waashk/dblp huggingface dataset:
anonymisation of swedish clinical data. there is a constantly growing demand for exchanging clinical and health-related information electronically. in the era of the electronic health record the release of individual data for research, health care statistics, monitoring of new diagnostic tests and tracking disease outbreak alerts are some of the areas in which the protection of (patient) privacy has become an important concern. in this paper we present a system for automatic anonymisation of swedish clinical free text, in the form of discharge letters, by applying generic named entity recognition technology.
contrast set mining for distinguishing between similar diseases. the task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. in particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. we describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
multi-resolution image parametrization in stepwise diagnostics of coronary artery disease. coronary artery disease is one of the world's most important causes of early mortality, so any improvements of diagnostic procedures are highly appreciated. in the clinical setting, coronary artery disease diagnostics is typically performed in a sequential manner. the four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and ecg (electrocardiogram) at rest, (2) ecg testing during a controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography (which is considered as the "gold standard" reference method). in our study we focus on improving diagnostic performance of the third diagnostic level (myocardial perfusion scintigraphy). this diagnostic level consists of series of medical images that are easily obtained and the imaging procedure represents only a minor threat to patients' health. in clinical practice, these images are manually described (parameterized) and subsequently evaluated by expert physicians. in our paper we present an innovative alternative to manual image evaluation --- an automatic image parametrization on multiple resolutions, based on texture description with specialized association rules, and image evaluation with machine learning methods. our results show that multi-resolution image parameterizations equals the physicians in terms of quality of image parameters. however, by using both manual and automatic image description parameters at the same time, diagnostic performance can be significantly improved with respect to the results of clinical practice.
interacting agents for the risk assessment of allergies in newborn babies. allergic diseases are increasing all over the world. therefore, the risk assessment of allergy in newborns is a key issue for prevention purposes. the risk can be assessed at the birth by combining information about familiarity with results of blood examination. then, the individual must be monitored, particularly in the fist months of life, in order to better define the type of allergy and the risk. the monitoring is carried on by different professionals (agents), therefore the communication and collaboration between these agents must be supported in order to obtain the best treatment strategy for the baby. this paper presents a new project which allows the cooperation between the agents involved in the risk assessment of allergy in newborn babies, and presents the main technologies which will be used to develop it.
novel features for automated lung function diagnosis in spontaneously breathing infants. a comparative analysis of 14 classic and 23 novel mathematical features for diagnosing lung function in infants is presented. the data set comprises tidal breathing flow volume loops of 195 spontaneously breathing infants aged 3 to 24 months, with 9 known breathing problems (diseases). the data set is sparse. diagnostic power was evaluated using support vector machines featuring both polynomial and gaussian kernels in a rigorous experimental setting (100 runs for random splits of data into the training set (90% of data) and test set (10% of data)). novel features achieve lower error rates than the old ones.
a mixed data clustering algorithm to identify population patterns of cancer mortality in hijuelas-chile. the cancer disease in hijuelas-chile represents the 45% of the population deaths in the last decade. this high mortality rate have concerned the sanitary authority that lacks of information to identify the risk groups and the factors that influence in the disease.in this work we propose a clustering algorithm for mixed numerical, categorical and multi-valued attributes. we apply our proposed algorithm to identify and to characterize the common patterns in people who died of cancer in the population of hijuelas between 1994 and 2006. as a consequence of this research, we were able to characterize the people who died of cancer in hijuelas-chile.
a causal modeling framework for generating clinical practice guidelines from data. the practice of medicine is becoming increasingly evidence-based and clinical practice guidelines (cpgs) are necessary for advancing evidence-based medicine (ebm). we hypothesize that machine learning methods can play an important role in learning cpgs automatically from data . automatically induced cpgs can then be used for further manual refinement and deployment, for automated guideline compliance checking, for better understanding of disease processes, and for improved physician education. we discuss why learning cpgs is a special form of computational causal discovery and why simply predictive (i.e., non-causal) methods may not be appropriate for this task.
