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Journal of Biomedical Informatics

Andrej Kastrin, Dimitar Hristovski
Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science. In this paper, we analyzed the dynamic properties and growth principles of the MEDLINE bibliographic database using network analysis methodology. The basic assumption of this work is that the scientific evolution of the life sciences can be represented as a list of co-occurrences of MeSH descriptors that are linked to MEDLINE citations...
December 6, 2018: Journal of Biomedical Informatics
Bruno Iochins Grisci, Bruno César Feltes, Marcio Dorn
Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data...
December 3, 2018: Journal of Biomedical Informatics
Danilo Avola, Luigi Cinque, Gian Luca Foresti, Marco Raoul Marini
Strokes, surgeries, or degenerative diseases can impair motor abilities and balance. Long-term rehabilitation is often the only way to recover, as completely as possible, these lost skills. To be effective, this type of rehabilitation should follow three main rules. First, rehabilitation exercises should be able to keep patient's motivation high. Second, each exercise should be customizable depending on patient's needs. Third, patient's performance should be evaluated objectively, i.e., by measuring patient's movements with respect to an optimal reference model...
December 3, 2018: Journal of Biomedical Informatics
T Blanco, R Casas, A Marco, I Martínez
OBJECTIVE: To contribute the design, development, and assessment of a new concept: Micro ad hoc Health Social Networks (uHSN), to create a social-based solution for supporting patients with chronic disease. DESIGN: After in-depth fieldwork and intensive co-design over a 4-year project following Community-Based Participatory Research (CBPR), this paper contributes a new paradigm of uHSN, defining two interaction areas (the "backstage", the sphere invisible to the final user, where processes that build services take place; and the "onstage", the visible part that includes the patients and relatives), and describes a new transversal concept, i...
November 29, 2018: Journal of Biomedical Informatics
Bakhtiar Ostadi, Reza Mokhtarian Daloie, Mohammad Mehdi Sepehri
Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare services. In this regard, new approaches such as Time Driven-Activity based Costing (TDABC) provide appropriate information on the activities needed to provide a quality service. However, TDABC is not flawless. This system is designed for conditions of relatively accurate information that can accurately estimate the cost of services provided to patients. In this study, the fuzzy logic in the TDABC model is used to resolve the inherent ambiguity and uncertainty and determine the best possible values for cost, capacity, and time parameters to provide accurate information on the costs of the healthcare services...
November 22, 2018: Journal of Biomedical Informatics
Jiantao Bian, Samir Abdelrahman, Jianlin Shi, Guilherme Del Fiol
OBJECTIVES: Finding recent clinical studies that warrant changes in clinical practice ("high impact" clinical studies) in a timely manner is very challenging. We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance. METHODS: To identify recent studies, we developed our classification model using time-agnostic features that are available as soon as an article is indexed in PubMed® , such as journal impact factor, author count, and study sample size...
November 20, 2018: Journal of Biomedical Informatics
(no author information available yet)
No abstract text is available yet for this article.
November 15, 2018: Journal of Biomedical Informatics
Abeed Sarker, Graciela Gonzalez-Hernandez
BACKGROUND: Data collection and extraction from noisy text sources such as social media typically rely on keyword-based searching/listening. However, health-related terms are often misspelled in such noisy text sources due to their complex morphology, resulting in the exclusion of relevant data for studies. In this paper, we present a customizable data-centric system that automatically generates common misspellings for complex health-related terms, which can improve the data collection process from noisy text sources...
November 13, 2018: Journal of Biomedical Informatics
Wen Zhang, Yanlin Chen, Dingfang Li, Xiang Yue
Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery. Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs, and is important for the drug safety surveillance. Here, we formulate the drug-drug interaction prediction as a matrix completion task, and project drugs in the interaction space into a low-dimensional space. We consider drug features, i.e., substructures, targets, enzymes, transporters, pathways, indications, side effects, and off side effects, to calculate drug-drug similarities, and assume them as manifolds in feature spaces...
November 13, 2018: Journal of Biomedical Informatics
Milad Moradi
Automatic text summarizers can reduce the time required to read lengthy text documents by extracting the most important parts. Multi-document summarizers should produce a summary that covers the main topics of multiple related input texts to diminish the extent of redundant information. In this paper, we propose a novel summarization method named Clustering and Itemset mining based Biomedical Summarizer (CIBS). The summarizer extracts biomedical concepts from the input documents and employs an itemset mining algorithm to discover main topics...
