waashk/dblp huggingface dataset:
online prediction of ovarian cancer. in this paper we apply computer learning methods to the diagnosis of ovarian cancer using the level of the standard biomarker ca125 in conjunction with information provided by mass spectrometry. our algorithm gives probability predictions for the disease. to check the power of our algorithm we use it to test the hypothesis that ca125 and the peaks do not contain useful information for the prediction of the disease at a particular time before the diagnosis. it produces p-values that are less than those produced by an algorithm that has been previously applied to this data set. our conclusion is that the proposed algorithm is especially reliable for prediction the ovarian cancer on some stages.
subgroup discovery in data sets with multi-dimensional responses: a method and a case study in traumatology. biomedical experimental data sets may often include many features both at input (description of cases, treatments, or experimental parameters) and output (outcome description). state-of-the-art data mining techniques can deal with such data, but would consider only one output feature at the time, disregarding any dependencies among them. in the paper, we propose the technique that can treat many output features simultaneously, aiming at finding subgroups of cases that are similar both in input and output space. the method is based on k-medoids clustering and analysis of contingency tables, and reports on case subgroups with significant dependency in input and output space. we have used this technique in explorative analysis of clinical data on femoral neck fractures. the subgroups discovered in our study were considered meaningful by the participating domain expert, and sparked a number of ideas for hypothesis to be further experimentally tested.
automatic detecting documents containing personal health information. with the increasing usage of computers and internet, personal health information (phi) is distributed across multiple institutes and often scattered on multiple devices and stored in diverse formats. non-traditional medical records such as emails and e-documents containing phi are in a high risk of privacy leakage. we are facing the challenges of locating and managing phi in the distributed environment. the goal of this study is to classify electronic documents into phi and non-phi. a supervised machine learning method was used for this text categorization task. three classifiers: svm, decision tree and naive bayesian were used and tested on three data sets. lexical, semantic and syntactic features and their combinations were compared in terms of their effectiveness of classifying phi documents. the results show that combining semantic and/or syntactic with lexical features is more effective than lexical features alone for phi classification. the supervised machine learning method is effective in classifying documents into phi and non-phi.
a mobile clinical decision support system for clubfoot treatment. in current congenital clubfoot treatment, clinicians use paper forms to register and monitor the treatment process. routines for registration and archiving are scarce, and the guideline for treating clubfoot is not always followed strictly. this paper presents a pda-based system (gensupport) that can support the registration of patient information, supervise the treatment process, as well as provide advice during treatment. gensupport has been evaluated in a pilot study.
feasibility of case-based beam generation for robotic radiosurgery. robotic radiosurgery uses the kinematic flexibility of a robotic arm to target tumors and lesions from many different directions. this approach allows to focus the dose to the target region while sparing healthy surrounding tissue. however, the flexibility in the placement of treatment beams is also a challenge during treatment planning. so far, a randomized beam generation heuristic has been proven to be most robust in clinical practice. yet, for prevalent types of cancer similarities in patient anatomy and dose prescription exist. we propose a case-based method to solve the planning problem for a new patient by adapting beam sets from successful previous treatments. preliminary experimental results indicate that the novel method could lead to faster treatment planning.
mealtime blood glucose classifier based on fuzzy logic for the diabtel telemedicine system. the accurate interpretation of blood glucose (bg) values is essential for diabetes care. however, bg monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. an automatic classification of incomplete bg data helps to a more accurate interpretation, contributing to knowledge management (km) tools that support decision-making in a telemedicine system. this work presents a fuzzy rule-based classifier integrated in a km agent of the diabtel telemedicine architecture, to automatically classify bg measurements into meal intervals and moments of measurement. fuzzy logic (fl) tackles with the incompleteness of bg measurements and provides a semantic expressivity quite close to natural language used by physicians, what makes easier the system output interpretation. the best mealtime classifier provides an accuracy of 77.26% and does not increase significantly the km analysis times. results of classification are used to extract anomalous trends in the patient's data.
