Artificial Intelligence in Medicine This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
Publication date: Available online 13 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Elisa Ferrari, Alessandra Retico, Davide BacciuAbstractOver the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, ca...
Source: Artificial Intelligence in Medicine - January 14, 2020 Category: Bioinformatics Source Type: research

ADHD classification by dual subspace learning using resting-state functional connectivity
Publication date: Available online 13 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Ying Chen, Yibin Tang, Chun Wang, Xiaofeng Liu, Li Zhao, Zhishun WangAbstractAs one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively contain...
Source: Artificial Intelligence in Medicine - January 14, 2020 Category: Bioinformatics Source Type: research

Seven Pillars of Precision Digital Health and Medicine
Publication date: Available online 11 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Arash Shaban-Nejad, Martin Michalowski, Niels Peek, John S. Brownstein, David L. Buckeridge (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - January 12, 2020 Category: Bioinformatics Source Type: research

Comprenhensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines
ConclusionsThe proposed framework of criteria may help clinical institutions to select the most suitable rule technology for the representation of CGs in general, and for the antibiotic prescription domain in particular, depicting the main aspects that lead to Computer Interpretable Guidelines (CIGs), such as Logic expressivity (Open/Closed World Assumption, Negation-As-Failure), Temporal Reasoning and Interoperability with existing HIS and clinical workflow. Future work will focus on providing clinicians with suggestions regarding new potential steps for CGs, considering process mining approaches and CGs Process Workflows...
Source: Artificial Intelligence in Medicine - January 10, 2020 Category: Bioinformatics Source Type: research

An effective approach for CT lung segmentation using mask region-based convolutional neural networks
Publication date: Available online 8 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Qinhua Hu, Luís Fabrício de F. Souza, Gabriel Bandeira Holanda, Shara S.A. Alves, Francisco Hércules dos S. Silva, Tao Han, Pedro P. Rebouças FilhoAbstractComputer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung re...
Source: Artificial Intelligence in Medicine - January 8, 2020 Category: Bioinformatics Source Type: research

Optimisation and control of the supply of blood bags in hemotherapic centres via Markov Decision Process with discounted arrival rate
Publication date: Available online 8 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Henrique L.F. Soares, Edilson F. Arruda, Laura Bahiense, Daniel Gartner, Luiz Amorim FilhoAbstractRunning a cost-effective human blood transfusion supply chain challenges decision makers in blood services world-wide. In this paper, we develop a Markov decision process with the objective of minimising the overall costs of internal and external collections, storing, producing and disposing of blood bags, whilst explicitly considering the probability that a donated blog bag will perish before demanded. The model finds an opt...
Source: Artificial Intelligence in Medicine - January 8, 2020 Category: Bioinformatics Source Type: research

Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning
ConclusionSCA gait parameters were characterized by a reduced stride length, slower walking velocity, and longer supporting phase. Additionally, a smaller cerebellar volume correlated with an increased irregularity in gait. Gait characteristics exhibited considerable clinical relevance to hereditary SCA. We conclude that a combination of gait parameters, ataxia scales, and MRVD may represent more objective markers for clinical evaluations of SCA. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - January 7, 2020 Category: Bioinformatics Source Type: research

An Improved Multi-swarm Particle Swarm Optimizer for Optimizing the Electric Field Distribution of Multichannel Transcranial Magnetic Stimulation
Publication date: Available online 3 January 2020Source: Artificial Intelligence in MedicineAuthor(s): Hui Xiong, Bowen Qiu, Jinzhen LiuAbstractMultichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of double layer coil array. To balance the exploration and exploitation abilities, three novel improve...
Source: Artificial Intelligence in Medicine - January 4, 2020 Category: Bioinformatics Source Type: research

An Intelligent Learning Approach for Improving ECG Signal Classification and Arrhythmia Analysis
Publication date: Available online 31 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Arun Kumar Sangaiah, Maheswari Arumugam, Gui-Bin BianAbstractThe recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia cla...
Source: Artificial Intelligence in Medicine - January 2, 2020 Category: Bioinformatics Source Type: research

Semantic Segmentation with DenseNets for Carotid Artery Ultrasound Plaque Segmentation and CIMT estimation
ConclusionsThe validation carried out demonstrates that the proposed method is accurate and objective for both plaque detection and CIMT measurement. Moreover, the robustness and generalization capacity of the method have been proven with two different data sets. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - January 2, 2020 Category: Bioinformatics Source Type: research

The impact of machine learning on patient care: a systematic review
ConclusionThe majority of literature describing the use of ML in clinical medicine is retrospective in nature and often outlines proof-of-concept approaches to impact patient care. We postulate that identifying and overcoming key translational barriers, including real-time access to clinical data, data security, physician approval of “black box” generated results, and performance evaluation will allow for a fundamental shift in medical practice, where specialized tools will aid the healthcare team in providing better patient care. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - January 2, 2020 Category: Bioinformatics Source Type: research

A novel method of motor imagery classification using eeg signal
Publication date: Available online 31 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Venkatachalam K., Devipriya A., Maniraj J., Sivaram M., Ambikapathy A., S Amiri IrajAbstractA subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, neuro-prosthetics and neuro-rehabilitation, where the user’s thoughts of imagined movements need to be decoded. Electroencephalography (EEG) equipment is commonly used for keeping tra...
Source: Artificial Intelligence in Medicine - January 2, 2020 Category: Bioinformatics Source Type: research

Topic-informed neural approach for biomedical event extraction
Publication date: Available online 30 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Junchi Zhang, Mengchi Liu, Yue ZhangAbstractAs a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is bene...
Source: Artificial Intelligence in Medicine - January 1, 2020 Category: Bioinformatics Source Type: research

A fusion framework to extract typical treatment patterns from electronic medical records
ConclusionThe extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice. (Source: Artificial Intelligence in Medicine)
Source: Artificial Intelligence in Medicine - December 29, 2019 Category: Bioinformatics Source Type: research

Multi-planar 3D Breast Segmentation in MRI via Deep Convolutional Neural Networks
Publication date: Available online 23 December 2019Source: Artificial Intelligence in MedicineAuthor(s): Gabriele Piantadosi, Mario Sansone, Roberta Fusco, Carlo SansoneAbstractNowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual DCE-MRI examination can be difficult and error-prone. The early stage of breast tissue segmentation, in a typical CAD, is crucial to increase reliability and reduce the computational effort by reducing ...
Source: Artificial Intelligence in Medicine - December 24, 2019 Category: Bioinformatics Source Type: research