November 13, 2018: Journal of Biomedical Informatics
Orazio Gambino, Leonardo Rundo, Vincenzo Cannella, Salvatore Vitabile, Roberto Pirrone
Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets...
November 9, 2018: Journal of Biomedical Informatics
Lisa V Grossman, Elliot G Mitchell, George Hripcsak, Chunhua Weng, David K Vawdrey
BACKGROUND: Previous research has developed methods to construct acronym sense inventories from a single institutional corpus. Although beneficial, a sense inventory constructed from a single institutional corpus is not generalizable, because acronyms from different geographic regions and medical specialties vary greatly. OBJECTIVE: Develop an automated method to harmonize sense inventories from different regions and specialties towards the development of a comprehensive inventory...
November 7, 2018: Journal of Biomedical Informatics
Matin Kheirkhahan, Sanjay Nair, Anis Davoudi, Parisa Rashidi, Amal A Wanigatunga, Duane B Corbett, Tonatiuh Mendoza, Todd M Manini, Sanjay Ranka
Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data...
November 7, 2018: Journal of Biomedical Informatics
Zhu Zhecheng, Heng Bee Hoon, Teow Kiok Liang
INTRODUCTION: Comorbidity is common in elderly patients and it imposes heavy burden on both individual and the whole healthcare system. This study aims to gain insights of comorbidity development by simulating the lifetime trajectory of disease progression from single chronic disease to comorbidity. METHODS: Eight health states spanning from no chronic condition to comorbidity are considered in this study. Disease progression network is constructed based on the seven-year retrospective data of around 700,000 residents living in Singapore central region...
November 7, 2018: Journal of Biomedical Informatics
Liuyun Gong, Dan Zhang, Yiping Dong, Yutiantian Lei, Yuanjie Qian, Xinyue Tan, Suxia Han, Jiquan Wang
PURPOSE: We explored the mechanism of aspirin in SCLC by dissecting many publicly available databases. METHODS AND RESULTS: Firstly, 11 direct protein targets (DPTs) of aspirin were identified by DrugBank 5.0. Then protein-protein interaction (PPI) network and signaling pathways of aspirin DPTs were analyzed. We found that aspirin was linked with many kinds of cancer, and the most significant one is SCLC. Next, we classified the mutation of 4 aspirin DPTs in SCLC (IKBKB, NFKBIA, PTGS2 and TP53) using cBio Portal...
November 7, 2018: Journal of Biomedical Informatics
Ying Shen, Lizhu Zhang, Jin Zhang, Min Yang, Buzhou Tang, Yaliang Li, Kai Lei
The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs...
November 3, 2018: Journal of Biomedical Informatics
Samaneh Layeghian Javan, Mohammad Mehdi Sepehri, Hassan Aghajani
BACKGROUND: One of the significant problems in the field of healthcare is the low survival rate of people who have experienced sudden cardiac arrest. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Traditional statistical methods have been used to predict cardiac arrest. They have often analyzed group-level differences using a limited number of variables. On the other hand, machine learning approach, which is part of a growing trend of predictive medical analysis, has provided personalized predictive analyses on more complex data and produced remarkable results...
October 30, 2018: Journal of Biomedical Informatics
Sumithra Velupillai, Hanna Suominen, Maria Liakata, Angus Roberts, Anoop D Shah, Katherine Morley, David Osborn, Joseph Hayes, Robert Stewart, Johnny Downs, Wendy Chapman, Rina Dutta
The importance of incorporating Natural Language Processing(NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e...
October 24, 2018: Journal of Biomedical Informatics
M Ahangaran, M R Jahed-Motlagh, B Minaei-Bidgoli
One of the most important issues in predictive modeling is to determine major cause factors of a phenomenon and causal relationships between them. Extracting causal relationships between parameters in a natural phenomenon can be accomplished through checking the parameters' changes in consecutive events. In addition, using information and probabilistic theory help better conception of causal relationships of a phenomenon. Therefore, probabilistic causal discovery from sequential data of a natural phenomenon can be useful for dimension reduction and predicting the future trend of a process...
October 16, 2018: Journal of Biomedical Informatics
Po Yang, Lida Xu
No abstract text is available yet for this article.
November 2018: Journal of Biomedical Informatics
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