waashk/dblp huggingface dataset:
temporal data mining with temporal constraints. nowadays, methods for discovering temporal knowledge try to extract more complete and representative patterns. the use of qualitative temporal constraints can be helpful in that aim, but its use should also involve methods for reasoning with them (instead of using them just as a high level representation) when a pattern consists of a constraint network instead of an isolated constraint.in this paper, we put forward a method for mining temporal patterns that makes use of a formal model for representing and reasoning with qualitative temporal constraints. three steps should be accomplished in the method: 1) the selection of a model that allows a trade off between efficiency and representation; 2) a preprocessing step for adapting the input to the model; 3) a data mining algorithm able to deal with the properties provided by the model for generating a representative output.in order to implement this method we propose the use of the fuzzy temporal constraint network (ftcn) formalism and of a temporal abstraction method for preprocessing. finally, the ideas of the classic methods for data mining inspire an algorithm that can generate ftcns as output.along this paper, we focus our attention on the data mining algorithm.
using temporal context-specific independence information in the exploratory analysis of disease processes. disease processes in patients are temporal in nature and involve uncertainty. it is necessary to gain insight into these processes when aiming at improving the diagnosis, treatment and prognosis of disease in patients. one way to achieve these aims is by explicitly modelling disease processes; several researchers have advocated the use of dynamic bayesian networks for this purpose because of the versatility and expressiveness of this time-oriented probabilistic formalism. in the research described in this paper, we investigate the role of context-specific independence information in modelling the evolution of disease. the hypothesis tested was that within similar populations of patients differences in the learnt structure of a dynamic bayesian network may result, depending on whether or not patients have a particular disease. this is an example of temporal context-specific independence information. we have tested and confirmed this hypothesis using a constraint-based bayesian network structure learning algorithm which supports incorporating background knowledge into the learning process. clinical data of mechanically-ventilated icu patients, some of whom developed ventilator-associated pneumonia, were used for that purpose.
the role of model checking in critiquing based on clinical guidelines. medical critiquing systems criticise clinical actions performed by a physician. in order to provide useful feedback, an important task is to find differences between the actual actions and a set of `ideal' actions as described by a clinical guideline. in case differences exist, insight to which extent they are compatible is provided by the critiquing system. we propose a methodology for such critiquing, where the ideal actions are given by a formal model of a clinical guideline, and where the actual actions are derived from real world patient data. we employ model checking to investigate whether a part of the actual treatment is consistent with the guideline. furthermore, it is shown how critiquing can be cast in terms of temporal logic, and what can be achieved by using model checking. the methodology has been applied to a clinical guideline of breast cancer in conjunction with breast cancer patient data.
procarsur: a system for dynamic prognostic reasoning in cardiac surgery. we present the procarsur system for prognostic reasoning in the domain of cardiac surgery. the system has a three-tiered architecture consisting of a bayesian network, a task layer, and a graphical user interface. in contrast to traditional prognostic tools, that are usually based on logistic regression, procarsur implements a dynamic, process-oriented view on prognosis. the system distinguishes between the various phases of peri-surgical care, explicates the scenarios that lead to different clinical outcomes, and can be used to update predictions when new information becomes available. to support users in their interaction with the bayesian network, a set of predefined prognostic reasoning tasks is implemented in the task layer. the user communicates with the system through an interface that hides the underlying bayesian network and aggregates the results of probabilistic inferences.
segmentation techniques for automatic region extraction: an application to aphasia rehabilitation. we describe a system that facilitates speech therapists to administer cognitive rehabilitation exercises and to evaluate treatment outcomes. we started by augmenting a commercial tool with a more user-friendly interface, meeting the needs of the healthcare professionals involved. then we integrated, into the same tool, a new type of exercise, that is particularly patient-tailored, being based on the recognition of familiar images within a picture (such as a relative, a domestic animal, a home object, etc). segmentation techniques are used to elaborate an input picture and individuate areas including interesting objects, that will be semi-automatically linked to text and sound. the picture and associated information are then stored in the system database and may be subsequently used as objects for the new exercise. any number of images may be elaborated, personalised and stored for each patient. the performance has been tested on voluntary subjects with good results.
