements)

References 22 publications

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“…In our case, we assume that repeated words would be less significant given the small data sample while the absolute count of predicates in a discourse (not at the sentence level) could be more representative of the groups instead of their average per sentence. We will compare the ML predictive performance of our significant features to [3, 25], and also [37] which is a precursor to this study.…”

Section: Resultsmentioning

confidence: 99%

“…The final ML models were built using a reliable learning algorithm, which we will discuss later. We compared our technique with [3, 25, 37] as benchmark papers.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we introduce the n -gram model [35], which captures the pattern of the sequence of words in the language of the disease and healthy elderly groups. We emphasise that the machine learning approach has proven to be effective in clinical diagnostics [3, 12, 36, 37]. Our predictive models achieved a better Area Under the ROC Curve (AUC) in distinguishing the probable AD group from the healthy elderly group.…”

Section: Introductionmentioning

confidence: 96%

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Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

et al. 2017

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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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1456-0) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

“…The final ML models were built using a reliable learning algorithm, which we will discuss later. We compared our technique with [3, 25, 37] as benchmark papers.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

et al. 2017

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“…More recently, researchers are beginning to utilize machine learning to differentiate between older adults with neurodegenerative disorders using extracted narrative speech (Fraser et al, 2014; Orimaye, Wong, and Golden, 2014). Our group has also utilized machine learning techniques and sensor technology to assess everyday functional ability and cognitive health of older adults (Cook & Schmitter-Edgecombe, 2009; Dawadi, Cook, Schmitter-Edgecombe, & Parsey, 2013; Rashidi, Cook, Holder, & Schmitter-Edgecombe, 2011).…”

Section: Discussionmentioning

confidence: 99%

Neuropsychological test selection for cognitive impairment classification: A machine learning approach

Weakley

Williams

Schmitter‐Edgecombe

et al. 2015

Journal of Clinical and Experimental Neuropsychology

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Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis.

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“…However, in these earlier studies, primitives consisted of properties that were syntactic, lexical, or semantic in nature, whereas the primitives for the current work consist of properties that are phonological in nature. Beukelman et al (2011), Duffy (2013, Green et al (2013), and Orimaye et al (2014) have established that pronunciation varies systematically within categories of speech impairment. (Silbergleit et al, 1997;Carrow et al, 1974) have shown that ALS speech shows deviant characteristics.…”

Section: Related Workmentioning

confidence: 99%

Characterization of Divergence in Impaired Speech of ALS Patients

Bhatia¹,

Dorr²,

Hollingshead³

et al. 2017

BioNLP 2017

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Approximately 80% to 95% of patients with Amyotrophic Lateral Sclerosis (ALS) eventually develop speech impairments (Beukelman et al., 2011), such as defective articulation, slow laborious speech and hypernasality (Duffy, 2013). The relationship between impaired speech and asymptomatic speech may be seen as a divergence from a baseline. This relationship can be characterized in terms of measurable combinations of phonological characteristics that are indicative of the degree to which the two diverge. We demonstrate that divergence measurements based on phonological characteristics of speech correlate with physiological assessments of ALS. Speech-based assessments offer benefits over commonly-used physiological assessments in that they are inexpensive, non-intrusive, and do not require trained clinical personnel for administering and interpreting the results.

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Learning Predictive Linguistic Features for Alzheimer’s Disease and related Dementias using Verbal Utterances

Cited by 85 publications

References 22 publications

Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

Neuropsychological test selection for cognitive impairment classification: A machine learning approach

Characterization of Divergence in Impaired Speech of ALS Patients

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