knowledge-based modeling and simulation of diseases with highly differentiated clinical manifestations. this paper presents the cognitive model of gastroesophageal reflux disease (gerd) developed for the maryland virtual patient simulation and mentoring environment. gerd represents a class of diseases that have a large number of clinical manifestations. our model at once manages that complexity while offering robust automatic function in response to open-ended user actions. this ontologically grounded model is largely based on script-oriented representations of causal chains reflecting the actual physiological processes in virtual patients. a detailed description of the gerd model is presented along with a high-level description of the environment for which it was developed.
a methodology for automated extraction of the optimal pathways from influence diagrams. the influence diagram (id) is a powerful tool for modelling medical decision-making processes, like the optimal application of diagnostic imaging. in this area, where safety and efficacy are determined by a number of aspects varying in nature and importance, it is difficult for humans to relate all available pieces of evidence and consequences of choices. ids are well suited to provide evidence-based diagnostic pathways. however, medical specialists cannot be expected to be familiar with ids and their output can be difficult to interpret. to overcome these shortcomings, a methodology is developed to automatically extract the optimal pathways from an id and represent these in a tree shaped flow diagram. it imposes a few general rules on the structure of the model, which determine the relation between decisions to perform imaging and the availability of test results. extracting the optimal pathways requires post-processing the results of an id, leaving out the sub optimal choices and irrelevant scenarios. predictive value of tests are vital information in medical protocols, they are at hand in the id for each relevant scenario. the methodology is illustrated by the problem of diagnosing acute chest pain. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
a pattern recognition approach to diagnose foot plant pathologies: from segmentation to classification. some foot plant diseases such as flat foot and cave foot are usually diagnosed by a human expert. in this paper we propose an original method to diagnose these diseases by using optical color foot plant images. a number of modern image processing and pattern recognition techniques have been employed to configure a system that can dramatically decrease the time in which such analysis are performed, besides delivering robust and reliable results to complement efficiently the specialist's task. our results demonstrate the feasibility of building such automatic diagnosis systems that can be used as massive first screening methods for detecting foot plant pathologies.
co-operative agents in analysis and interpretation of intracerebral eeg activity: application to epilepsy. the paper presents a distributed approach for the interpretation of epileptic signals based on a dynamical vectorial analysis method. the approach associates signal processing methods into a situated, reactive, cooperative and decentralized implementation. the objective is to identify and locate the various interictal and ictal epileptiform events (pathological and/or normal) contained in intracerebral eeg signals (one hundred recording channels in general) recorded in patients suffering from partial temporal lobe epilepsy. this approach associates some signal processing methods (spectral analysis, causality measurements, detection, classification) in a multi-agent system.
an integrated it system for phenotypic and genotypic data mining and management. this paper describes the application of an information technology infrastructure aimed at supporting translational bioinformatics studies which need the joint management of phenotypic and genotypic data. the system provides an integrated and easy to use software environment, based on data warehouse and data mining tools, to discover the most frequent complex phenotypes and search their penetrance and heritability by mapping them on the population pedigree. we first use a logical formalization to define phenotypes of interest in order to retrieve individuals having that phenotype from the electronic medical record. we then use an open-source web-based data warehouse application for analyzing phenotypic data and presenting the results in a multidimensional format. relationships between the selected individuals are automatically visualized by integrating in the system an ad-hoc developed pedigree visualization tool. finally, the application of the system to support a genetic study of an isolated population, the val borbera project, is presented.
using semantic web technologies for knowledge-driven querying of biomedical data. software applications that work with biomedical data have significant knowledge-management requirements. formal knowledge models and knowledge-based methods can be very useful in meeting these requirements. however, most biomedical data are stored in relational databases, a practice that will continue for the foreseeable future. using these data in knowledge-driven applications requires approaches that can form a bridge between relational models and knowledge models. accomplishing this task efficiently is a research challenge. to address this problem, we have developed an end-to-end knowledge-based system based on semantic web technologies. it permits formal design-time specification of the data requirements of a system and uses those requirements to drive knowledge-driven queries on operational relational data in a deployed system. we have implemented a dynamic owl-to-relational mapping method and used swrl, the semantic web rule language, as a high-level query language that uses these mappings. we have used these methods to support the development of a participant tracking application for clinical trials and in the development of a test bed for evaluating biosurveillance methods.
