OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.

BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):55

Authors: Lossio-Ventura JA, Hogan W, Modave F, Guo Y, He Z, Yang X, Zhang H, Bian J

Abstract
BACKGROUND: There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB.
METHODS: We developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB.
RESULTS: We conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB.
CONCLUSIONS: We presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.

PMID: 30066655 [PubMed - in process]

Visualized Emotion Ontology: a model for representing visual cues of emotions.

BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):64

Authors: Lin R, Amith MT, Liang C, Duan R, Chen Y, Tao C

Abstract
BACKGROUND: Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being.
METHODS: In order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors.
RESULTS: As determined by ontology evaluation metrics, VEO exhibited better machine-readability (z=1.12), linguistic quality (z=0.61), and domain coverage (z=0.39) compared to a sample of cognitive ontologies. Additionally, a survey of 1082 participants through Amazon Mechanical Turk revealed that a significantly higher proportion of people agree than disagree with 17 out of our 25 emotion images, validating the majority of our visualizations.
CONCLUSION: From the development, evaluation, and serialization of the VEO, we have defined a set of 25 emotions using OWL that linked surveyed visualizations to each emotion. In the future, we plan to use the VEO in patient-facing software tools, such as embodied conversational agents, to enhance interactions between patients and providers in a clinical environment.

PMID: 30066654 [PubMed - in process]

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment.

J Digit Imaging. 2018 Jul 31;:

Authors: Messaoudi R, Jaziri F, Mtibaa A, Grand-Brochier M, Ali HM, Amouri A, Fourati H, Chabrot P, Gargouri F, Vacavant A

Abstract
Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).

PMID: 30066122 [PubMed - as supplied by publisher]

An Ontology-Based Knowledge Methodology in the Medical Domain in the Latin America: the Study Case of Republic of Panama.

Acta Inform Med. 2018 Jun;26(2):98-101

Authors: Cedeno-Moreno D, Vargas-Lombardo M

Abstract
Introduction: Nowadays in Panama, there is a lot of patient information stored in textual form which cannot be manipulated to manage adequate knowledge. There are multiple resources created to represent knowledge, including specialized glossaries, ontologies, among others. The ontologies are an important part within the scope of the recovery and organization of the information and the semantic web. Also in recent works they are used in applications of natural language processing (NLP), as a knowledge base.
Aim: This research was conducted with the aim of creating a methodology that allows from a text written in NL, extract the necessary elements using NLP tools and with them create a knowledge base represented by one domain ontology and extract knowledge to help medical specialists.
Material and Methods: In this study we carried out a methodology that allows the extraction of knowledge of patient clinical records, general medicine and palliative care, in order to show relevant knowledge elements to specialists. The methodology was validated with a data corpus of approximately 200 patient records.
Conclusion: We have created a knowledge representation methodology, combining NLP techniques and tools and the automatic instantiation of an ontology, which can serve as a software agent for other applications or used to visualize the patient's clinical information. The study was validated using the traditional metrics of information retrieval systems precision, recall, F-measure obtaining excellent results, and can be used as a software agent or methodology for the development of information extraction software systems in the medical domain.

PMID: 30061779 [PubMed]

On the plausibility of socioeconomic mortality estimates derived from linked data: a demographic approach.

Popul Health Metr. 2017 Jul 14;15(1):26

Authors: Lerch M, Spoerri A, Jasilionis D, Viciana Fernandèz F

Abstract
BACKGROUND: Reliable estimates of mortality according to socioeconomic status play a crucial role in informing the policy debate about social inequality, social cohesion, and exclusion as well as about the reform of pension systems. Linked mortality data have become a gold standard for monitoring socioeconomic differentials in survival. Several approaches have been proposed to assess the quality of the linkage, in order to avoid the misclassification of deaths according to socioeconomic status. However, the plausibility of mortality estimates has never been scrutinized from a demographic perspective, and the potential problems with the quality of the data on the at-risk populations have been overlooked.
METHODS: Using indirect demographic estimation (i.e., the synthetic extinct generation method), we analyze the plausibility of old-age mortality estimates according to educational attainment in four European data contexts with different quality issues: deterministic and probabilistic linkage of deaths, as well as differences in the methodology of the collection of educational data. We evaluate whether the at-risk population according to educational attainment is misclassified and/or misestimated, correct these biases, and estimate the education-specific linkage rates of deaths.
RESULTS: The results confirm a good linkage of death records within different educational strata, even when probabilistic matching is used. The main biases in mortality estimates concern the classification and estimation of the person-years of exposure according to educational attainment. Changes in the census questions about educational attainment led to inconsistent information over time, which misclassified the at-risk population. Sample censuses also misestimated the at-risk populations according to educational attainment.
CONCLUSION: The synthetic extinct generation method can be recommended for quality assessments of linked data because it is capable not only of quantifying linkage precision, but also of tracking problems in the population data. Rather than focusing only on the quality of the linkage, more attention should be directed towards the quality of the self-reported socioeconomic status at censuses, as well as towards the accurate estimation of the at-risk populations.

