Biomedical Health Informatics Research Articles

Knocking on Doors - How Healthcare Workers Informally Develop Expertise in Biomedical and Health Informatics.

With digital systems permeating the healthcare sector, the healthcare workforce (clinical and administrative) need insight in biomedical health informatics (BMHI) to some degree. This study shows how novices in BMHI had to knock hard on several doors to find and become part of a community of practice to gain such expertise within BMHI. While it may be generally understood that gaining access to expertise is important, our findings suggest that more attention is needed to how such access is gained. The study exemplifies that the needed skills and competencies are difficult to identify in the individual projects and are highly situated - not least because it requires access to various experts in communities of practices. Therefore, there is a continued need to reframe the necessary education and training. Knowing when to knock on doors, which doors to knock on, and keeping doors open is central to becoming - and keep on being - a part of a community of practice centring on health information technology and BMHI.

Methodical Systematic Review of Abstractive Summarization and Natural Language Processing Models for Biomedical Health Informatics: Approaches, Metrics and Challenges

Text summarization tasks are primarily very useful for decision support systems and provide a source for useful data for training of bots as they can reduce and retain the useful information from the large corpus. This review article is for studying the literature that already exists in context of abstractive summarization and application of NLP language models in biomedical and associated healthcare applications. In past decade with trends like bigdata, IOT, enormous amount of data is getting processed in all structured, unstructured and semi structured formats. This review provides a comprehensive literature survey in research trends for abstractive summarization, foundations of machine translation and evolution of language models. This review identifies the potential of language model to provide a possible methodology for improving the performance and accuracy of various tasks in summarization. Deep neural network-based language models have now been the widely accepted state of art for various abstractive summarization and there exists an enormous scope to improvise and tune the language models for domain specific use case. This study shows current systems lack in faithfulness to original content and control of degree of hallucination. This review also details on the evaluation criteria and need for automated metrics and attempts to provide guideline for evaluation for abstractive summarization for health informatics.

Transformer-Based Network for Accurate Classification of Lung Auscultation Sounds.

Respiratory diseases are a major cause of death worldwide, affecting a significant proportion of the population with lung function abnormalities that can lead to respiratory illnesses. Early detection and prevention are critical to effective management of these disorders. Deep learning algorithms offer a promising approach for analyzing complex medical data and aiding in early disease detection. While transformer-based models for sequence classification have proven effective for tasks like sentiment analysis, topic classification, etc., their potential for respiratory disease classification remains largely unexplored. This paper proposes a classifier utilizing the transformer-encoder block, which can capture complex patterns and dependencies in medical data. The proposed model is trained and evaluated on a large dataset from the International Conference on Biomedical Health Informatics 2017, achieving state-of-the-art results with a mean sensitivity of 70.53%, mean specificity of 84.10%, mean average score of 77.32%, and mean harmonic score of 76.10%. These results demonstrate the model's effectiveness in diagnosing respiratory diseases while taking up minimal computational resources.

Search for Editor-in-Chief IEEE Journal of Biomedical Health Informatics

Search for Editor-in-Chief IEEE Journal of Biomedical Health Informatics

Secure Collaborative Augmented Reality Framework for Biomedical Informatics.

Augmented reality is currently of interest in biomedical health informatics. At the same time, several challenges have appeared, in particular with the rapid progress of smart sensor technologies, and medical artificial intelligence. This yields the necessity of new needs in biomedical health informatics. Collaborative learning and privacy are just some of the challenges of augmented reality technology in biomedical health informatics. This paper introduces a novel secure collaborative augmented reality framework for biomedical health informatics-based applications. Distributed deep learning is performed across a multi-agent system platform. The privacy strategy is then developed for ensuring better communications of the different intelligent agents in the system. In this research work, a system of multiple agents is created for the simulation of the collective behaviours of the smart components of biomedical health informatics. Augmented reality is also incorporated for better visualization of medical patterns. A novel privacy strategy based on blockchain is investigated for ensuring the confidentiality of the learning process. Experiments are conducted on real use cases of the biomedical segmentation process. Our strong experimental analysis reveals the strength of the proposed framework when directly compared to state-of-the-art biomedical health informatics solutions.