a system for the acquisition, interactive exploration and annotation of stereoscopic images. we present in the paper a system that integrates all hardware and software to extract information from 3d images of skin. it is composed of a lighting equipment and stereoscopic cameras, a camera calibration algorithm that uses evolutionary principles, virtual reality equipment to visualize the images and interact with them in 3d, a set of interactive features to annotate images, to create links between them and to build a 3d hypermedia. we present an experimental study and an application of our tool on faces skin.
effect of background correction on cancer classification with gene expression data. this paper empirically compares six background correction methods aimed at removing unspecific background noise of the overall signal level measured by a scanner across microarrays. using three published cdna microarray datasets we investigated the effect of background correction on cancer classification in terms of the predictive performance of two classifiers (k-nn and support vector machine with linear kernel) induced from microarray data where a particular background correction method is applied, individually and in combination with a single-bias or double-bias-removal normalization method.
a framework for multi-class learning in micro-array data analysis. a large pool of techniques have already been developed for analyzing micro-array datasets but less attention has been paid on multi-class classification problems. in this context, selecting features and quantify classifiers may be hard since only few training examples are available in each single class. this paper demonstrates a framework for multi-class learning that considers learning a classifier within each class independently and grouping all relevant features in a single dataset. next step, that dataset is presented as input to a classification algorithm that learns a global classifier across the classes. we analyze two micro-array datasets using the proposed framework. results demonstrate that our approach is capable of identifying a small number of influential genes within each class while the global classifier across the classes performs better than existing multi-class learning methods.
prediction of mechanical lung parameters using gaussian process models. mechanical ventilation can cause severe lung damage by inadequate adjustment of the ventilator. we introduce a machine learning approach to predict the pressure-dependent, non-linear lung compliance, a crucial parameter to estimate lung protective ventilation settings. features were extracted by fitting a generally accepted lumped parameter model to time series data obtained from ards (adult respiratory distress syndrome) patients. numerical prediction was performed by use of gaussian processes, a probabilistic, non-parametric modeling approach for non-linear functions.
a hybrid approach to clinical guideline and to basic medical knowledge conformance. several computer-based approaches to clinical guidelines have been developed in the last two decades. however, only recently the community has started to cope with the fact that clinical guidelines are just a part of the medical knowledge that physicians have to take into account when treating patients. the procedural knowledge in the guidelines have to be complemented by additional declarative medical knowledge. in this paper, we analyse such an interaction, by studying the conformance problem, defined as evaluating the adherence of a set of performed clinical actions w.r.t. the behaviour recommended by the guideline and by the medical knowledge.
temporal data mining of hiv registries: results from a 25 years follow-up. the human immunodeficiency virus (hiv) causes a pandemic infection in humans, with millions of people infected every year. although the highly active antiretroviral therapy reduced the number of aids cases since 1996 by significantly increasing the disease-free survival time, the therapy failure rate is still high due to hiv treatment complexity. to better understand the changes in the outcomes of hiv patients we have applied temporal data mining techniques to the analysis of the data collected since 1981 by the infectious diseases unit of the hospital clínic in barcelona, spain. we run a precedence temporal rule extraction algorithm on three different temporal periods, looking for two types of treatment failures: viral failure and toxic failure, corresponding to events of clinical interest to assess the treatment outcomes. the analysis allowed to extract different typical patterns related to each period and to meaningfully interpret the previous and current behaviour of hiv therapy.
ontology-based personalization and modulation of computerized cognitive exercises. cognitive rehabilitation may benefit from computer-based approaches that, with respect to paper-based ones, allow managing big amounts of stimuli (images, sounds, written texts) and combining them to create ever-new exercises. moreover, they allow storing and analysing patients' performance, that may vary in time, thus increasing/decreasing difficulty of the exercises accordingly. an ontological organisation of the stimuli may help to automatically generate patient-tailored exercises, accounting for patients' performance, skills and preferences.
analyzing critical process models through behavior model synthesis. process models capture tasks performed by agents together with their control flow. building and analyzing such models is important but difficult in certain areas such as safety-critical healthcare processes. tool-supported techniques are needed to find and correct flaws in such processes. on another hand, event-based formalisms such as labeled transition systems (lts) prove effective for analyzing agent behaviors.
