waashk/dblp huggingface dataset:
an ontology-based method to link database integration and data mining within a biomedical distributed kdd. over the last years, collaborative research has been continuously growing in many scientific areas such as biomedicine. however, traditional knowledge discovery in databases (kdd) processes generally adopt centralized approaches that do not fully address many research needs in these distributed environments. this paper presents a method to improve traditional centralized kdd by adopting an ontology-based distributed model. ontologies are used within this model: (i) as virtual schemas (vs) to solve structural heterogeneities in databases and (ii) as frameworks to guide automatic transformations when data is retrieved by users--preprocessing ontologies (po). both types of ontologies aim to facilitate data gathering and preprocessing while maintaining data source decentralization. this ontology-based approach allows to link database integration and data mining, improving final results, reusability and interoperability. the results obtained present improvements in outcome performance and new capabilities compared to traditional kdd processes.
the role of biomedical dataset in classification. in this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. we quantify the complexity of a biomedical dataset using five complexity measures: correlation-based feature selection subset merit, noise, imbalance ratio, missing values and information gain. the effect of these complexity measures on classification accuracy is evaluated using five diverse machine learning algorithms: j48 (decision tree), smo (support vector machines), naive bayes (probabilistic), ibk (instance based learner) and jrip (rule-based induction). the results of our experiments show that noise and correlation-based feature selection subset merit --- not a particular choice of algorithm --- play a major role in determining the classification accuracy. in the end, we provide researchers with a meta-model and an empirical equation to estimate the classification potential of a dataset on the basis of its complexity. this well help researchers to efficiently pre-process the dataset for automatic knowledge extraction.
coraal - towards deep exploitation of textual resources in life sciences. prominent biomedical literature search tools like sciencedirect, pubmed central or medline allow for efficient retrieval of resources based on key words. due to vast amounts of data available in life sciences, key word search is not always sufficient, though. one would often welcome more intelligent search for knowledge, i.e., for concepts and their mutual relations. this is, however, still a major challenge, since getting the necessary machine-readable knowledge manually is virtually impossible in large scale, while its automatic extraction is not particularly reliable. we have researched a novel framework actually enabling practical exploitation of automatically extracted knowledge, though. on the top of the framework, we implemented coraal, a prototype for knowledge-based biomedical literature search. this paper describes its essential principles, innovative capabilities and current results.
a mutual information approach to data integration for alzheimer's disease patients. clinical data alignment plays a critical role in identifying important features for significant experiments. a central problem is data fusion i.e., how to correctly integrate data provided by different labs. this integration is done in order to increase ability of inferring target classes of controls and patients. our paper proposes an approach based both on a information theoretic perspective, generally used in a feature construction problem [3] and on the approximated solution for a mathematical programming task (i.e. the weighted bipartite matching problem [6]). numerical evaluations with two competitive approaches show the improved performance of the proposed method. for this evaluation we used data sets from plasma / ethylenediaminetetraacetic acid (edta) of controls and alzheimer patients collected in three different hospitals.
computer vision: a plea for a constructivist view. computer vision is presented and discussed under two complementary views. the positivist view provides a formal background under which vision is approached as a problem-solving task. by contrast, the constructivist view considers vision as the opportunistic exploration of a realm of data. the former view is rather well supported by evidence in neurophysiology while the latter view rather relies on recent trends in the field of distributed and situated cognition. the notion of situated agent is presented as a way to design computer vision systems under a constructivist hypothesis. various applications in the medical domain are presented to support the discussion.
implementing a clinical decision support system for glucose control for the intensive cardiac care. adherence to guidelines and protocols in clinical practice can be difficult to achieve. we describe the implementation of a clinical decision support system (cdss) for glucose control on the intensive cardiac care unit (iccu) of the erasmus mc. an existing paper protocol for glucose control was used for the cdss rule set. in the first phase we implemented a proof of concept of a cdss: a web 2.0 ajax-driven web screen, which resulted in an improved adherence to the glucose guideline. this paper will reflect on the technical implementations and challenges of our experience with this process. the end product will allow: storage of guidelines in a shareable and uniform matter, presentation of guidelines in a more clear way to physicians, a more flexible platform to maintain guidelines, the ability to adjust guidelines to incorporate changes based on collected evidence from the cdss and/or literature review, and be able to better review the outcome.