nasopharyngeal carcinoma data analysis with a novel bayesian network skeleton learning algorithm. in this paper, we discuss efforts to apply a novel bayesian network (bn) structure learning algorithm to a real world epidemiological problem, namely the nasopharyngeal carcinoma (npc). our specific aims are : (1) to provide a statistical profile of the recruited population, (2) to help indentify the important environmental risk factors involved in npc, and (3) to gain insight on the applicability and limitations of bn methods on small epidemiological data sets obtained from questionnaires. we discuss first the novel bn structure learning algorithm called max-min parents and children skeleton (mmpc) developped by tsamardinos et al. in 2005. mmpc was proved by extensive empirical simulations to be an excellent trade-off between time and quality of reconstruction compared to most constraint based algorithms, especially for the smaller sample sizes. unfortunately, mmpc is unable to deal with datasets containing approximate functional dependencies between variables. in this work, we overcome this problem and apply the new version of mmpc on nasopharyngeal carcinoma data in order to shed some light into the statistical profile of the population under study.
induction of partial orders to predict patient evolutions in medicine. in medicine, prognosis is the task of predicting the probable course and outcome of a disease. questions like, is a patient going to improve?, what is his/her chance of recovery?, and how likely a relapse is? are common and they rely on the concept of state. the feasible states of a disease define a partial order structure with extreme states those of 'cure' and 'death'; improving, recovering, and survival meaning particular transitions between states of the partial order. in spite of this, it is not usual in medicine to find an explicit representation either of the states or of the states partial order for many diseases. on the contrary, the variables (e.g. signs and symptoms) related to a disease and their normality and abnormality values are broadly agreed. here, an inductive algorithm is introduced that generates partial orders from a data matrix containing information about the patient-professional encounters, and the normality functions of each one of these disease variables.
machine learning techniques for decision support in anesthesia. the growing availability of measurement devices in the operating room enables the collection of a huge amount of data about the state of the patient and the doctors' practice during a surgical operation. this paper explores the possibilities of generating, from these data, decision support rules in order to support the daily anesthesia procedures. in particular, we focus on machine learning techniques to design a decision support tool. the preliminary tests in a simulation setting are promising and show the role of computational intelligence techniques in extracting useful information for anesthesiologists.
a novel way of incorporating large-scale knowledge into mrf prior model. based on markov random fields (mrf) theory, bayesian methods have been accepted as an effective solution to overcome the ill-posed problems of image restoration and reconstruction. traditionally, the knowledge in most of prior models is from a simply weighted differences between the pixel intensities within a small local neighborhood, so it can only provide limited prior information for regularization. exploring the ways of incorporating more large-scale knowledge into prior model, this paper proposes an effective approach to incorporate large-scale image knowledge into mrf prior model. and a novel nonlocal prior is put forward. relevant experiments in emission tomography prove that the proposed mrf nonlocal prior is capable of imposing more effective regularization on original reconstructions.
testing careflow process execution conformance by translating a graphical language to computational logic. careflow systems implement workflow concepts in the clinical domain in order to administer, support and monitor the execution of health care services performed by different health care professionals and structures. in this work we focus on the monitoring aspects and propose a solution for the conformance verification of careflow process executions.given a careflow model, we have defined an algorithm capable of translating it to a formal language based on computational logic and abductive logic programming in particular. the main advantage of this formalism lies in its operational proof-theoretic counterpart, which is able to verify the conformance of a given careflow process execution (in the form of an event log) w.r.t. the model.the feasibility of the approach has been tested on a case study related to the careflow process described in the cervical cancer screening protocol.
querying clinical workflows by temporal similarity. the degree of fulfillment of clinical guidelines is considered a key factor when evaluating the quality of a clinical service. guidelines can be seen as processes describing the sequence of activities to be done. consequently, workflow formalisms seem to be a valid approach to model the flow of actions in the guideline and their temporal aspects. the application of a guideline to a specific patient (guideline instance) can be modeled by means of a workflow case. the best (worst) application of a guideline, represented as a reference workflow case, can be used to evaluate the quality of the service, by comparing the optimal case with specific patient instances. on the other hand, the correct application of a guideline to a patient involves the fulfillment of the guideline temporal constraints. thus, the evaluation of the temporal similarity degree between different workflow cases is a key aspect in evaluating health care quality. in this work, we represent a portion of the stroke guideline using a temporal workflow schema and we propose a method to evaluate the temporal similarity between workflow cases. our proposal, based on temporal constraint networks, consists of a linear combination of functions to differentiate intra-task and inter-task temporal distances.
