Automatic detection of linguistic indicators as a means of early detection of Alzheimer's disease and of related dementias: A computational linguistics analysis

Rentoumi, Vassiliki; Paliouras, George; Danasi, Eva; Arfani, Dimitra; Fragkopoulou, Katerina; Varlokosta, Spyridoula; Papadatos, Spyros

doi:10.1109/coginfocom.2017.8268212

Cited by 29 publications

(17 citation statements)

References 12 publications

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“…Our study investigates and combines two types of connected speech measures, namely linguistic features and n-gram vocabulary spaces, as an attempt to optimize the automatic identification of different dementia etiologies. Linguistic features have widely been employed to reveal dementia stages in previous studies [23,30,38,[53][54][55][56]; nevertheless, our study focused on one linguistic perspective that is lexicosyntactic features as being recommended for further investigations by other researchers for its established association with early cognitive decline [25,26]. N-gram vocabulary spaces, on the other hand, have recently attracted several NLP studies on dementia detection [24,38,39,57].…”

Section: ) Feature Engineeringmentioning

confidence: 99%

“…More recently, studies using automatic or semiautomatic methods for language and speech analysis have asserted that linguistic analysis can characterize early AD and MCI [22][23][24]. Specifically, as stated by Ball et al [25] and asserted by Rentoumim et al [26], lexical and syntactic (i.e., lexicosyntactic) processing in people with language disorders has revealed promising outcomes, highlighting the necessity of additional investigations for more effective lexicosyntactic biomarkers and techniques.…”

Section: Introduction and Motiviationmentioning

confidence: 99%

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Predicting Prodromal Dementia Using Linguistic Patterns and Deficits

Alkenani

et al. 2020

IEEE Access

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Language deficiency is evident in the onset of several neurodegenerative disorders yet has barely been investigated when first occurs on the continuum of cognitive impairment for the purpose of early diagnoses. Alzheimer's disease (AD) is a neurodegenerative pathology that develops years prior to clinical manifestations and typically preceded by prodromal stages such as Mild Cognitive Impairment (MCI). Currently, the manual diagnostic procedures of both types are time consuming, following certain clinical criteria and neuropsychological examinations. Our study aims to establish state-of-the-art performance in the automatic identification of different dementia etiologies, including AD, MCI, and Possible AD (PoAD), and to determine whether patients with initial cognitive declines exhibit language deficits through the analysis of language samples deduced with the cookie theft picture description task. Data were derived from the cookie theft picture corpus of DementiaBank, from which all language samples of the identified etiologies were used, with a random subsampling technique that handles the skewness of the classes. Several original lexical and syntactic (i.e., lexicosyntactic) features were introduced and used alongside previously established lexicosyntactics to train machine learning (ML) classifiers against these etiologies. Further, a statistical analysis was conducted to uncover the deficiency across these etiologies. Our models resulted in benchmarks for differentiating all the identified classes with accuracies ranging between 95 to 98% and corresponding F1 values falling between 94 and 98%. The statistical analysis of our lexicosyntactic biomarkers shows that linguistic deviations are associated with prodromal as well as advanced neurodegenerative pathologies, being greatly impacted as cognitive decline increases and suggesting that language biomarkers may aid the early diagnosis of these pathologies.

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Section: ) Feature Engineeringmentioning

confidence: 99%

Section: Introduction and Motiviationmentioning

confidence: 99%

Predicting Prodromal Dementia Using Linguistic Patterns and Deficits

Alkenani

et al. 2020

IEEE Access

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“…Automatic speech-based tools can also use lexical and conversation analysisinspired features derived from transcripts of recorded data [8] in conversations led by neurologists or intelligent virtual agents. As another example, the CogAware tool [9] provides textual analysis for transcripts of individuals describing the "cookie theft" picture [10] in order to automatically detect whether they are originated from a patient with dementia or a cognitively normal individual.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Presence and Severity of Dementia by Applying Interpretable Machine Learning Techniques on Structured Clinical Records

Vyas

Aisopos

Vidal

et al. 2021

Preprint

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Background: Dementia occurs through declining of cognitive abilities, and its early detection stands essential for effective preventive measures. However, mainstream diagnostic tests and screening tools, such as Cambridge Cognition Examination (CAMCOG) and Mini-Mental State Examination (MMSE), often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ electronic medical records, they represent promising options to identify the presence and severity of dementia more precisely. Methods: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation method where a dementia expert is in the loop to ensure meaningful curation decisions. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the detection of dementia. Results: Our experiment results prove that clinical record features (such as age or comorbidities), along with baseline arithmetic or memory tests, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.85 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various clinical features and cognitive tests from the past episodes of the elderly population. Moreover, a set of rules represent building blocks in the efficiently patient classification. Relevant clinical and screening test features (e.g., simple arithmetic or animal fluency task) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. They identify not only meaningful features but also justifications of classifications. As a result, the predictive power of machine learning models over curated clinical data is demonstrated, paving the way for accurate diagnosis of dementia.

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“…In recent years, an almost exponential increase has been documented in the number of investigations aimed at shedding light on speech analysis as a non-invasive biomarker of AD [15]. Since the first lines appeared, 78% have been based on the use of conventional parameters; mainly duration of deaf or sound segments, pitch, amplitude and periodicity, as well as others obtained from analysis of the frequency and cepstral domains [16][17][18]. Also, concepts such as speech quality or Emotional Temperature (ET) have been defined.…”

Section: Introductionmentioning

confidence: 99%

Using a Human Interviewer or an Automatic Interviewer in the Evaluation of Patients with AD from Speech

et al. 2021

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Currently, there are more and more frequent studies focused on the evaluation of Alzheimer’s disease (AD) from the automatic analysis of the speech of patients, in order to detect the presence of the disease in an individual or for the evolutionary control of the disease. However, studies focused on analyzing the effect of the methodology used to generate the spontaneous speech of the speaker who undergoes this type of analysis are rare. The objective of this work is to study two different strategies to facilitate the generation of the spontaneous speech of a speaker for further analysis: the use of a human interviewer that promotes the generation of speech through an interview and the use of an automatic system (an automatic interviewer) that invites the speaker to describe certain visual stimuli. In this study, a database called Cross-Sectional Alzheimer Prognosis R2019 has been created, consisting of speech samples from speakers recorded using both methodologies. The speech recordings have been studied through a feature extraction based on five basic temporal measurements. This study demonstrates the discriminatory capacity between the speakers with AD and the control subjects independent of the strategy used in the generation of spontaneous speech. These results are promising and can serve as a basis for knowing the effectiveness and extension of automated interview processes, especially in telemedicine and telecare scenarios.

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Automatic detection of linguistic indicators as a means of early detection of Alzheimer's disease and of related dementias: A computational linguistics analysis

Cited by 29 publications

References 12 publications

Predicting Prodromal Dementia Using Linguistic Patterns and Deficits

Predicting Prodromal Dementia Using Linguistic Patterns and Deficits

Identifying the Presence and Severity of Dementia by Applying Interpretable Machine Learning Techniques on Structured Clinical Records

Using a Human Interviewer or an Automatic Interviewer in the Evaluation of Patients with AD from Speech

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