using temporal constraints to integrate signal analysis and domain knowledge in medical event detection. the events which occur in an intensive care unit (icu) are many and varied. very often, events which are important to an understanding of what has happened to the patient are not recorded in the electronic patient record. this paper describes an approach to the automatic detection of such unrecorded 'target' events which brings together signal analysis to generate temporal patterns, and temporal constraint networks to integrate these patterns with other associated events which are manually or automatically recorded. this approach has been tested on real data recorded in a neonatal icu with positive results.
explaining anomalous responses to treatment in the intensive care unit. the intensive care unit (icu) provides treatment to critically ill patients. when a patient does not respond as expected to such treatment it can be challenging for clinicians, especially junior clinicians, as they may not have the relevant experience to understand the patient's anomalous response. datasets for 10 patients from glasgow royal infirmary's icu have been made available to us. we asked several icu clinicians to review these datasets and to suggest sequences which include anomalous or unusual reactions to treatment. further, we then asked two icu clinicians if they agreed with their colleagues' assessments, and if they did to provide possible explanations for these anomalous sequences. subsequently we have developed a system which is able to replicate the clinicians' explanations based on the knowledge contained in its several ontologies; further the system can suggest additional explanations which will be evaluated by the senior consultant.
goal-based decisions for dynamic planning. the need for clinical guidelines to be implemented at different sites, to adapt to rapidly changing environments, and to be carried out by distributed clinical teams, implies a degree of flexibility beyond that of current guideline languages. we propose an extension to the proforma language allowing hierarchical goal-based plans. sub-plans to achieve goals are proposed at runtime so that changing circumstances may be flexibly accommodated without redefining the workflow.
histopathology image classification using bag of features and kernel functions. image representation is an important issue for medical image analysis, classification and retrieval. recently, the bag of features approach has been proposed to classify natural scenes, using an analogy in which visual features are to images as words are to text documents. this process involves feature detection and description, construction of a visual vocabulary and image representation building through visual-word occurrence analysis. this paper presents an evaluation of different representations obtained from the bag of features approach to classify histopathology images. the obtained image descriptors are processed using appropriate kernel functions for support vector machines classifiers. this evaluation includes extensive experimentation of different strategies, and analyses the impact of each configuration in the classification result.
segmentation of text and non-text in on-line handwritten patient record based on spatio-temporal analysis. note taking is a common way for physicians to collect information from their patients in medical inquiries and diagnoses. many times, when describing the pathology in medical records, a physician also draws diagrams and/or anatomical sketches along with the free-text narratives. the ability to understand unstructured handwritten texts and drawings in patient record could lead to implementation of automated patient record systems with more natural interfaces than current highly structured systems. the first and crucial step in automated processing of free-hand medical records is to segment the record into handwritten text and drawings, so that appropriate recognizers can be applied to different regions. this paper presents novel algorithms that separate text from non-text strokes in an on-line handwritten patient record. the algorithm is based on analyses of spatio-temporal graphs extracted from an on-line patient record and support vector machine (svm) classification. experiments demonstrate that the proposed approach is effective and robust.
an ambient intelligent agent for relapse and recurrence monitoring in unipolar depression. mental healthcare is a prospective area for applying ai techniques. for example, a computerized system could support individuals with a history of depression in maintaining their well-being throughout their lifetime. in this paper, the design of an ambient intelligent agent to support these individuals is presented. it incorporates an analysis and support model for diagnostics based on observed features and for suggested actions. the model used is based on dynamic relations that describe the occurrence of relapse in unipolar depression. by incorporating this model into an ambient agent system, the agent is able to reason about the state of the human and the effect of possible actions. several simulation experiments have been conducted to illustrate the functioning of the proposed model in different scenarios.
optimization of online patient scheduling with urgencies and preferences. we consider the online problem of scheduling patients with urgencies and preferences on hospital resources with limited capacity. to solve this complex scheduling problem effectively we have to address the following sub problems: determining the allocation of capacity to patient groups, setting dynamic rules for exceptions to the allocation, ordering timeslots based on scheduling efficiency, and incorporating patient preferences over appointment times in the scheduling process. we present a scheduling approach with optimized parameter values that solves these issues simultaneously. in our experiments, we show how our approach outperforms standard scheduling benchmarks for a wide range of scenarios, and how we can efficiently trade-off scheduling performance and fulfilling patient preferences.