computerised guidelines implementation: obtaining feedback for revision of guidelines, clinical data model and data flow. in this paper we describe a module that allows to collect (a) motivations for non-compliance to guidelines, (b) motivations for poor data entry into the electronic patient record, and (c) comments on medical aspects of guideline recommendations, on their formalisation into computerised rules, and on the guideline integration into the computerised clinical chart. we organised a well-structured taxonomy of non-compliance motivations in such a way that the main hierarchical levels correspond to different medical or technical roles suitable for feedback managing. we analysed about 400 consecutive cases of patients with ischemic stroke. about 40 non-compliances, as well as several incomplete data forms have been identified and motivated.
analyzing differences in operational disease definitions using ontological modeling. in medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. in the communication between health care professionals, this is generally not problematic. in biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. in practice, this results in different operational definitions being in use for a single disease. this paper presents an approach to compare different operational definitions of a single disease using ontological modeling. the approach is illustrated with a case-study in the area of severe sepsis.
learning decision tree for selecting qrs detectors for cardiac monitoring. the qrs complex is the main wave of the ecg. it is widely used for diagnosing many cardiac diseases. automatic qrs detection is an essential task of cardiac monitoring and many detection algorithms have been proposed in the literature. although most of the algorithms perform satisfactorily in normal situations, there are contexts, in the presence of noise or a specific pathology, where one algorithm performs better than the others. we propose a combination method that selects, on line, the detector that is the most adapted to the current context. the selection is done by a decision tree that has been learnt from the performance measures of 7 algorithms in various instances of 130 combinations of arrhythmias and noises. the decision tree is compared to expert rules tested in the framework of the cardiac monitoring system ip-calicot.
automatic generation of textual summaries from neonatal intensive care data. intensive care is becoming increasingly complex. if mistakes are to be avoided, there is a need for the large amount of clinical data to be presented effectively to the medical staff. although the most common approach is to present the data graphically, it has been shown that textual summarisation can lead to improved decision making. as the first step in the babytalk project, a prototype is being developed which will generate a textual summary of 45 minutes of continuous physiological signals and discrete events (e.g.: equipment settings and drug administration). its architecture brings together techniques from the different areas of signal analysis, medical reasoning, and natural language generation. although the current system is still being improved, it is powerful enough to generate meaningful texts containing the most relevant information. this prototype will be extended to summarize several hours of data and to include clinical interpretation.
a nearest neighbor approach to predicting survival time with an application in chronic respiratory disease. the care for patients with chronic and progressive diseases often requires that reliable estimates of their remaining lifetime are made. the predominant method for obtaining such individual prognoses is to analyze historical data using cox regression, and apply the resulting model to data from new patients. however, the black-box nature of the cox regression model makes it unattractive for clinical practice. instead most physicians prefer to relate a new patient to the histories of similar, individual patients that were treated before. this paper presents a prognostic inference method that combines the k-nearest neighbor paradigm with cox regression. it yields survival predictions for individual patients, based on small sets of similar patients from the past, and can be used to implement a prognostic case-retrieval system. to evaluate the method, it was applied to data from patients with idiopathic interstitial pneumonia, a progressive and lethal lung disease. experiments pointed out that the method competes well with cox regression. the best predictive performance was obtained with a neighborhood size of 20.
monitoring human resources of a public health-care system through intelligent data analysis and visualization. a public health-care system (hcs) is a complex system that requires permanent monitoring. this paper focuses on the slovenian national hcs sub-system consisting of a network of health-care professionals at the primary care level. the challenge addressed in this paper is the development and application of intelligent data analysis, decision support and visualization methods aimed to improve the monitoring of human resources of this network. the main outcome is a set of proposed performance indicators and the developed model for monitoring the network of primary health-care professionals of slovenia. the model enables improved planning and management through data analysis and visualization modules developed for the monitoring of physicians' qualification, age, workload and dispersion.
