Abstract：
OBJECTIVE:We present a combined terminological resource for text mining over biomedical literature. The purpose of the resource is to allow the detection of mentions of specific biological entities in scientific publications, and their grounding to widely accepted identifiers. This is an essential process, useful in itself, and necessary as an intermediate step for almost every type of complex text mining application. METHODS:We discuss some of the properties of the terminology for this domain, in particular the degree of ambiguity, which constitutes a peculiar problem for text mining applications. Without a correct recognition and disambiguation of the domain entities no reliable results can be produced. RESULTS:We also discuss an application that makes use of the resulting terminological knowledge base. We annotate an existing corpus of sentences about protein interactions. The annotation consists of a normalization step that matches the terms in our resource with their actual representation in the corpus, and a disambiguation step that resolves the ambiguity of matched terms. CONCLUSION:In this paper we present a large terminological resource, compiled through the aggregation of a number of different manually curated sources. We discuss the lexical properties of such resources, specifically the degree of ambiguity of the terms, and we inspect the causes of such ambiguity, in particular for protein names. This information is of vital importance for the implementation of an efficient term normalization and grounding algorithm.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Rinaldi F,Kaljurand K,Sætre Rdoi
10.1016/j.artmed.2011.04.011subject
Has Abstractpub_date
2011-06-01 00:00:00pages
107-14issue
2eissn
0933-3657issn
1873-2860pii
S0933-3657(11)00052-2journal_volume
52pub_type
杂志文章abstract：:Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101927
更新日期：2020-07-01 00:00:00
abstract：:We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed,...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(01)00101-4
更新日期：2002-02-01 00:00:00
abstract：:An object-oriented approach has been applied to the different stages involved in developing a knowledge base about insulin metabolism. At an early stage the separation of terminological and assertional knowledge was made. The terminological component was developed by medical experts and represented in CORE. An object-...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/0933-3657(94)90025-6
更新日期：1994-12-01 00:00:00
abstract：OBJECTIVE:Gene Ontology (GO) has become a routine resource for functional analysis of gene lists. Although a number of tools have been provided to identify enriched GO terms in one or two gene lists, two technical challenges remain. First, how to handle multiple hypothesis testing in the analysis given that the tests a...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2007.08.002
更新日期：2007-10-01 00:00:00
abstract：OBJECTIVE:To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS:We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent ag...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2012.04.005
更新日期：2012-07-01 00:00:00
abstract：OBJECTIVE:The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS:All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored b...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2007.04.003
更新日期：2007-07-01 00:00:00
abstract：:A capsule endoscopy examination of the human small bowel generates a large number of images that have high similarity. In order to reduce the time it takes to review the high similarity images, clinicians will increase the playback speed, typically to 15 frames per second [1]. Associated with this behaviour is an incr...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2018.12.008
更新日期：2019-03-01 00:00:00
abstract：:The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive an...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.02.002
更新日期：2019-05-01 00:00:00
abstract：:Over 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 mi...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101804
更新日期：2020-03-01 00:00:00
abstract：OBJECTIVE:In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal concept...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.013
更新日期：2009-05-01 00:00:00
abstract：:Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated sys...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2018.06.004
更新日期：2018-08-01 00:00:00
abstract：OBJECTIVE:This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY:The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2005.09.002
更新日期：2006-05-01 00:00:00
abstract：OBJECTIVE:This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. METHODS AND MATERIAL:Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an ey...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.04.005
更新日期：2008-07-01 00:00:00
abstract：:According to functional or anatomical modalities, medical imaging provides a visual representation of complex structures or activities in the human body. One of the most common processing methods applied to those images is segmentation, in which an image is divided into a set of regions of interest. Human anatomical c...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101980
更新日期：2020-11-01 00:00:00
abstract：:To explore the design of computer-supported collaborative work in health care, a case study is described addressing the social contexts and conditions influencing the development process. The data set covers 13 consecutive meetings held in a systems design group over a 2-year period, in total approximately 24 h of vid...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(97)00046-8
更新日期：1998-02-01 00:00:00
abstract：OBJECTIVE:Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not native...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2015.09.009
更新日期：2018-11-01 00:00:00
abstract：:This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00076-3
更新日期：2003-11-01 00:00:00
abstract：:INTERNIST-I was an expert system designed in the early 1970's to diagnose multiple diseases in internal medicine by modelling the behaviour of clinicians. Its form and operation are described, and evaluations of the system are surveyed. The major result of the project was its knowledge base which has been used in succ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/0933-3657(94)00028-q
更新日期：1995-04-01 00:00:00
abstract：:Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart soun...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2017.05.005
更新日期：2017-05-01 00:00:00
abstract：CONTEXT:Temporal information plays a crucial role in medicine, so that in medical informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit trea...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2012.03.002
更新日期：2012-07-01 00:00:00
abstract：OBJECTIVE:The successful preparation of cells for therapy depends on the characterization of causal factors affecting cell quality. Ultra scale-down methods are used to characterize cells in terms of their response to process engineering causal factors of hydrodynamic shear stress and time. This response is in turn cha...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.07.003
更新日期：2014-10-01 00:00:00
abstract：:As 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 ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101783
更新日期：2020-03-01 00:00:00
abstract：OBJECTIVE:This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does n...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.05.001
更新日期：2011-10-01 00:00:00
abstract：OBJECTIVES:The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system. METHOD:Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorit...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.06.005
更新日期：2019-07-01 00:00:00
abstract：:Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipment. However, the bulk of continuous data arising from complex monitoring systems in combination with discontinuously assessed numerical and qualitative data creates a rising infor...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(96)00355-7
更新日期：1996-11-01 00:00:00
abstract：BACKGROUND:The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspec...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101751
更新日期：2020-01-01 00:00:00
abstract：:Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be ut...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00057-8
更新日期：1999-03-01 00:00:00
abstract：OBJECTIVE:The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining technology to analyze and predict the depression tendency of web posts. ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2015.10.003
更新日期：2016-01-01 00:00:00
abstract：OBJECTIVE:A metaschema is an abstraction network of the UMLS's semantic network (SN) obtained from a connected partition of its collection of semantic types. A lexical metaschema was previously derived based on a lexical partition which partitioned the SN into semantic-type groups using identical word-usage among the n...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2005.01.002
更新日期：2005-07-01 00:00:00
abstract：:In this study different substitution methods for the replacement of missing data values were inspected for the use of these cases in a neural network based decision support system for acute appendicitis. The leucocyte count had the greatest number of missing values and was used in the analyses. Four different methods ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00027-x
更新日期：1998-07-01 00:00:00