using quantitative analysis to implement autonomic it systems. the software underpinning today's it systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. we describe a software framework that achieves such adaptiveness for it systems whose components can be modelled as markov chains. the framework comprises (i) an autonomic architecture that uses markov-chain quantitative analysis to dynamically adjust the parameters of an it system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively.
a case-study on using an automated in-process software engineering measurement and analysis system in an industrial environment. automated systems for measurement and analysis are not adopted on a large scale in companies, despite the opportunities they offer. the fear of the “big brother” and the lack of reports giving insights into the real adoption process and concrete usages in industry are barriers to this adoption. we report on a case-study on the adoption and long-term usage (2 years of running system) of such a system in a company focusing on the adoption process and the related challenges we encountered.
taming dynamically adaptive systems using models and aspects. since software systems need to be continuously available under varying conditions, their ability to evolve at runtime is increasingly seen as one key issue. modern programming frameworks already provide support for dynamic adaptations. however the high-variability of features in dynamic adaptive systems (das) introduces an explosion of possible runtime system configurations (often called modes) and mode transitions. designing these configurations and their transitions is tedious and error-prone, making the system feature evolution difficult. while aspect-oriented modeling (aom) was introduced to improve the modularity of software, this paper presents how an aom approach can be used to tame the combinatorial explosion of das modes. using aom techniques, we derive a wide range of modes by weaving aspects into an explicit model reflecting the runtime system. we use these generated modes to automatically adapt the system. we validate our approach on an adaptive middleware for home-automation currently deployed in rennes metropolis.
predicting faults using the complexity of code changes. predicting the incidence of faults in code has been commonly associated with measuring complexity. in this paper, we propose complexity metrics that are based on the code change process instead of on the code. we conjecture that a complex code change process negatively affects its product, i.e., the software system. we validate our hypothesis empirically through a case study using data derived from the change history for six large open source projects. our case study shows that our change complexity metrics are better predictors of fault potential in comparison to other well-known historical predictors of faults, i.e., prior modifications and prior faults.
semdiff: analysis and recommendation support for api evolution. as a framework evolves, changes in its application programming interface (api) can break client programs that extend the framework. repairing a client program can be a challenging task because developers need to understand the context surrounding the api change. this paper describes semdiff, a tool that recommends replacements for framework methods that were accessed by a client program and deleted during the evolution of the framework. semdiff recommends replacements for non-trivial changes undiscovered by other change-detection techniques and also enables developers to look at the context of the changes that led to the deletion of a framework method.
feedback-driven requirements engineering: the heuristic requirements assistant. the complexity of today's software systems is constantly increasing. as a result, requirements for these systems become more comprehensive and complicated. in this setting, requirements engineers struggle to capture consistent and complete requirements of high quality. we propose a feedback-centric requirements editor to help analysts controlling the information overload. our hera tool provides analysts with important data from various feedback facilities. the feedback is directly given based on the input to the editor. on the one hand, it is based on heuristic rules, on the other hand, on automatically derived models. thus, when new requirements are added, the analyst gets important information on how consistent these requirements are with the existing ones.
early aspects at icse 2009: workshop on aspect-oriented requirements engineering and architecture design. the “early aspects @ icse'09” is the 15th edition of the workshops on early aspects. early aspects focuses on the systematic identification, modularization, representation and composition of crosscutting concerns, and their impact at the requirements engineering and architecture derivation activities. the primary aim of the present workshop is to continue the maturation of early aspects as a discipline in order to identify the problems and potential solutions in requirements engineering, domain engineering and software architecture design. the present edition of the workshop provides a forum for an open set of early-aspects related topics, without restricting to a specific theme or domain.
synthesis of timed behavior from scenarios in the fujaba real-time tool suite. based on a well-defined component architecture the tool supports the synthesis of so-called real-time statecharts from timed sequence diagrams. the two step synthesis process addresses the existing scalability problems by a proper decomposition and allows the user to define particular restrictions on the resulting statecharts.