metacode: a lightweight umls mapping tool. in the course of our current research on automatic information extraction from medical electronic literature, we have been facing the need to map big corpora onto the concepts of the umls metathesaurus, both in french and in english. in order to meet our specific needs in terms of processing speed, we have developed a lightweight umls tagger, metacode, that processes large text collections at an acceptable speed, but at the cost of the sophistication of the treatments. in this paper, we describe metacode and evaluate its quality, allowing potential users to balance the gain in speed against the loss in quality.
extracting specific medical data using semantic structures. in this paper, we discuss the architecture, functionality and performance of a medical information extraction system. the system is based on an approach to automatic generation of semantic structures for free-text. using a multiaxial nomenclature (wingert nomenclature) and existing language-engineering technologies, a conceptual graph-like representation is produced for each sentence of a text. these semantic structures are then exploited to extract information. the components that might be adopted for processing texts in another language than german are identified. results of first evaluations of the system's performance in an information extraction (ie) subtask in the medical domain are presented: the filling of selected template slots obtained values of 81- 95% precision and 83-97% recall.
unsupervised documents categorization using new threshold-sensitive weighting technique. as the number of published documents increase quickly, there is a crucial need for fast and sensitive categorization methods to manage the produced information. in this paper, we focused on the categorization of biomedical documents with concepts of the gene ontology, an ontology dedicated to gene description. our approach discovers associations between the predefined concepts and the documents using string matching techniques. the assignations are ranked according to a score computed given several strategies. the effects of these different scoring strategies on the categorization effectiveness are evaluated. more especially a new weighting technique based on term frequency is presented. this new weighting technique improves the categorization effectiveness on most of the experiment performed. this paper shows that a cleaver use of the frequency can bring substantial benefits when performing automatic categorization on large collection of documents.
supporting factors in descriptive analysis of brain ischaemia. this paper analyzes two different approaches to the detection of supporting factors used in descriptive induction. the first is based on the statistical comparison of the pattern properties relative to the properties of the entire negative and the entire positive example sets. the other approach uses artificially generated random examples that are added into the original training set. the methodology is illustrated in the analysis of patients suffering from brain ischaemia.
automatic retrieval of web pages with standards of ethics and trustworthiness within a medical portal: what a page name tells us. the ever-increasing volume of health online information, coupled with the uneven reliability and quality, may have considerable implications for the citizen. in order to address this issue, we propose to use, within a general or specialised search engine, standards for identifying the reliability of online documents. standards used are those related to the ethics as well as trustworthiness of websites. in this research, they are detected through the url names of web pages by applying machine learning algorithms. according to algorithms used and to principles, our straightforward approach shows up to 93% precision and 91% recall. but a few principles remain difficult to recognize.
integrating document-based and knowledge-based models for clinical guidelines analysis. research in the computerization of clinical guidelines (cg) has often opposed document-based approaches to knowledge-based ones. in this paper, we suggest that both approaches can be used simultaneously to assess the contents of textual clinical guidelines. in this first experiment, we investigate the mapping between a document model, which has been marked-up to structure its recommendations, and a knowledge structure representing the management of specific disease. this knowledge representation is based on planning formalisms, more specifically hierarchical task networks (htn). our system operates by first automatically encoding the textual guideline through the identification of specific expressions with surface natural language processing, as described in previous work. in a subsequent step, the htn, constructed manually and independently, and represented as an explicit and/or graph, is searched for a solution sub-graph using an algorithm derived from ao*. whilst the htn is being traversed, corresponding information is accessed in the encoded textual cg, to guide the solution extraction process. we illustrate this through a case study developed around french guidelines for the management of hypertension. recommendations included in the textual guideline provide complementary information for the instantiation of an htn on specific patient data. the mapping takes place at different levels, from the pre-condition of operators to the rules playing a role as selection heuristics when extracting a solution sub-graph. such a process, which explores the textual document from the prospective of a task model, can help analyzing the overall structure of clinical guidelines and ultimately improving its applicability.
predictive modeling of fmri brain states using functional canonical correlation analysis. we present a novel method for predictive modeling of human brain states from functional neuroimaging (fmri) data. extending the traditional canonical correlation analysis of discrete data to the domain of stochastic functional measurements, the method explores the functional canonical correlation between stimuli and fmri training data. via an incrementally steered pattern searching technique, subspaces of voxel time courses are explored to arrive at (spatially distributed) voxel clusters that optimize the relationship between stimuli and fmri in terms of redundancy. application of the method for prediction of naturalistic stimuli from unknown fmri data shows that the method finds highly predictive brain areas, i.e. brain areas relevant in processing the stimuli.