An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images

According to a 2020 WHO report, cancer is one of the main causes of deaths worldwide. Among these deaths, lung and colon cancer collectively responsible for nearly 2.735 million deaths.So, detection and classification of lung and colon cancer is one of the utmost priority research areas in the field of biomedical health informatics. In this article, comparative analysis of two feature extraction methodologies has been presented for lung and colon cancer classification. In one approach, six handcrafted features extraction techniques based on colour, texture, shape and structure are presented. Gradient Boosting (GB), SVM-RBF, Multilayer Perceptron (MLP) and Random Forest (RF) classifiers with handcrafted features are trained and tested for lung and colon cancer classification. In another approach, using the notion of transfer learning, seven deep learning frameworks for deep feature extraction from lung and colon cancer histopathological images are presented. The extracted deep features (as input attributes) are applied into conventional GB, SVM-RBF, MLP and RF classifiers for lung and colon cancer classification. However, in contrast to handcrafted features a significant improvement in classifiers performance is observed with features extracted by deep CNN networks. It has been found that the proposed technique obtained excellent results in terms of accuracy, precision, recall, F1 score and ROC-AUC. The RF classifier with DenseNet-121 extracted deep features can identify the lung and colon cancer tissue with an accuracy and recall of 98.60%, precision of 98.63%, F1 score of 0.985 and ROC-AUC of 01.

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.

In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z-score normalization. The deep learning network architecture consisted of two stages; convolutional neural networks and bidirectional long short-term memory units. The training of the model was evaluated based on a k-fold cross-validation scheme of tenfolds using several performance evaluation metrics including Cohen’s kappa, accuracy, sensitivity, specificity, precision, and F1-score. The developed algorithm achieved the highest average accuracy of 99.62% with a precision of 98.85% in classifying patients based on the pulmonary disease types using CNN + BDLSTM. Furthermore, a total agreement of 98.26% was obtained between the predictions and original classes within the training scheme. This study paves the way towards implementing deep learning models in clinical settings to assist clinicians in decision making related to the recognition of pulmonary diseases.Supplementary InformationThe online version supplementary material available at 10.1007/s12652-021-03184-y.

Multi-Time-Scale Features for Accurate Respiratory Sound Classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation.

Automatic acoustic identification of respiratory diseases

Several disease affecting the human respiratory system, such as asthma, pneumonia, etc. are associated with distinctive sounds. Here, we propose a methodology towards their automatic identification by means of signal processing and pattern recognition algorithms. We designed a suitable feature set based on wavelet packet analysis characterizing data coming from diverse classes of respiratory sounds following the logic of the challenge organised within the International Conference on Biomedical Health Informatics in 2017. The patterns revealed by the feature extraction stage are modelled by hidden Markov models. Automatic identification is carried out via a directed acyclic graph (DAG) scheme limiting the problem space while based on decisions made by the available HMMs. Thorough experiments following a well-established protocol demonstrate the efficacy of the proposed solution. Indeed, such a DAG-based structure outperforms the current state of the art including deep networks based on convolutional kernels. Importantly, the presented solution offers a high level of explainability as one is able to investigate the effective DAG path and understand both correct and incorrect predictions.

Can AI Truly Transform Health Care?: A Recent IEEE Pulse on Stage Forum Offers Some Perspective.

As scholars have predicted and researchers have now shown, we are entering an age of global artificial intelligence (AI) convergence. Health care is just one area in which AI is gaining a foothold, as evidenced by two parallel conferences held in March 2018 in Las Vegas, Nevada. The first of these was the annual meeting of the Hospital Information and Management Systems Society, attended by nearly 45,000 participants; the second was a more focused, engineering-oriented conference on biomedical health informatics and biosensor networks, attended by about 450. Straddling those two meetings, IEEE Pulse held its second IEEE Pulse on Stage event, focusing (not surprisingly) on the use of AI in health care (Figure 1).

