“Incremental Learning (IL)” is the niche area of “Machine Learning.” It is of utmost essential to keep learning incremental for ever-increasing data from all domains for effectual decisions, predications and solving problems. This can be achieved effectually by applying “Incremental Clustering” methods on real-time data sources. IL can be achieved by “Incremental Clustering” easily as well as effectively. To achieve worldwide data analysis related to the data and to achieve broader perspectives, it is essential to deploy “Incremental Clustering” algorithms on distributed platforms, which will enable them to accept data from varied sources; analyze it and produce distributed worldwide solutions. This paper hence focuses on understanding the current status of “Distributed Incremental Clustering Algorithms (DICA),” its scope, limitations and other details so as to formulate better than the best algorithm in future. To enhance the analysis further Word-Clouds of impactful papers were explored and added in this paper, along with the details about platforms used to implement DICA by various upcoming researchers, readers and authors.

Purpose: Patient experience is fundamental to Patient-Centered Care (PCC). Although prior bibliometric research studies have focused on various aspects of PCC, a comprehensive analysis of PREM articles is required to understand its impact on the clinical practices. This study aims to analyze the top 100 most-cited PREM articles to examine the critical studies and related trends. Methods: The 100 most cited articles on PREM were gathered from the Web of Science using a combination keyword search approach. The following information was extracted: study design, sample size, topic, number of citations, authorship, country, year of publication, journal title, and dimensions included in these PREM instruments. The VOSviewer software was used to generate graphical bibliometric networks. Results: The citation count of the top 100 PREM articles varied from 20 to 775 citations. 21 articles had received a minimum of 100 citations. All the articles were in English, and out of these 45% were from the USA. The cross-sectional study (69%) was the most common study design, and the impact of treatment (44%) was the most frequent topic. The common PREM instruments used were customized PREM questionnaires (16%) and HCAHPS (10%). Conclusion: This bibliometric research showed that the area of PREM is far from being saturated. The authors have attempted to provide an overview of global PREM research. Future research should focus on studies from underdeveloped and developing countries to develop condition–specific PREM tools. Longitudinal researches among special populations and studies in day-care and outpatient settings are recommended in future.

Quantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation, the machine learning models are continuously evolving. Hence, the new challenge is to improve the existing complex and critical machine learning training models. Therefore, the healthcare sector is shifting from a classical to a quantum domain to sustain patient-oriented attention to healthcare patrons. This paper presents a hybrid classical-quantum approach for training the unsupervised data models. In order to achieve good performance and optimization of the machine learning algorithms, a quantum k-means (QK-means) clustering problem was deployed on the IBM quantum simulators, i.e.,the IBM QASM simulator. In the first place, the approach was theoretically studied and then implemented to analyze the experimental results. The approach was further tested using small synthetics and cardiovascular datasets on a qsam simulator to obtain the clustering solution. The future direction connecting the dots is the incremental k-means algorithm with the quantum platform, which would open hitherto unimaginable technological doors.

Learning paradigm is associated with the study of how computers and natural systems such as humans learn in the presence of both labelled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled or in the supervised paradigm (e.g., classification, regression) where all the data are labelled. 'Incremental learning' is an approach to deal with classification task or clustering when datasets are too large and when new information can arrive at any time, dynamically. We propose a new incremental clustering algorithm based on closeness, an efficient and scalable approach which updates cluster and learn new information effectually. Confusion matrix is implemented to validate the results given by proposed system as compared to published results. The proposed systems achieves knowledge augmentation, incremental learning via incremental clustering without compromising quality of data and saving computing time and complexity.