Cultural adaptation of Alzheimer's disease assessment scale-cognitive subscale for use in India and validation of the Tamil version for South Indian population.

OBJECTIVE: Currently no standardized tools are available in the Indian languages to assess changes in cognition. Our objectives are to culturally adapt the Alzheimer's disease Assessment Scale-Cognitive Subscale (ADAS-Cog) for use in India and to validate the Tamil version in an urban Tamil-speaking older adult population. METHODS: Two panels of key stakeholders and a series of qualitative interviews informed the cultural and linguistic adaptation of the ADAS-Cog-Tamil. Issues related to levels of literacy were considered during the adaptation. Validation of the ADAS-Cog-Tamil was completed with 107 participants - 54 cases with a confirmed diagnosis of mild-moderate dementia, and 53 age, gender and education matched controls. Concurrent validity was examined with the Vellore Screening Instrument for Dementia (VSID) in Tamil. Internal consistency using Cronbach's alpha, sensitivity and specificity data using the Area under the Receiver Operating Characteristics (AUROC) curve values were computed. Inter-rater reliability was established in a subsample. RESULTS: The ADAS-Cog-Tamil shows good internal consistency (α = 0.91), inter-rater reliability and concurrent validity (with VSID-Patient version: r = -0.84 and with VSID-Caregiver version: r = -0.79). A cut-off score of 13, has a specificity of 89% and sensitivity of 90% for the diagnosis of dementia. CONCLUSION: ADAS-Cog-Tamil, derived from a rigorous, replicable linguistic and cultural adaptation process involving service users and experts, shows good psychometric properties despite the limitations of the study. It shows potential for use in clinical settings with urban Tamil speaking populations. The English version of the tool derived from the cultural adaptation process could be used for further linguistic adaptation across South Asia.

A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records.

Electronic health records (EHR) are a major source of information in biomedical informatics. Yet, missing values are prominent characteristics of EHR. Prediction on dataset with missing values results in inaccurate inferences. Nearest neighbour imputation based on lazy learning approach is a proven technique for missing data imputation and is recognized as one among the top ten data mining algorithms due to its simplicity and understandability. But its performance is deteriorated due to the curse of dimensionality as unimportant features are likely to dominate. We address this problem by proposing a novel approach for feature weighting based on a hybrid of metaheuristic whale optimization algorithm (WOA) and local search late acceptance hill climbing algorithm (LAHCA) on nearest neighbour imputation method. Our proposed approach Metaheuristic and Local Search based Feature Weighted Nearest Neighbour Imputation (kNN+LAHCAWOA) also learns different k values for different test points. Our approach is tested on benchmark EHR datasets with three proven classifiers Support Vector Machines(SVM), Random forest(RF) and Deep neural networks(DNN). The results prove that kNN+LAHCAWOA is an effective imputation strategy and aids in improving the classification performance when compared with its competitor methods.

Missing data imputation on biomedical data using deeply learned clustering and L2 regularized regression based on symmetric uncertainty.

Big data era in healthcare led to the generation of high dimensional datasets like genomic datasets, electronic health records etc. One among the critical issues to be addressed in such datasets is handling incomplete data that may yield misleading results if not handled properly. Imputation is considered to be an effective way when the missing data rate is high. While imputation accuracy and classification accuracy are the two important metrics generally considered by most of the imputation techniques, high dimensional datasets such as genomic datasets motivated the need for imputation techniques that are also computationally efficient and preserves the structure of the dataset. This paper proposes a novel approach to missing data imputation in biomedical datasets using an ensemble of deeply learned clustering and L2 regularized regression based on symmetric uncertainty. The experiments are conducted with different proportion of missing data on both genomic and non-genomic biomedical datasets for different types of missingness pattern. Our proposed approach is compared with seven proven baseline imputation methods and two recently proposed imputation approaches. The results show that the proposed approach outperforms the other approaches considered in our experimentation in terms of imputation accuracy and computational efficiency despite preserving the structure of the dataset. Thus, the overall classification accuracy of the biomedical classification tasks is also improved when our proposed missing data imputation technique is used.

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification.

Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

Patient and Public Involvement for Dementia Research in Low- and Middle-Income Countries: Developing Capacity and Capability in South Asia.

Background: Patient and public involvement (PPI) is an active partnership between the public and researchers in the research process. In dementia research, PPI ensures that the perspectives of the person with "lived experience" of dementia are considered. To date, in many lower- and middle-income countries (LMIC), where dementia research is still developing, PPI is not well-known nor regularly undertaken. Thus, here, we describe PPI activities undertaken in seven research sites across South Asia as exemplars of introducing PPI into dementia research for the first time. Objective: Through a range of PPI exemplar activities, our objectives were to: (1) inform the feasibility of a dementia-related study; and (2) develop capacity and capability for PPI for dementia research in South Asia. Methods: Our approach had two parts. Part 1 involved co-developing new PPI groups at seven clinical research sites in India, Pakistan and Bangladesh to undertake different PPI activities. Mapping onto different "rings" of the Wellcome Trust's "Public Engagement Onion" model. The PPI activities included planning for public engagement events, consultation on the study protocol and conduct, the adaptation of a study screening checklist, development and delivery of dementia training for professionals, and a dementia training programme for public contributors. Part 2 involved an online survey with local researchers to gain insight on their experience of applying PPI in dementia research. Results: Overall, capacity and capability to include PPI in dementia research was significantly enhanced across the sites. Researchers reported that engaging in PPI activities had enhanced their understanding of dementia research and increased the meaningfulness of the work. Moreover, each site reported their own PPI activity-related outcomes, including: (1) changes in attitudes and behavior to dementia and research involvement; (2) best methods to inform participants about the dementia study; (3) increased opportunities to share knowledge and study outcomes; and (4) adaptations to the study protocol through co-production. Conclusions: Introducing PPI for dementia research in LMIC settings, using a range of activity types is important for meaningful and impactful dementia research. To our knowledge, this is the first example of PPI for dementia research in South Asia.