BHI IEEE International Conference on Biomedical Health Informatics 2018

BHI IEEE International Conference on Biomedical Health Informatics 2018

Solving Interoperability in Translational Health. Perspectives of Students from the International Partnership in Health Informatics Education (IPHIE) 2016 Master Class.

In the summer of 2016 an international group of biomedical and health informatics faculty and graduate students gathered for the 16th meeting of the International Partnership in Health Informatics Education (IPHIE) masterclass at the University of Utah campus in Salt Lake City, Utah. This international biomedical and health informatics workshop was created to share knowledge and explore issues in biomedical health informatics (BHI). The goal of this paper is to summarize the discussions of biomedical and health informatics graduate students who were asked to define interoperability, and make critical observations to gather insight on how to improve biomedical education. Students were assigned to one of four groups and asked to define interoperability and explore potential solutions to current problems of interoperability in health care. We summarize here the student reports on the importance and possible solutions to the "interoperability problem" in biomedical informatics. Reports are provided from each of the four groups of highly qualified graduate students from leading BHI programs in the US, Europe and Asia. International workshops such as IPHIE provide a unique opportunity for graduate student learning and knowledge sharing. BHI faculty are encouraged to incorporate into their curriculum opportunities to exercise and strengthen student critical thinking to prepare our students for solving health informatics problems in the future.

50th Anniversary International Medical Informatics Association (IMIA) History Working Group and Its Projects.

The IMIA History Working Group has as its first goal the editing of a volume of contributions from pioneers and leaders in the field of biomedical and health informatics (BMHI) to commemorate the 50th anniversary of IMIA's predecessor IFIP-TC4. This paper describes how the IMIA History WG evolved from an earlier Taskforce, and has focused on producing the edited book of original contributions. We describe its proposed outline of objectives for the personal stories, and national and regional society narratives, together with some comments on the evolution of Medinfo meeting contributions over the years, to provide a reference source for the early motivations of the scientific, clinical, educational, and professional changes that have influenced the historical course of our field.

Guest Editorial: MobiHealth 2014, IEEE HealthCom 2014, and IEEE BHI 2014

The papers in this special section were presented at three well-known conferences organized in 2014: EAI Mobihealth, IEEE HealthCom, and IEEE Biomedical and Health Informatics. EAI Mobihealth is an annually organized conference, which started in 2010, to address the demands of the rapidly evolving disciplines of wireless communications, mobile computing, and sensing technologies in healthcare. The IEEE-Healthcom is held every year since 1999 in different countries in Asia, Europe, and in America. It aims at bringing together interested parties working in the field of healthcare to exchange ideas, discuss innovative and emerging solutions, and develop collaborations. The IEEE Biomedical Health Informatics Conference started in 2013 and is organized every year providing the forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization, and analysis of medical data. The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients, security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.

Partnership to promote interprofessional education and practice for population and public health informatics: A case study

Team-based healthcare delivery models, which emphasize care coordination, patient engagement, and utilization of health information technology, are emerging. To achieve these models, expertise in interprofessional education, collaborative practice across professions, and informatics is essential. This case study from informatics programs in the Academic Health Center (AHC) at the University of Minnesota and the Office of Health Information Technology (OHIT) at the Minnesota Department of Health presents an academic–practice partnership, which focuses on both interprofessionalism and informatics. Outcomes include the Minnesota Framework for Interprofessional Biomedical Health Informatics, comprising collaborative curriculum development, teaching and research, practicums to promote competencies, service to advance biomedical health informatics, and collaborative environments to facilitate a learning health system. Details on these Framework categories are presented. Partnership success is due to interprofessional connections created with emphasis on informatics and to committed leadership across partners. A limitation of this collaboration is the need for formal agreements outlining resources and roles, which are vital for sustainability. This partnership addresses a recommendation on the future of interprofessionalism: that both education and practice sectors be attuned to each other’s expectations and evolving trends. Success strategies and lessons learned from collaborations, such as that of the AHC-OHIT that promote both interprofessionalism and informatics, need to be shared.