Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer’s Disease

Guerrero, José M.; Martínez‐Tomás, Rafael; Rincón, M.; Peraita, Herminia

doi:10.3414/me14-01-0071

Cited by 19 publications

(16 citation statements)

References 21 publications

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“…Our results show that we have a possible indicator of early AD, due to areas of 0.89 and 0.7 obtained, which is considered acceptable for an ROC curve. The results are also comparable to those obtained by Guerrero et al ( 2016 ) with 91 and 94% precision for detecting AD, while our study obtained a precision of 88%. These studies can be compared with early indicators or screening of the likely onset of AD.…”

Section: Conclusion and Further Research Worksupporting

confidence: 92%

The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment

Tapia

Martínez‐Tomás

Gómez

et al. 2016

Front. Comput. Neurosci.

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The present study aims to identify early cognitive impairment through the efficient use of therapies that can improve the quality of daily life and prevent disease progress. We propose a methodology based on the hypothesis that the dissociation between oral semantic expression and the physical expressions, facial gestures, or emotions transmitted in a person's tone of voice is a possible indicator of cognitive impairment. Experiments were carried out with phrases, analyzing the semantics of the message, and the tone of the voice of patients through unstructured interviews in healthy people and patients at an early Alzheimer's stage. The results show that the dissociation in cognitive impairment was an effective indicator, arising from patterns of inconsistency between the analyzed elements. Although the results of our study are encouraging, we believe that further studies are necessary to confirm that this dissociation is a probable indicator of cognitive impairment.

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Section: Conclusion and Further Research Worksupporting

confidence: 92%

The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment

Tapia

Martínez‐Tomás

Gómez

et al. 2016

Front. Comput. Neurosci.

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show abstract

“…From this, we concluded that for the linguistic features considered, there is no need to separate participants' descriptions into living and non-living categories. Guerrero et al (2016) reported an accuracy of 91% (see Table 1) and an AUC of 0.9636. They used a Bayesian Network fed with manuallyextracted attributes and incorporated participants' socio-demographic information as a priori deterministic inputs.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…The cohort used by Guerrero et al (2016) in their study includes a total of 69 participants (30 controls and 39 AD patients previously diagnosed by neurologists) aged between 55 and 95 years old. For each participant, Peraita and Grasso recorded free oral descriptions of six common objects (referred in their work as "semantic categories"): dog, apple, pine (living things), car, trousers and chair (non-living things).…”

Section: Corpusmentioning

confidence: 99%

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Detection of Alzheimer's disease based on automatic analysis of common objects descriptions

Hernández-Domínguez¹,

Garcia-Cano²,

Ratté³

et al. 2016

Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning

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Many studies have been made on the language alterations that take place over the course of Alzheimer's disease (AD). As a consequence, it is now admitted that it is possible to discriminate between healthy and ailing patients solely based on the analysis of language production. Most of these studies, however, were made on very small samples-30 participants per study, on an average-, or involved a great deal of manual work in their analysis. In this paper, we present an automatic analysis of transcripts of elderly participants describing six common objects. We used partof-speech and lexical richness as linguistic features to train an SVM classifier to automatically discriminate between healthy and AD patients in the early and moderate stages. The participants, in the corpus used for this study, were 63 Spanish adults over 55 years old (29 controls and 34 AD patients). With an accuracy of 88%, our experimental results compare favorably to those relying on the manual extraction of attributes, providing evidence that the need for manual analysis can be overcome without sacrificing in performance.

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“…Because its procedures of reasoning and expression can be easily understood and accepted by both therapists and patients, unlike the black box of other traditional algorithms, it is also able to present uncertainties and causalities, which are both important in the medical domain [12]. Several studies have examined the performance of BN by developing mathematical models for diagnosing different diseases, including breast cancer [13], lung cancer [14], and Alzheimer disease [12,15]. These experimental results showed that all models of these disease diagnoses provided accuracy of at least 80%, and their network structures could be easily understood.…”

Section: Introductionmentioning

confidence: 99%

Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study

Sheng¹,

Huang²,

Wang³

et al. 2019

JMIR Med Inform

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Background Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective The aim of this study was to propose a Bayesian network (BN)–based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals. Methods The proposed model’s structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78). Conclusions The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs.

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Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer’s Disease

Cited by 19 publications

References 21 publications

The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment

The Dissociation between Polarity, Semantic Orientation, and Emotional Tone as an Early Indicator of Cognitive Impairment

Detection of Alzheimer's disease based on automatic analysis of common objects descriptions

Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study

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