Making Study Populations Visible through Knowledge Graphs

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations.Comment: 16 pages, 4 figures, 1 table, accepted to the ISWC 2019 Resources Track (https://iswc2019.semanticweb.org/call-for-resources-track-papers/

A semantic metadata enrichment software ecosystem (SMESE) : its prototypes for digital libraries, metadata enrichments and assisted literature reviews

Contribution 1: Initial design of a semantic metadata enrichment ecosystem (SMESE) for Digital Libraries The Semantic Metadata Enrichments Software Ecosystem (SMESE V1) for Digital Libraries (DLs) proposed in this paper implements a Software Product Line Engineering (SPLE) process using a metadata-based software architecture approach. It integrates a components-based ecosystem, including metadata harvesting, text and data mining and machine learning models. SMESE V1 is based on a generic model for standardizing meta-entity metadata and a mapping ontology to support the harvesting of various types of documents and their metadata from the web, databases and linked open data. SMESE V1 supports a dynamic metadata-based configuration model using multiple thesauri. The proposed model defines rules-based crosswalks that create pathways to different sources of data and metadata. Each pathway checks the metadata source structure and performs data and metadata harvesting. SMESE V1 proposes a metadata model in six categories of metadata instead of the four currently proposed in the literature for DLs; this makes it possible to describe content by defined entity, thus increasing usability. In addition, to tackle the issue of varying degrees of depth, the proposed metadata model describes the most elementary aspects of a harvested entity. A mapping ontology model has been prototyped in SMESE V1 to identify specific text segments based on thesauri in order to enrich content metadata with topics and emotions; this mapping ontology also allows interoperability between existing metadata models. Contribution 2: Metadata enrichments ecosystem based on topics and interests The second contribution extends the original SMESE V1 proposed in Contribution 1. Contribution 2 proposes a set of topic- and interest-based content semantic enrichments. The improved prototype, SMESE V3 (see following figure), uses text analysis approaches for sentiment and emotion detection and provides machine learning models to create a semantically enriched repository, thus enabling topic- and interest-based search and discovery. SMESE V3 has been designed to find short descriptions in terms of topics, sentiments and emotions. It allows efficient processing of large collections while keeping the semantic and statistical relationships that are useful for tasks such as: 1. topic detection, 2. contents classification, 3. novelty detection, 4. text summarization, 5. similarity detection. Contribution 3: Metadata-based scientific assisted literature review The third contribution proposes an assisted literature review (ALR) prototype, STELLAR V1 (Semantic Topics Ecosystem Learning-based Literature Assisted Review), based on machine learning models and a semantic metadata ecosystem. Its purpose is to identify, rank and recommend relevant papers for a literature review (LR). This third prototype can assist researchers, in an iterative process, in finding, evaluating and annotating relevant papers harvested from different sources and input into the SMESE V3 platform, available at any time. The key elements and concepts of this prototype are: 1. text and data mining, 2. machine learning models, 3. classification models, 4. researchers annotations, 5. semantically enriched metadata. STELLAR V1 helps the researcher to build a list of relevant papers according to a selection of metadata related to the subject of the ALR. The following figure presents the model, the related machine learning models and the metadata ecosystem used to assist the researcher in the task of producing an ALR on a specific topic

Improving Providers’ Survival Estimates and Selection of Prognosis- and Guidelines-Appropriate Radiotherapy Regimens for Patients with Symptomatic Bone Metastases: Development and Evaluation of the BMETS Model and Decision Support Platform

