Introduction to special issue on ‘Recent computing paradigms, network protocols, and applications’

This special issue of Innovations in Systems and Software Engineering: A NASA Journal is devoted to selected contributions from the 3rd International Conference on Advanced Computing, Networking and Informatics (ICACNI-2015), organized by School of Computer Engineering, KIIT University, Odisha, India, during 23–25 June, 2015. The conference commenced with a keynote by Prof. Nikhil R. Pal (Fellow IEEE, Vice President for Publications IEEE Computational Intelligence Society (2015–2016), Indian Statistical Institute, Kolkata, India) on ‘A Fuzzy Rule-Based Approach to Single Frame Super Resolution’. Apart from three regular tracks on advanced computing, networking, and informatics, the conference hosted three invited special sessions. While a total of 558 articles across different tracks of the conference were received, 132 articles are finally selected for presentation and publication by Smart Innovation, Systems and Technologies series of Springer as Volume 43 and 44. The conference showcased a technical talk by Prof. Nabendu Chaki (Senior Member IEEE, Calcutta University, India) on ‘Evolution from Web-based Applications to Cloud Services: A Case Study with Remote Healthcare’. The conference identified some wonderful works and has given away eight awards in different categories

An agent-based cognitive approach for healthcare process management

Proceedings of the IEEE International Conference on Cognitive Informatics, 2009, p. 441-447Healthcare organizations are facing the challenge of delivering high-quality services through effective process management. There have been frequent changes of clinical processes and increased interactions between different functional units. To facilitate the dynamic and interactive processes in healthcare organizations, an agent-based cognitive approach is presented in this study. The emphasis is placed on dynamic clinical and administrative process management, and knowledge building as the foundation for process management. The treatment of primary open angle glaucoma is used as an example to demonstrate the effectiveness of approach for healthcare process management. © 2009 IEEE.published_or_final_versio

Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect

Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.Comment: 10 pages, IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015

Healthcare Process Support: Achievements, Challenges, Current Research

Healthcare organizations are facing the challenge of delivering high-quality services to their patients at affordable costs. To tackle this challenge, the Medical Informatics community targets at formalisms for developing decision-support systems (DSSs) based on clinical guidelines. At the same time, business process management (BPM) enables IT support for healthcare processes, e.g., based on workflow technology. By integrating aspects from these two fields, promising perspectives for achieving better healthcare process support arise. The perspectives and limitations of IT support for healthcare processes provided the focus of three Workshops on Process-oriented Information Systems (ProHealth). These were held in conjunction with the International Conference on Business Process Management in 2007-2009. The ProHealth workshops provided a forum wherein challenges, paradigms, and tools for optimized process support in healthcare were debated. Following the success of these workshops, this special issue on process support in healthcare provides extended papers by research groups who contributed multiple times to the ProHealth workshop series. These works address issues pertaining to healthcare process modeling, process-aware healthcare information system, workflow management in healthcare, IT support for guideline implementation and medical decision support, flexibility in healthcare processes, process interoperability in healthcare and healthcare standards, clinical semantics of healthcare processes, healthcare process patterns, best practices for designing healthcare processes, and healthcare process validation, verification, and evaluation

Document Understanding for Healthcare Referrals

Reliance on scanned documents and fax communication for healthcare referrals leads to high administrative costs and errors that may affect patient care. In this work we propose a hybrid model leveraging LayoutLMv3 along with domain-specific rules to identify key patient, physician, and exam-related entities in faxed referral documents. We explore some of the challenges in applying a document understanding model to referrals, which have formats varying by medical practice, and evaluate model performance using MUC-5 metrics to obtain appropriate metrics for the practical use case. Our analysis shows the addition of domain-specific rules to the transformer model yields greatly increased precision and F1 scores, suggesting a hybrid model trained on a curated dataset can increase efficiency in referral management.Comment: Accepted and presented at the 11th IEEE International Conference on Healthcare Informatics (ICHI) 2023 - Industry Trac

Building predictive models of healthcare costs with open healthcare data

Due to rapidly rising healthcare costs worldwide, there is significant interest in controlling them. An important aspect concerns price transparency, as preliminary efforts have demonstrated that patients will shop for lower costs, driving efficiency. This requires the data to be made available, and models that can predict healthcare costs for a wide range of patient demographics and conditions. We present an approach to this problem by developing a predictive model using machine-learning techniques. We analyzed de-identified patient data from New York State SPARCS (statewide planning and research cooperative system), consisting of 2.3 million records in 2016. We built models to predict costs from patient diagnoses and demographics. We investigated two model classes consisting of sparse regression and decision trees. We obtained the best performance by using a decision tree with depth 10. We obtained an R-square value of 0.76 which is better than the values reported in the literature for similar problems.Comment: 2020 IEEE International Conference on Healthcare Informatics (ICHI