mrf agent based segmentation: application to mri brain scans. the markov random field (mrf) probabilistic framework is classically introduced for a robust segmentation of magnetic resonance imaging (mri) brain scans. most mrf approaches handle tissues segmentation via global model estimation. structure segmentation is then carried out as a separate task. we propose in this paper to consider mrf segmentation of tissues and structures as two local and cooperative procedures immersed in a multiagent framework. tissue segmentation is performed by partitionning the volume in subvolumes where agents estimate local mrf models in cooperation with their neighbours to ensure consistency of local models. these models better reflect local intensity distributions. structure segmentation is performed via dynamically localized agents that integrate anatomical spatial constraints provided by an apriori fuzzy description of brain anatomy. structure segmentation is not reduced to a postprocessing step: rather, structure agents cooperate with tissue agents to render models gradually more accurate. we report several experiments that illustrate the working of our multiagent framework. the evaluation was performed using both phantoms and real 3t brain scans and showed a robustness to nonuniformity and noise together with a low computational time. this mrf agent based approach appears as a very promising new tool for complex image segmentation.
on the behaviour of information measures for test selection. in diagnostic decision-support systems, a test-selection facility serves to select tests that are expected to yield the largest decrease in the uncertainty about a patient's diagnosis. for capturing diagnostic uncertainty, often an information measure is used. in this paper, we study the shannon entropy, the gini index, and the misclassification error for this purpose. we argue that for a large range of values, the first derivative of the gini index can be regarded as an approximation of the first derivative of the shannon entropy. we also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. we further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. experimental results from using the measures with a real-life probabilistic network in oncology support our observations.
enhancing automated test selection in probabilistic networks. most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test variables are selected sequentially, on a one-by-one basis, based upon expected information gain. while myopic test selection is not realistic for many medical applications, non-myopic test selection, in which information gain would be computed for all combinations of variables, would be too demanding. we present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables; these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. in a preliminary evaluation study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.
computer-aided assessment of drug-induced lung disease plausibility. drug-induced lung disease (dild), often suspected in pneumology, is still a diagnostic challenge because of the ever increasing number of pneumotoxic drugs and the large diversity of observed clinical patterns. as a result, dild can only be evoked as a plausible diagnosis after the exclusion of all other possible causes. pneumodoc is a computer-based decision support that formalises the evaluation process of the drug-imputability of a lung disease. the knowledge base has been structured as a two-level decision tree. patient-specific chronological and semiological criteria are first examined leading to the assessment of a qualitative intrinsic dild plausibility score. then literature-based data including the frequency of dild with a given drug and the frequency of the observed clinical situation among the clinical patterns reported with the same drug are evaluated to compute a qualitative extrinsic dild plausibility score. based on a simple multimodal qualitative model, extrinsic and intrinsic scores are combined to yield an overall dild plausibility score.
maintaining formal models of living guidelines efficiently. translating clinical guidelines into formal models is beneficial in many ways, but expensive. the progress in medical knowledge requires clinical guidelines to be updated at relatively short intervals, leading to the term living guideline. this causes potentially expensive, frequent updates of the corresponding formal models.when performing these updates, there are two goals: the modelling effort must be minimised and the links between the original document and the formal model must be maintained. in this paper, we describe our solution, using tools and techniques developed during the protocure ii project.
categorical representation of evolving structure of an ontology for clinical fungus. with increasing popularity of using ontologies, many industrial and clinical applications have employed ontologies as their conceptual backbone. ontologies try to capture knowledge from a domain of interest and when the knowledge changes, the definitions will be altered. we study change management in the fungalweb ontology, which is the result of integrating numerous biological databases and web accessible textual resources. the fungal taxonomy is currently unstable and evolves over time. this evolution can be seen in both nomenclature and the taxonomic structure. in an experiment we have focused on changes in medical species of fungus which can potentially alter the related disease name and description in an integrated clinical system. in order to address certain aspects of representation of changes in an ontology driven clinical application we propose a methodology based on category theory as a mathematical notation, which is independent of a specific choice of ontology language and any particular implementation.