model evolution by run-time parameter adaptation. models can help software engineers to reason about design-time decisions before implementing a system. this paper focuses on models that deal with non-functional properties, such as reliability and performance. to build such models, one must rely on numerical estimates of various parameters provided by domain experts or extracted by other similar systems. unfortunately, estimates are seldom correct. in addition, in dynamic environments, the value of parameters may change over time. we discuss an approach that addresses these issues by keeping models alive at run time and feeding a bayesian estimator with data collected from the running system, which produces updated parameters. the updated model provides an increasingly better representation of the system. by analyzing the updated model at run time, it is possible to detect or predict if a desired property is, or will be, violated by the running implementation. requirement violations may trigger automatic reconfigurations or recovery actions aimed at guaranteeing the desired goals. we illustrate a working framework supporting our methodology and apply it to an example in which a web service orchestrated composition is modeled through a discrete time markov chain. numerical simulations show the effectiveness of the approach.
lightweight fault-localization using multiple coverage types. lightweight fault-localization techniques use program coverage to isolate the parts of the code that are most suspicious of being faulty. in this paper, we present the results of a study of three types of program coverage—statements, branches, and data dependencies—to compare their effectiveness in localizing faults. the study shows that no single coverage type performs best for all faults—different kinds of faults are best localized by different coverage types. based on these results, we present a new coverage-based approach to fault localization that leverages the unique qualities of each coverage type by combining them. because data dependencies are noticeably more expensive to monitor than branches, we also investigate the effects of replacing data-dependence coverage with an approximation inferred from branch coverage. our empirical results show that (1) the cost of fault localization using combinations of coverage is less than using any individual coverage type and closer to the best case (without knowing in advance which kinds of faults are present), and (2) using inferred data-dependence coverage retains most of the benefits of combinations.
vida: visual interactive debugging. software debugging is time-consuming and effort-consuming. although software debugging, especially fault-localization, has been studied for long, few practical debugging tools have been developed and used by the industry. in this paper we present vida, a visual interactive debugging tool, which has been integrated with the eclipse integrated development environment to support a programmer's debugging process. during the programmer's conventional debugging process, vida continuously recommends break-points for the programmer based on the analysis of execution information and the gathered feedback from the programmer. moreover, vida provides a program outline to help the programmer choose breakpoints and visualizes the static dependency relation to help the programmer make estimation at breakpoints.
itaca: an integrated toolbox for the automatic composition and adaptation of web services. adaptation is of utmost importance in systems developed by assembling reusable software services accessed through their public interfaces. this process aims at solving, as automatically as possible, mismatch cases which may be given at the different interoperability levels among interfaces by synthesizing a mediating adaptor. in this paper, we present a toolbox that fully supports the adaptation process, including: (i) different methods to construct adaptation contracts involving several services; (ii) simulation and verification techniques which help to identify and correct erroneous behaviours or deadlocking executions; and (iii) techniques for the generation of centralized or distributed adaptor protocols based on the aforementioned contracts. our toolbox relates our models with implementation platforms, starting with the automatic extraction of behavioural models from existing interface descriptions, until the final adaptor implementation is generated for the target platform.
reman: a pro-active reputation management infrastructure for composite web services. reman is a reputation management infrastructure for composite web services. it supports the aggregation of client feedback on the perceived qos of external services, using reputation mechanisms to build service rankings. changes in rankings are pro-actively notified to composite service clients to enable self-tuning properties in their execution.
mining exception-handling rules as sequence association rules. programming languages such as java and c++ provide exception-handling constructs to handle exception conditions. applications are expected to handle these exception conditions and take necessary recovery actions such as releasing opened database connections. however, exception-handling rules that describe these necessary recovery actions are often not available in practice. to address this issue, we develop a novel approach that mines exception-handling rules as sequence association rules of the form “(fc1c1…fccn) ∧ fca ⇒ (fce1…fcem)”. this rule describes that function call fca should be followed by a sequence of function calls (fce1…fcem) when fca is preceded by a sequence of function calls (fce1…fccn). such form of rules is required to characterize common exception-handling rules. we show the usefulness of these mined rules by applying them on five real-world applications (including 285 kloc) to detect violations in our evaluation. our empirical results show that our approach mines 294 real exception-handling rules in these five applications and also detects 160 defects, where 87 defects are new defects that are not found by a previous related approach.