conversational case-based reasoning in medical classification and diagnosis. in case-based reasoning (cbr) approaches to classification and diagnosis, a description of the problem to be solved is often assumed to be available in advance. conversational cbr (ccbr) is a more interactive approach in which the system is expected to play an active role in the selection of relevant tests to help minimize the number of problem features that the user needs to provide. we present a new algorithm for ccbr called inn(k) and demonstrate its ability to achieve high levels of accuracy on a selection of datasets related to medicine and health care, while often requiring the user to provide only a small subset of the problem features required by a standard k-nn classifier. another important benefit of inn(k) is a goal-driven approach to feature selection that enables a ccbr system to explain the relevance of any question it asks the user in terms of its current goal.
data-efficient information-theoretic test selection. we use the concept of conditional mutual information (mi) to approach problems involving the selection of variables in the area of medical diagnosis. computing mi requires estimates of joint distributions over collections of variables. however, in general computing accurate joint distributions conditioned on a large set of variables is expensive in terms of data and computing power. therefore, one must seek alternative ways to calculate the relevant quantities and still use all the available observations. we describe and compare a basic approach consisting of averaging mi estimates conditioned on individual observations and another approach where it is possible to condition on all observations at once by making some conditional independence assumptions. this yields a data-efficient variant of information maximization for test selection. we present experimental results on public heart disease data and data from a controlled study in the area of breast cancer diagnosis.
predicting the need to perform life-saving interventions in trauma patients by using new vital signs and artificial neural networks. previous work in risk stratification of critically injured patients involved artificial neural networks (anns) of various configurations tuned to process traditional vital signs and demographical, clinical, and laboratory data obtained via direct contact with the patient. we now report "new vital signs" (nvss) that are superior in distinguishing the injured and can be derived without hands-on patient contact. data from 262 trauma patients are presented, in whom nvss derived from electrocardiogram (ekg) analysis (heart-rate complexity and variability) were input into a commercially available ann. the endpoint was performance of life-saving interventions (lsis) such as intubation, cardiopulmonary resuscitation, chest-tube placement, needle chest decompression, and blood transfusion. we conclude that based on ekg-derived nvs alone, it is possible to accurately identify trauma patients who undergo lsis. our approach may permit development of a next-generation decision support system.
analysing clinical guidelines' contents with deontic and rhetorical structures. the computerisation of clinical guidelines can greatly benefit from the automatic analysis of their content using natural language processing techniques. because of the central role played by specific deontic structures, known as recommendations, it is possible to tune the processing step towards the recognition of such expressions, which can be used to structure key sections of the document. in this paper, we extend previous work on the automatic identification of guidelines' recommendations, by showing how rhetorical structure theory can be used to characterise the actual contents of elementary recommendations. the emphasis on causality and time in rst proves a powerful complement to the recognition of deontic structures and supports the identification of relevant knowledge, in particular for the identification of conditional structures, which play an important role for the subsequent analysis of recommendations.
using existing biomedical resources to detect and ground terms in biomedical literature. we present an approach towards the automatic detection of names of proteins, genes, species, etc. in biomedical literature and their grounding to widely accepted identifiers. the annotation is based on a large term list that contains the common expression of the terms, a normalization step that matches the terms with their actual representation in the texts, and a disambiguation step that resolves the ambiguity of matched terms. we describe various characteristics of the terms found in existing term resources and of the terms that are used in biomedical texts. we evaluate our results against a corpus of manually annotated protein mentions and achieve a precision of 57% and recall of 72%.
integrating healthcare knowledge artifacts for clinical decision support: towards semantic web based healthcare knowledge morphing. healthcare decision making demands the systematic integration of knowledge from multiple sources, such as clinical guidelines, clinical pathways, knowledge of practitioners and so on. we present a semantic web based approach for synthesizing health knowledge through the semantic modeling of healthcare knowledge as ontologies and reasoning over the ontologies to derive a morphed knowledge object. we demonstrate the application of our approach by generating morphed knowledge about prostate cancer clinical pathways.
discovering novel adverse drug events using natural language processing and mining of the electronic health record. this talk presents an overview of our research in use of medical knowledge, natural language processing, the electronic health record, and statistical methods to automatically discover novel adverse drug events, which are serious problems world-wide.
