Biomedical Information Processing Research Articles

Artificial Intelligence in Healthcare- An Overview

Artificial intelligence (AI) has been developing fleetly in recent times in terms of software algorithms, tackle preparation, and operations in a vast number of areas. In this review, we epitomize the rearmost of operations of AI in biomedicine, including complaint diagnostics, living backing, biomedical information processing, and biomedical exploration. The end of this review is to keep track of new scientific accomplishments, to understand the vacuity of technologies, to appreciate the tremendous eventuality of AI in biomedicine, and to give experimenters in affiliated field’s alleviation. It can be asserted that, just like AI itself, the operation of AI in biomedicine is still in its early stage. New progress and improvements will continue to push the frontier and widen the compass of AI operations, and fast developments are envisaged in the near future.AI in healthcare is an umbrella term to describe the application of machine learning (ML) algorithms and other cognitive technologies in medical settings. In the simplest sense, AI is when computers and other machines mimic human cognition, and are capable of learning, thinking, and making decisions or taking actions. Artificial intelligence (AI) is gradationally changing medical practice. With recent progress in digitized data accession, machine literacy and computing structure, AI operations are expanding into areas that were preliminary allowed to be only the fiefdom of mortal experts. In this Review composition, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and epitomize the profitable, legal and counteraccusations of AI in healthcare.

Clinical trial search: Using biomedical language understanding models for re-ranking.

Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art effectiveness in some of the biomedical information processing applications. We investigate the effectiveness of these techniques for clinical trial search systems. In precision medicine, matching patients to relevant experimental evidence or prospective treatments is a complex task which requires both clinical and biological knowledge. To assist in this complex decision making, we investigate the effectiveness of different ranking models based on the BERT models under the same retrieval platform to ensure fair comparisons. An evaluation on the TREC Precision Medicine benchmarks indicates that our approach using the BERT model pre-trained on scientific abstracts and clinical notes achieves state-of-the-art results, on par with highly specialised, manually optimised heuristic models. We also report the best results to date on the TREC Precision Medicine 2017 ad hoc retrieval task for clinical trial search.

Artificial Intelligence in Healthcare: Review and Prediction Case Studies

Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.

Protein Entity Name Recognition Using Orthographic, Morphological and Proteinhood Features

Protein name identification in text is an important and challenging fundamental precursor in biomedical information processing. For example, accurate identification of protein names affects the finding of protein-protein interactions from biomedical literature. In this paper, we present an efficient protein name identification technique based on a rich set of features: orthographic, morphological as well as Proteinhood features which are introduced newly in this study. The method was evaluated on GENIA corpus with the use of different machine learning algorithms. The highest values for precision 92.1%, recall 86.5% and F-measure 89.2% were achieved on Random Forest, while reducing the training and testing time significantly. We studied and showed the impact of the Proteinhood feature in protein identification as well as the effect of tuning the parameters of the machine learning algorithm.

Biomedical Signal Processing and Modeling Complexity of Living Systems 2014

Biomedical Signal Processing and Modeling Complexity of Living Systems 2014

Guest Editorial—ISCAS 2013 Special Issue

The eight articles in this special section have been selected from papers presented at the Biomedical and Life-Science Circuits and Systems, Biomedical Information Processing, Implantable and Wearable Technology, and Biomedical Algorithm sessions of the 2013 International Symposium on Circuits and Systems (ISCAS 2013) that was held in Beijing, China, May 19-23, 2013.

Biomedical Signal Processing and Modeling Complexity of Living Systems

Biomedical Signal Processing and Modeling Complexity of Living Systems

Resource-aware secure ECG healthcare monitoring through body sensor networks

Real-time medical data about patients' physiological status can be collected simply by using wearable medical sensors based on a body sensor network. However, we lack an efficient, reliable, and secure BSN platform that can meet increasing needs in e-healthcare applications. Many such applications require a BSN to support multiple data rates with reliable and energyefficient data transmission. In this article we propose a secure and resource-aware BSN architecture to enable real-time healthcare monitoring, especially for secure wireless electrocardiogram data streaming and monitoring. A cross-layer framework was developed based on unequal resource allocation to support efficient biomedical data monitoring. In this framework important information (e.g., critical ECG data) is identified, and extra resources are allocated to protect it. Furthermore, BSN resource factors are exploited to guarantee a strict requirement of real-time performance. In this work we integrate biomedical information processing and transmission in a unified platform, where secure data transmission in a BSN proceeds with energy efficiency and minimum delay. In particular, we present a wearable ECG device consisting of small and low-power healthnode sensors for wireless three-lead ECG monitoring. Experimental and simulation results demonstrate that the proposed framework can support real-time wireless biomedical monitoring applications.

Biomedical Informatics and Granularity

An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).

Conducting nonadaptive psychoacoustic or biomedical tests by means of an interactive program

The program described in this paper was originally designed to generate the (synthetic) stimuli required for a large class of psychoacoustic tests. In our opinion, however, it may also be of interest in other fields of biomedical information processing. The applied computational techniques are standard in the field of digital signal processing, but the program is particular in its way of presenting signals, executing window operations, and describing the decision blocks to be presented during a test. Each decision block is a succession of stimuli and pauses, presented to obtain one judgement from the subject.