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Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine
Time-Space Efficient Regression Testing for Configurable Systems
Configurable systems are those that can be adapted from a set of options.
They are prevalent and testing them is important and challenging. Existing
approaches for testing configurable systems are either unsound (i.e., they can
miss fault-revealing configurations) or do not scale. This paper proposes
EvoSPLat, a regression testing technique for configurable systems. EvoSPLat
builds on our previously-developed technique, SPLat, which explores all
dynamically reachable configurations from a test. EvoSPLat is tuned for two
scenarios of use in regression testing: Regression Configuration Selection
(RCS) and Regression Test Selection (RTS). EvoSPLat for RCS prunes
configurations (not tests) that are not impacted by changes whereas EvoSPLat
for RTS prunes tests (not configurations) which are not impacted by changes.
Handling both scenarios in the context of evolution is important. Experimental
results show that EvoSPLat is promising. We observed a substantial reduction in
time (22%) and in the number of configurations (45%) for configurable Java
programs. In a case study on a large real-world configurable system (GCC),
EvoSPLat reduced 35% of the running time. Comparing EvoSPLat with sampling
techniques, 2-wise was the most efficient technique, but it missed two bugs
whereas EvoSPLat detected all bugs four times faster than 6-wise, on average.Comment: 14 page
QueryOR: a comprehensive web platform for genetic variant analysis and prioritization
Background: Whole genome and exome sequencing are contributing to the extraordinary progress in the study of
human genetic variants. In this fast developing field, appropriate and easily accessible tools are required to facilitate
data analysis.
Results: Here we describe QueryOR, a web platform suitable for searching among known candidate genes as well
as for finding novel gene-disease associations. QueryOR combines several innovative features that make it comprehensive,
flexible and easy to use. Instead of being designed on specific datasets, it works on a general XML schema specifying
formats and criteria of each data source. Thanks to this flexibility, new criteria can be easily added for future
expansion. Currently, up to 70 user-selectable criteria are available, including a wide range of gene and variant features.
Moreover, rather than progressively discarding variants taking one criterion at a time, the prioritization is achieved by a
global positive selection process that considers all transcript isoforms, thus producing reliable results. QueryOR is easy
to use and its intuitive interface allows to handle different kinds of inheritance as well as features related to sharing
variants in different patients. QueryOR is suitable for investigating single patients, families or cohorts.
Conclusions: QueryOR is a comprehensive and flexible web platform eligible for an easy user-driven variant
prioritization. It is freely available for academic institutions at http://queryor.cribi.unipd.it/
Derivation of diagnostic models based on formalized process knowledge
© IFAC.Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange
Diagnosis and management of postpartum hemorrhage and intrapartum asphyxia in a quality improvement initiative using nurse-mentoring and simulation in Bihar, India.
BackgroundIn the state of Bihar, India a multi-faceted quality improvement nurse-mentoring program was implemented to improve provider skills in normal and complicated deliveries. The objective of this analysis was to examine changes in diagnosis and management of postpartum hemorrhage (PPH) of the mother and intrapartum asphyxia of the infant in primary care facilities in Bihar, during the program.MethodsDuring the program, mentor pairs visited each facility for one week, covering four facilities over a four-week period and returned for subsequent week-long visits once every month for seven to nine consecutive months. Between- and within-facility comparisons were made using a quasi-experimental and a longitudinal design over time, respectively, to measure change due to the intervention. The proportions of PPH and intrapartum asphyxia among all births as well as the proportions of PPH and intrapartum asphyxia cases that were effectively managed were examined. Zero-inflated negative binomial models and marginal structural methodology were used to assess change in diagnosis and management of complications after accounting for clustering of deliveries within facilities as well as time varying confounding.ResultsThis analysis included 55,938 deliveries from 320 facilities. About 2% of all deliveries, were complicated with PPH and 3% with intrapartum asphyxia. Between-facility comparisons across phases demonstrated diagnosis was always higher in the final week of intervention (PPH: 2.5-5.4%, intrapartum asphyxia: 4.2-5.6%) relative to the first week (PPH: 1.2-2.1%, intrapartum asphyxia: 0.7-3.3%). Within-facility comparisons showed PPH diagnosis increased from week 1 through 5 (from 1.6% to 4.4%), after which it decreased through week 7 (3.1%). A similar trend was observed for intrapartum asphyxia. For both outcomes, the proportion of diagnosed cases where selected evidence-based practices were used for management either remained stable or increased over time.ConclusionsThe nurse-mentoring program appears to have built providers' capacity to identify PPH and intrapartum asphyxia cases but diagnosis levels are still not on par with levels observed in Southeast Asia and globally
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