waashk/dblp huggingface dataset:
interactive argument. this paper addresses the question of attribution of agency to artifacts. taking an activity-theoretical perspective, i argue that artifacts are used to guide actions. in other words, i claim that artifacts have instructional impact. the introductory part of the paper is an account of how three kinds of artifacts - physical artifacts, linguistic representations, and graphic representations -- are instructionally used in coronary diagnostic work. the main part of the paper is an empirical exploration of how a forth kind of artifact, organization of work, is instructionally used. the empirical case analyzed involves clinical diagnostic work conducted as a video-mediated conference between two collaborating diagnostic subteams, one of which had made the coronary investigation by means of coronary angiography, while the other was to take actions in the form of by-pass surgery or balloon dilatation. in the concluding sections, i discuss in what way it makes sense to say that organization of work and other artifacts have instructional properties.
security. abstract: this keynote address reviews several techniques from morphometrics (the multivariate biometrics of shape) developed mainly in the context of medical image analysis over the last decade. the new techniques pro-vide powerful tools for geometric tasks that arise in the course of most analyses of medical images in groups. these tasks include standardizing against euclidean similarity transformations or shear transformations, en-coding informative prior knowledge about shape variation, and detecting, testing, and visualizing linear statistical patterns of variation within or between groups. i review the features of the present toolkit, the standard underlying data models entailed, and some of the extensions that reach out to the additional information content of medical images for common clinical or scientific applications.
threshold signature. in the late 1950s and early 1960s, computer systems were envisioned that could be used simultaneously by several people, each at a typewriter-like terminal, each appearing to have exclusive use of the computer. the computer was to take advantage of the typing time of one user by turning its attention to another. if the computational tasks requested were short enough, then all users could be serviced, and the illusion of a single-user private computer would be maintained. early systems served less than a dozen people, whereas modern systems service 10 to 1,000 or more. but what if there were one or more very long computational tasks?
credentials. the main trends discernible both in session iv and in the general discussion that ended the 1974 lake arrowhead workshop can be summarized as follows: 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
simultaneous exponentiation. software engineering is the disciplined application of theories and techniques from computer science to define, develop, deliver, and maintain, on time and within budget, software products that meet customers' needs and expectations. software products include the actual program source code and data structures (q.v.), as well as the documents necessary to produce these, and documents and interface programs necessary to use them in the intended environment.
towards the merging of multiple clinical protocols and guidelines via ontology-driven modeling. decision support systems based on computerized clinical protocols (cp) and clinical practice guidelines (cpg) fall short when dealing with patient co-morbidites, as this demands the concurrent merging of multiple cp/cpg. we present an ontology-based approach for the merging of cpg and cp at two levels---i.e. knowledge modeling level and knowledge execution level. we have developed specialized ontological modeling constructs to facilitate merging of cpg and cp. we demonstrate the merging of multiple location-specific cp and disease-specific cpg.
multiple terminologies in a health portal: automatic indexing and information retrieval. background: in the specific context of developing quality-controlled health gateways, several standards must be respected (e.g. dublin core for metadata element set; thesaurus mesh as the controlled vocabulary to index internet resources; hon code to accredit quality of health web sites). these standards were applied to create the cismef web site (french acronym for catalog & index of health internet resources in french). objective: in this work, the strategic shift of the cismef team is intended to index and retrieve french resources not anymore with a single terminology (mesh thesaurus) but with the main health terminologies available in french (icd 10, snomed international, ccam, atc). methods & results: since 2005, we have developed the french multi-terminology indexer (f-mti), using a multi-terminology approach and mappings between health terminologies. this tool is used for automatic indexing and information retrieval. conclusion: since the last quarter of 2008, f-mti is daily used in the cismef production environment and is connected to a french health multi-terminology server.
a knowledge-based system to support emergency medical services for disabled patients. this paper illustrates a knowledge based system devoted to help nurses and volunteers of emergency medical services (ems) in dealing with disabled patients during an emergency.