mining discriminant sequential patterns for aging brain. discovering new information about groups of genes implied in a disease is still challenging. microarrays are a powerful tool to analyse gene expression. in this paper, we propose a new approach outlining relationships between genes based on their ordered expressions. our contribution is twofold. first, we propose to use a new material, called sequential patterns, to be investigated by biologists. secondly, due to the expression matrice density, extracting sequential patterns from microarray datasets is far away from being easy. the aim of our proposal is to provide the biological experts with an efficient approach based on discriminant sequential patterns. results of various experiments on real biological data highlight the relevance of our proposal.
analysis of eeg epileptic signals with rough sets and support vector machines. epilepsy is a common chronic neurological disorder that impacts over 1% of the population. animal models are used to better understand epilepsy, particularly the mechanisms and the basis for better antiepileptic therapies. for animal studies, the ability to identify accurately seizures in electroencephalographic (eeg) recordings is critical, and the use of computational tools is likely to play an important role. electrical recording electrodes were implanted in rats before kainate-induced status epilepticus (one in each hippocampus and one on the surface of the cortex), and eeg data were collected with radio-telemetry. several data mining methods, such as wavelets, ffts, and neural networks, were used to develop algorithms for detecting seizures. rough sets, which were used as an additional feature selection technique in addition to the daubechies wavelets and the ffts, were also used in the detection algorithm. compared with the seizure-at-once method by using the rbf neural network classifier used earlier on the same data [12], the new method achieved higher recognition rates (i.e., 91%). furthermore, when the entire dataset was used, as compared to only 50% used earlier, preprocessing using wavelets, principal component analysis, and rough sets in concert with support vector machines resulted in accuracy of 94% in identifying epileptic seizures.
modelling screening mammography images: a probabilistic relational approach. computer-aided detection systems have as aim the increase of detection rates when analysing mammograms, by identifying features that are characteristic for breast cancer. in this research we aimed at using the features extracted from mammographic images in order to analyse the development of suspicious lesions. different from other approaches, we based our data modelling on object orientation. this allowed not only for a description of domain entities and their intrinsic relationships, but also for the application of relational probabilistic techniques, which can handle heterogeneous data instances both in terms of learning and inference.
detecting intuitive mentions of diseases in narrative clinical text. a significant portion of the clinical information content of narrative text documents in the medical record is only mentioned intuitively, but automated information extraction systems typically focus on explicitly mentioned concepts only. to extend the extraction of clinical information to intuitively mentioned diseases, we have developed a natural language processing application based on mmtx and on context analysis algorithms, enhanced with the detection of disease-specific concepts (e.g. medications used only for this disease), and values of some specific biomarkers. this application was developed for the i2b2 obesity challenge, a competition focused on the detection of patients with obesity or common comorbidities.
learning approach to analyze tumour heterogeneity in dce-mri data during anti-cancer treatment. the paper proposes a learning approach to support medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of dynamic contrast-enhanced mri (dce-mri) data. tumour heterogeneity is characterized by identifying regions with different vascular perfusion. the overall aim is to measure volume differences of such regions for two experimental groups: the treated group, to which an anticancer therapy is administered, and a control group. the proposed approach is based on a three-steps procedure: (i) robust features extraction from raw time-intensity curves, (ii) sample-regions identification manually traced by medical researchers on a small portion of input data, and (iii) overall segmentation by training a support vector machine (svm) to classify the mri voxels according to the previously identified cancer areas. in this way a non-invasive method for the analysis of the treatment efficacy is obtained as shown by the promising results reported in our experiments.
mining safety signals in spontaneous reports database using concept analysis. in pharmacovigilance, linking the adverse reactions by patients to drugs they took is a key activity typically based on the analysis of patient reports. yet generating potentially interesting pairs (drug, reaction) from a record database is a complex task, especially when many drugs are involved. to limit the generation effort, we exploit the frequently occurring patterns in the database and form association rules on top of them. moreover, only rules of minimal premise are considered as output by concept analysis tools, which are then filtered through standard measures for statistical significance. we illustrate the process on a small database of anti-hiv drugs involved in the haart therapy while larger-scope validation within the database of the french medicines agency is also reported.