A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Morphological evolution on the surface of hydrothermally synthesized hydroxyapatite microspheres in the presence of EDTMP

Well-ordered and surface engineered hierarchical hydroxyapatite microspheres (HAM) were prepared via a template free hydrothermal process. Ethylene diamine tetra (methylene phosphonic acid) (EDTMP) was used as chelating or regulating agent for the first time in this study. The results indicated the formation of sheet-like particles in the absence of EDTMP. On the other hand, microspheres with radially grown nanorods (HAMNR) or nanosheets (HAMNS) on the surface were obtained (with average diameter of 5?µm) in the presence of EDTMP. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy were used to characterize the crystalline phases in the synthesized samples. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that EDTMP concentration played an important part in regulating the morphology to form well organized microspheres with nanosheets or nanorods on the surface. Brunauer-Emmett-Teller (BET) revealed an increase in the specific surface area with the change in morphology from the HAMNS to HAMNR. Possible mechanisms are proposed to account for the formation of different morphologies based upon thermodynamic and kinetic theories.     

Molecular follow-up of disease progression and interferon therapy in chronic myelocytic leukemia

We previously reported that the abl promoter (Pa) undergoes de novo DNA methylation in the course of chronic myelocytic leukemia (CML). The clinical implications of this finding are the subject of the present study in which samples of CML patients, including a group treated with interferon alpha (IFNalpha) were surveyed. The methylation status of the abl promoter was monitored by polymerase chain reaction (PCR) amplification of the Pa region after digestion with several site-methylation sensitive restriction enzymes. Some 74% of the DNA samples from blood and marrow drawn in the chronic phase were nonmethylated, similar to control samples from non-CML patients. The remaining 26% were partially methylated in the abl Pa region. The latter samples were derived from patients who were indistinguishable from the others on the basis of clinical presentation. Methylated samples were mostly derived from patients known to have a disease of longer duration (26 months v 7.5 months, P = .01). Samples of 30 IFNalpha-treated patients were sequentially analyzed in the course of treatment. Fifteen patients with no evidence of Pa methylation before treatment remained methylation-free. The remainder, who displayed Pa methylation before treatment, reverted to the methylation-free status. The outcome is attributed to IFNalpha therapy, as the Pa methylation status was not reversed in any of the patients treated with hydroxyurea. Methylation of the abl promoter indicates a disease of long-standing, most likely associated with a higher probability of imminent blastic transformation. It appears to predict the outcome of IFNalpha therapy far better than the cytogenetic response.     

Sarcoplasmic reticular Ca2+ pump ATPase activity in congestive heart failure due to myocardial infarction

OBJECTIVE: Earlier studies have shown a depression in the sarcoplasmic reticular (SR) Ca2+ uptake and gene expression in Ca2+ pump ATPase protein in congestive heart failure subsequent to myocardial infarction. It is the objective of this study to understand further the mechanisms of depressed SR Ca2+ pump activity in the failing heart. METHODS: Heart failure in rats was induced by occluding the left coronary artery for 16 weeks and the viable left ventricle was processed for the isolation of SR membranes. Sham-operated animals were used as control. The characteristics of SR Ca2+ pump ATPase in the presence of different concentrations of K+, Ca2+ and ATP were examined and the purity of these membranes was monitored by determining the marker enzyme activities. In addition to measuring changes in cyclic adenosine monophosphate (cAMP) protein kinase and Ca(2+)-calmodulin induced phosphorylation, alterations in SR phospholipid composition as well as sulfhydryl (SH) group content were investigated. RESULTS: Ca(2+)-stimulated ATPase activity, unlike Mg(2+)-ATPase activity, was depressed in the left ventricular SR from failing hearts as compared to control. The decrease in Ca(2+)-stimulated ATPase activity was seen at different concentrations of Ca2+, K+ and ATP but no changes in the affinities of the enzyme for Ca2+ and ATP were evident. The SR Ca(2+)-stimulated ATPase activities in the presence of both cAMP-dependent protein kinase and Ca(2+)-calmodulin were markedly decreased in the failing hearts when compared to control preparations. Furthermore, the 32P incorporation in the presence of cAMP-dependent protein kinase or Ca(2+)-calmodulin was also reduced in the experimental heart SR membranes. The phospholipid composition of the SR membranes from the failing heart was markedly altered. No changes in SH-group or the degree of cross contamination with other membranes were apparent in the failing heart SR. CONCLUSIONS: These results suggest that abnormalities in membrane phospholipid composition and phosphorylation of the enzyme may partly explain the observed depression in SR Ca2+ pump ATPase activity in heart failure following myocardial infarction.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

A localization algorithm for large scale mobile wireless sensor networks: a learning approach

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn’t need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coefficients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR). A series of experiments were performed utilizing three different feature extraction methods: (1) conventional MFCC; (2) Delta-Delta MFCC (DDMFCC); and (3) DCT-II based DDMFCC. The experiments were then expanded to include four classifiers: (1) FVQ; (2) K-means Vector Quantization (VQ); (3) Linde, Buzo and Gray VQ; and (4) Gaussian Mixed Model (GMM). The combination of DCT-II based MFCC, DMFCC and DDMFCC with FVQ was found to have the lowest Equal Error Rate for the VQ based classifiers. The results found were an improvement over previously reported non-GMM methods and approached the results achieved for the computationally expensive GMM based method. Speaker verification tests carried out highlighted the overall performance improvement for the new ASR system. The National Institute of Standards and Technology Speaker Recognition Evaluation corpora was used to provide speaker source data for the experiments.