segmentation of lung tumours in positron emission tomography scans: a machine learning approach. lung cancer represents the most deadly type of malignancy. in this work we propose a machine learning approach to segmenting lung tumours in positron emission tomography (pet) scans in order to provide a radiation therapist with a "second reader" opinion about the tumour location. for each pet slice, our system extracts a set of attributes, passes them to a trained support vector machine (svm), and returns the optimal threshold value for distinguishing tumour from healthy voxels in that particular slice. we use this technique to analyse four different pet/ct 3d studies. the system produced fairly accurate segmentation, with jaccard and dice's similarity coefficients between 0.82 and 0.98 (the areas outlined by the returned thresholds vs. the ones outlined by the reference thresholds). besides the high level of geometric similarity, a significant correlation between the returned and the reference thresholds also indicates that during the training phase, the learning algorithm effectively acquired the dependency between the extracted attributes and optimal thresholds.
providing objective feedback on skill assessment in a dental surgical training simulator. dental students devote several years to the acquisition of sufficient psychomotor skills to prepare them for entry-level dental practice. traditional methods of dental surgical skills training and assessment are being challenged by the complications such as the lack of real-world cases, unavailability of expert supervision and the subjective manner of surgical skills assessment. to overcome these challenges, we developed a dental training system that provides a vr environment with a haptic device for dental students to practice tooth preparation procedures. the system monitors important features of the procedure, objectively assesses the quality of the performed procedure using hidden markov models, and provides objective feedback on the user's performance for each stage in the procedure. important features for characterizing the quality of the procedure were identified based on interviews with experienced dentists. we evaluated the accuracy of the skill assessment with data collected from novice dental students as well as experienced dentists. we also evaluated the quality of the system's feedback by asking a dental expert for comments. the experimental results show high accuracy in classifying users into novice and expert, and the evaluation indicated a high acceptance rate for the generated feedback.
genetic algorithm based scheduling of radiotherapy treatments for cancer patients. this paper presents a multi-objective model for scheduling of radiotherapy treatments for cancer patients based on genetic algorithms (ga). the model is developed and implemented considering real life radiotherapy treatment processes at arden cancer centre, coventry, uk. two objectives are defined: minimisation of the average patient waiting times and minimisation of average tardiness of the patient first treatment fractions. two scenarios are analysed considering the availability of the doctors to approve treatment plans. the schedules generated by the ga using real data collected from the collaborating cancer centre have good performance. it is demonstrated that enabling doctors to approve treatment plans instantly has a great impact on average waiting time and average tardiness for all patient categories.
homenl: homecare assistance in natural language. an intelligent conversational agent for hypertensive patients management. the prospective home-care management will probably offer intelligent conversational assistants for supporting patients at home through natural language interfaces. homecare assistance in natural language, homenl, is a proof-of-concept dialogue system for the management of patients with hypertension. it follows up a conversation with a patient in which the patient is able to take the initiative. homenl processes natural language, makes an internal representation of the patients' contributions, interprets sentences by reasoning about their meaning on the basis of a medical-knowledge representation and responds appropriately. homenl's aim is to provide a laboratory for studying natural language processing (nlp) and intelligent dialogues in clinical domains.
codeslinger: an interactive biomedical ontology browser. codeslinger is a highly interactive and semi-intelligent application designed to support the search and navigation of large biomedical coding schemes, thesauri, and ontologies. we discuss how codeslinger is used by epidemiologist/physicians in the creation of coding sets for data extraction and analysis, the exploratory nature of the application, and finally, the issues facing our knowledge-representation model and extension of the umls.
an advanced platform for managing complications of chronic diseases. this paper describes a generic platform for telemedicine services aimed at supporting chronic outpatients. the framework comprises a server agent and several cell phones as mobile agents through which patients and caregivers within their families may input data and receive back suggestions and advice. mobile agents and server are endowed with domain specific knowledge in order to support users in a flexible way structured on multiple levels.
on quality of different annotation sources for gene expression analysis. mining of biomedical data increasingly relies on utility of knowledge repositories. in gene expression analysis, these are often used for gene labeling with an assumption that similarly annotated genes have similar expression profiles. in the paper we use this assumption to craft a method with which we scored six different annotation sources (e.g., gene ontology, pubmed, and mesh annotations) for their utility in gene expression data analysis. experiments show that the sources that include manual curation perform well and, for instance, score better than automatic annotation from gene-related pubmed abstracts. we also show that there is no clear winner, pointing at the need for methods that could successfully integrate annotations from different sources.