In the management of symptomatic bone metastases, selection of appropriate palliative radiotherapy (RT) regimens should be based on patient-specific characteristics including estimated survival time. Yet, provider predictions of patient survival are notoriously inaccurate. Moreover, available evidence- and consensus-based guidelines do not provide clear criteria for selecting between the range of palliative RT regimens available. In an effort to improve selection of prognosis- and guidelines-appropriate palliative bone treatments, we developed the Bone Metastases Ensemble Trees for Survival (BMETS) model. Built using an institutional database of 397 patients seen in consultation for symptomatic bone metastases, this machine-learning model estimates survival time following RT consultation using 27 prognostic covariates. Cross validations procedures revealed excellent discrimination for survival, and the BMETS outperformed validated, simpler statistical models, justifying its use in this population. To better characterize a component of decisional uncertainty faced by providers, we next sought to identify the prevalence of “complicated” symptomatic bone metastases across a breadth of possible operational definitions. Our efforts identified up to 96 possible definitions of “complicated” bone metastases, present in up to 67.1% of patients in our database. Given that such “complicated” lesions may have been excluded from clinical trials in this setting, these data highlight the difficulty faced by providers when attempting to select appropriate RT regimens using inadequately defined selection criteria. Informed by these insights, we developed the BMETS Decision Support Platform (BMETS-DSP). This provider-facing, web-based tool was created to (1) collect relevant patient-specific data, (2) display an individualized predicted survival curve as per the BMETS model, and (3) provide case-specific, evidence-based recommendations for treatment of symptomatic bone metastases. We then conducted a pilot assessment of the clinical utility of the BMETS-DSP. In this preliminary assessment, the BMETS-DSP significantly improved physician accuracy in estimating survival and increased prognostic confidence, likelihood of sharing prognosis, and use of prognosis-appropriate RT regimens in the care of case patients. Collectively, this research provides early justification for the use of a machine-learning survival model and resultant decisions support platform to guide individualized selection of palliative RT regimens for symptomatic bone metastases. These data support a multi-institutional, randomized trial of the BMETS-DSP

Multi-objective torque control of switched reluctance machine

PhD ThesisThe recent growing interest in Switched Reluctance Drives (SRD) is due to the electrification of many products in industries including electric/hybrid electric vehicles, more-electric aircrafts, white-goods, and healthcare, in which the Switched Reluctance Machine (SRM) has potential prospects in satisfying the respective requirements of these applications. Its main merits are robust structure, suitability for harsh environments, fault-tolerance, low cost, and ability to operate over a wide speed range. Nevertheless, the SRM has limitations such as large torque ripple, high acoustic noise, and low torque density. This research focuses on the torque control of the SRD with the objectives of achieving zero torque error, minimal torque ripple, high reliability and robustness, and lower size, weight, and cost of implementation. Direct Torque Control and Direct Instantaneous Torque Control are the most common methods used to obtain desired torque characteristics including optimal torque density and minimized torque ripple in SRD. However, these torque control methods, compared to conventional hysteresis current control, require the use of power devices with a higher rating of about 150% to achieve the desired superior performance. These requirements add extra cost, conduction loss, and stress on the drive’s semiconductors and machine winding. To overcome these drawbacks, a simple and intuitive torque control method based on a novel adaptive quasi sliding mode control is developed in this study. The proposed torque control approach is designed considering the findings of an investigation performed in this thesis of the existing widely used control techniques for SRD based on information flow complexity. A test rig comprising a magnet assisted SRM driven by an asymmetric converter is constructed to validate the proposed torque control method and to compare its performance with that of direct instantaneous torque control, and current hysteresis control methods. The simulation and experimental results show that the proposed torque control reduces the torque ripple over a wide speed range without demanding a high current and/or a high switching frequency. In addition, It has been shown that the proposed method is superior to current hysteresis control method in the sensorless operation of the machine. Furthermore, the sensorless performance of the proposed method is investigated with the lower component count R-Dump converter. The simulation results have also demonstrated the excellent controller response using the standard R-Dump converter and also with its novel version developed in this thesis that needs only one current sensor

Synthesis and biological evaluation of aeruginosin based compounds and self-assembly of glucosamine based compounds

Aeruginosins are a family of marine natural products containing mostly non-proteogenic amino acids. These compounds contain a common 2-carboxy-6-hydroxy-octaindole (Choi) rigid bicyclic structure. Many aeruginosins are inhibitors for enzymes involved in the blood coagulation cascade, such as thrombin and Factor VIIa. In order to understand the structure activity relationship (SAR) of the aeruginosins and to discover novel anticoagulants with potentially improved inhibitory and pharmacokinetic properties, in the first part of my thesis I have discussed, synthesis of a series of novel analogs of aeruginosin 298-A, in which the Choi will be replaced with L-proline and oxygenated Choi analogs, and the Argol is replaced with various other functionalities. The preparation of oxygenated Choi analogs starting from glucose using a new method has been discussed. In the second part of my dissertation, I have discussed the design, synthesis and self–assembly of glucosamine based hydro and organogelators. Carbohydrate-based low molecular weight gelators are an interesting class of molecules with many potential applications. A series of amides and ureas were prepared from the protected D-glucosamine from the corresponding acid chloride and isocyanates. The self-assembling properties of these compounds were studied in several solvents, including water and aqueous solutions. Most of these compounds were found to be efficient low molecular weight hydrogelators (LMHGs) for aqueous solutions. The preparation and characterization of these compounds will be elaborated

Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

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