application of cross-language criteria for the automatic distinction of expert and non expert online health documents. distinction between expert and non expert documents is an important issue in the medical area, for instance in the context of information retrieval. in our work we address this issue through stylistic corpus analysis and application of machine learning algorithms. our hypothesis is that this distinction can be observed on the basis of a little number of criteria and that such criteria can be language and domain independent. the used criteria have been acquired in source corpus (russian) and then tested on source and target (french) corpora. the method shows up to 90% precision and 93% recall, and 85% precision and 74% recall in source and target corpora.
variable selection for optimal decision making. this paper discusses variable selection for medical decision making; in particular decisions regarding which treatment to provide a patient. current variable selection methods were designed for use in prediction applications. these techniques often leave behind small but important interaction variables that are critical when the goal is decision making rather than prediction. this paper presents a new method designed to find variables that aid in decision making and demonstrates the method on data from a clinical trial for treatment of depression.
a human-machine cooperative approach for time series data interpretation. this paper deals with the interpretation of biomedical multivariate time series for extracting typical scenarios. this task is known to be difficult, due to the temporal nature of the data at hand, and to the context-sensitive aspect of data interpretation, which hamper the formulation of a priori knowledge about the kind of patterns to detect and their interrelations. a new way to tackle this problem is proposed, based on a collaborative approach between a human and a machine by means of specific annotations. two grounding principles, namely autonomy and knowledge discovery, support the co-construction of successive abstraction levels for data interpretation. a multi-agent system is proposed to implement effectively these two principles. respiratory time series data (flow, paw) have been explored with our system for patient/ventilator asynchronies characterization studies.
semantic web framework for knowledge-centric clinical decision support systems. lately, there have been considerable efforts to computerize clinical practice guidelines (cpg) so that they can be executed via clinical decision support systems (cdss) at the point of care. we present a semantic web framework to both model and execute the knowledge within a cpg to develop knowledge-centric cdss. our approach entails knowledge modeling through a synergy between multiple ontologies---i.e. a domain ontology, cpg ontology and patient ontology. we develop decision-rules based on the ontologies, and execute them with a proof engine to derive cpg-based patient specific recommendations. we present a prototype of our cpg-based cdss to execute the cpg for follow-up after treatment for breast cancer.
an ontology-driven agent-based clinical guideline execution engine. one of the hardest tasks in any healthcare application is the management of knowledge. organisational information as well as medical concepts should be represented in an appropriate way in order to improve interoperability among existing systems, to allow the implementation of knowledge-based intelligent systems, or to provide high level support to healthcare professionals. this paper proposes the inclusion of an especially designed ontology into an agent-based medical platform called hecase2. the ontology has been constructed as an external resource, allowing agents to coordinate complex activities defined in any clinical guideline.
formalizing 'living guidelines' using lassie: a multi-step information extraction method. living guidelines are documents presenting up-to-date and state-of-the-art knowledge to practitioners. to have guidelines implemented by computer-support they firstly have to be formalized in a computer-interpretable form. due to the complexity of such formats the formalization process is challenging, but burdensome and time-consuming.the lassie methodology supports this task by formalizing guidelines in several steps from the textual form to the guideline representation language asbru using a document-centric approach. lassie uses information extraction technique to semi-automatically accomplish these steps.we apply lassie to support the implementation of living guidelines. based on a living guideline published by the scottish intercollegiate guidelines network (sign) we show that adaptations of previously formalized guidelines can be accomplished easily and fast. by using this new approach only new and changed text parts have to be modeled. furthermore, models can be inherited from previously modeled guideline versions that were added by domain experts.
multi-level clustering in sarcoidosis: a preliminary study. sarcoidosis is a multisystem disorder that is characterized by the formation of granulomas in certain organs of the body. the exact cause of sarcoidosis is unknown but evidence exists that sarcoidosis results from exposure of genetically susceptible hosts to specific environmental agents. the wide degree of clinical heterogeneity might indicate that sarcoidosis is not a single polymorphic disease but a collection of genetically complex diseases. as a first step to identify the hypothesized subcategories, large amounts of multidimensional data are collected that are divided into distinct levels. we investigated how clustering techniques can be applied to support the interpretation of sarcoidosis and subsequently to reveal categories of sarcoidosis data. an attempt is made to relate multiple clusters between the different data levels based on validation criteria.