an architecture for automated reasoning systems for genome-wide studies. the massive amounts of data generated by high-throughput experiments makes modern biomedical research a data-intensive discipline, shifting the research methodology from a hypothesis-based approach to a hypothesis-free one. a formal procedure should be defined to properly design a study, understand the outcomes and plan improvements for each task performed during the experiments. such formal approach needs the identification of a high-level conceptual model of the knowledge discovery process occurring in genome-wide studies: this is what existing computational tools lack. starting from an epistemological model of the discovery process proposed for diagnostic reasoning, we describe how the design and execution of modern genome-wide studies can be modelled using the same framework. we show the general validity of the model, how it can be instantiated to model typical scenarios of genome-wide studies, and how we use it to develop tools aimed at building semi-automated reasoning systems.
an ontology for the care of the elder at home. the health-care of the elder at home is highly demanded in modern societies. it is based on the difficult task of coordinating multiple professionals and procedures acting on the same patient. k4care is a project aiming at implementing and testing a technology-based incremental and adaptable model to assist health care systems in home care. one of the key components of this model is the case profile ontology (cpo) that is used to support the activities in the life-cycle of home care. these activities define a path that goes from assessing the problem to deploying a care plan. along this path several cpo-based tools have been implemented to ease the assessment step, to manage care plans as state-decision-action diagrams, to combine care plans for comorbid patients, and to personalize care plans. the use of these tools significantly reduces the complexity of dealing with patients at home.
voice pathology classification by using features from high-speed videos. for the diagnosis of pathological voices it is of particular importance to examine the dynamic properties of the underlying vocal fold (vf) movements occurring at a fundamental frequency of 100---300 hz. to this end, a patient's laryngeal oscillation patterns are captured with state-of-the-art endoscopic high-speed (hs) camera systems capable of recording 4000 frames/second. to date the clinical analysis of these hs videos is commonly performed in a subjective manner via slow-motion playback. hence, the resulting diagnoses are inherently error-prone, exhibiting high inter-rater variability. in this paper an objective method for overcoming this drawback is presented which employs a quantitative description and classification approach based on a novel image analysis strategy called phonovibrography. by extracting the relevant vf movement information from hs videos the spatio-temporal patterns of laryngeal activity are captured using a set of specialized features. as reference for performance, conventional voice analysis features are also computed. the derived features are analyzed with different machine learning (ml) algorithms regarding clinically meaningful classification tasks. the applicability of the approach is demonstrated using a clinical data set comprising individuals with normophonic and paralytic voices. the results indicate that the presented approach holds a lot of promise for providing reliable diagnosis support in the future.
severity evaluation support for burns unit patients based on temporal episodic knowledge retrieval. severity scores are a sort of medical algorithm commonly used in medicine. in practise, physicians only use a few of them, usually internationally accepted ones involving very simple calculations. however, their daily use in critical care services gives rise to two potential problems. first, they do not always cover the particularities of the local population or a specific pathology may not be considered in the score. second, these services (e.g. intensive care units or burns units) are strongly dependent on the evolution of the patients and, so the temporal component plays an essential role that should always be in mind. on the other hand, the knowledge required is at least partially present in the physician team of the medical unit due to the experience gained in treating individual patients, that is, in the form of episodic knowledge. therefore, the use of techniques based on analogy reasoning, such as case-based reasoning, would seem a suitable approach for dealing with part of this problem.in this work, we present an episodic knowledge retrieval system to support the physician in evaluating the severity patients from the temporal evolution point of view. to this end, we present different techniques for temporal retrieval based on previous works on temporal similarity. we also demonstrate the suitability of this system by applying it to a specific medical problem arising in a burns unit.
a novel multilingual report generation system for medical applications. there has been an increasing demand for high quality medical data that are in a standard electronic format and easily shared. although a great amount of effort has been invested to ease the process, an effective solution has yet to be found. in this study, we first discuss necessary features of an effective data collection and reporting system, and then reveal the conceptual view of a novel method that aims to encompass these features. we also present the design and implementation details of a web-based prototype.
subgroup discovery for weight learning in breast cancer diagnosis. in the recent years, there is an increasing interest of the use of case-based reasoning (cbr) in medicine. cbr is an approach to problem solving that is able to use specific knowledge of previous experiences. however, the efficiency of cbr strongly depends on the similarity metrics used to recover past experiences. in such metrics, the role of attribute weights is critical. in this paper we propose a methodology that use subgroup discovery methods to learn the relevance of the attributes. the methodology is applied to a breast cancer dataset obtaining significant improvements. ...
a temporal data mining approach for discovering knowledge on the changes of the patient's physiology. physiological data represent the health conditions of a patient over time. they can be analyzed to gain knowledge on the course of a disease or, more generally, on the physiology of a patient. typical approaches rely on background medical knowledge to track or recognize single stages of the disease. however, when no one domain knowledge is available these approaches become inapplicable. in this paper we describe a temporal data mining approach to acquire knowledge about the possible causes which can trigger particular stages of the disease or, more generally, which can determine changes in the patient's physiology. the analysis is performed in two steps: first, identification of the states of the disease (namely, the stages through which the physiology evolves), then detection of the events which may determine the change from a state to the next one. computational solutions to both issues are presented. the application to the scenario of the sleep disorders allows to discover events, in the form of breathing and cardiovascular disorders, which may trigger particular sleep stages. results are evaluated and discussed.
one telemedical solution in bulgaria. a new arena of healthcare is emerging, because physicians, hospitals, financial health planners and administrators are coming together in a single highly integrated and coordinated virtual health organization.the mission of telemedicine is to provide medical services independently of geographical distances between the involved sites. patients can get access to medical expertise that may not be available at the patients' site through telemedicine. experience over the last decade has shown that the goals of telemedicine are not automatically reached by the introduction and use of particular new technologies per se, but rather require the implementation of integral services and specialized information systems.software teleconsult aims are to provide logistic and telemedical services between two distant hospitals on the territory of bulgaria. the objectives of this development are to combine medicine field, e-health and informatics research so as to share information and experience in order to perform the best patient services at a reasonable price.
modeling clinical guidelines through petri nets. clinical guidelines (gls) play an important role to standardize and organize clinical processes according to evidence-based medicine. several computer-based gl representation languages have been defined, usually focusing on expressiveness and/or on user-friendliness. in many cases, the interpretation of some constructs in such languages is quite unclear. only recently researchers have started to provide a formal semantics for some of such languages, thus providing an unambiguous specification for implementers, and a formal ground in which different approaches can be compared, and verification techniques can be applied. petri nets are a natural candidate formalism to cope with gl semantics, since they are explicitly geared towards the representation of processes, and are paired with powerful verification mechanisms. we show how petri nets can cope with the semantics of gls in a clear way, taking the system glare formalism as a case study.
mining healthcare data with temporal association rules: improvements and assessment for a practical use. the regional healthcare agency (asl) of pavia has been maintaining a central data repository which stores healthcare data about the population of pavia area. the analysis of such data can be fruitful for the assessment of healthcare activities. given the crucial role of time in such databases, we developed a general methodology for the mining of temporal association rules on sequences of hybrid events. in this paper we show how the method can be extended to suitably manage the integration of both clinical and administrative data. moreover, we address the problem of developing an automated strategy for the filtering of output rules, exploiting the taxonomy underlying the drug coding system and considering the relationships between clinical variables and drug effects. the results show that the method could find a practical use for the evaluation of the pertinence of the care delivery flow for specific pathologies.
causal probabilistic modelling for two-view mammographic analysis. mammographic analysis is a difficult task due to the complexity of image interpretation. this results in diagnostic uncertainty, thus provoking the need for assistance by computer decision-making tools. probabilistic modelling based on bayesian networks is among the suitable tools, as it allows for the formalization of the uncertainty about parameters, models, and predictions in a statistical manner, yet such that available background knowledge about characteristics of the domain can be taken into account. in this paper, we investigate a specific class of bayesian networks--causal independence models--for exploring the dependencies between two breast image views. the proposed method is based on a multi-stage scheme incorporating domain knowledge and information obtained from two computer-aided detection systems. the experiments with actual mammographic data demonstrate the potential of the proposed two-view probabilistic system for supporting radiologists in detecting breast cancer, both at a location and a patient level.
steps on the road to clinical application of decision support - example treat. the decision support system treat advices on antibiotic treatment of severe infections. a multicenter randomized clinical trial has demonstrated that treat reduces inappropriate treatment by 50%. this paper will show that treat satisfies several features closely correlated with decision support systems's ability to improve clinical practice. examples of such criteria are: providing recommendations, not just assessments; transparent line of reasoning; convenience in use. additional design features, such as transferability and addressing an important clinical problem, will also be discussed.