PMID: 28705165 [PubMed - indexed for MEDLINE]

Pharmacovigilance from social media: An improved random subspace method for identifying adverse drug events.

Int J Med Inform. 2018 Sep;117:33-43

Authors: Liu J, Wang G

Abstract
OBJECTIVE: Recent advances in Web 2.0 technologies have seen significant strides towards utilizing patient-generated content for pharmacovigilance. Social media-based pharmacovigilance has great potential to augment current efforts and provide regulatory authorities with valuable decision aids. Among various pharmacovigilance activities, identifying adverse drug events (ADEs) is very important for patient safety. However, in health-related discussion forums, ADEs may confound with drug indications and beneficial effects, etc. Therefore, the focus of this study is to develop a strategy to identify ADEs from other semantic types, and meanwhile to determine the drug that an ADE is associated with.
MATERIALS AND METHODS: In this study, two groups of features, i.e., shallow linguistic features and semantic features, are explored. Moreover, motivated and inspired by the characteristics of explored two feature categories for social media-based ADE identification, an improved random subspace method, called Stratified Sampling-based Random Subspace (SSRS), is proposed. Unlike conventional random subspace method that applies random sampling for subspace selection, SSRS adopts stratified sampling-based subspace selection strategy.
RESULTS: A case study on heart disease discussion forums is performed to evaluate the effectiveness of the SSRS method. Experimental results reveal that the proposed SSRS method significantly outperforms other compared ensemble methods and existing approaches for ADE identification.
DISCUSSION AND CONCLUSION: Our proposed method is easy to implement since it is based on two feature sets that can be naturally derived, and therefore, can omit artificial stratum generation efforts. Moreover, SSRS has great potential of being applied to deal with other high-dimensional problems that can represent original data from two different aspects.

PMID: 30032963 [PubMed - in process]

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization.

IEEE Trans Cybern. 2018 Jul 17;:

Authors: Howell SK, Wicaksono H, Yuce B, McGlinn K, Rezgui Y

Abstract
This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.

PMID: 30028719 [PubMed - as supplied by publisher]

Speed-Dial: A Surrogate Mouse for Non-Visual Web Browsing.

ASSETS. 2017 Oct-Nov;2017:110-119

Authors: Billah SM, Ashok V, Porter DE, Ramakrishnan IV

Abstract
Sighted people can browse the Web almost exclusively using a mouse. This is because web browsing mostly entails pointing and clicking on some element in the web page, and these two operations can be done almost instantaneously with a computer mouse. Unfortunately, people with vision impairments cannot use a mouse as it only provides visual feedback through a cursor. Instead, they are forced to go through a slow and tedious process of building a mental map of the web page, relying primarily on a screen reader's keyboard shortcuts and its serial audio readout of the textual content of the page, including metadata. This can often cause content and cognitive overload. This paper describes our Speed-Dial system which uses an off-the-shelf physical Dial as a surrogate for the mouse for non-visual web browsing. Speed-Dial interfaces the physical Dial with the semantic model of a web page, and provides an intuitive and rapid access to the entities and their content in the model, thereby bringing blind people's browsing experience closer to how sighted people perceive and interact with the Web. A user study with blind participants suggests that with Speed-Dial they can quickly move around the web page to select content of interest, akin to pointing and clicking with a mouse.

PMID: 30027156 [PubMed]

BioSearch: a semantic search engine for Bio2RDF.

Database (Oxford). 2017 Jan 01;2017:

Authors: Hu W, Qiu H, Huang J, Dumontier M

Abstract
Database URL: http://ws.nju.edu.cn/biosearch/.

PMID: 29220451 [PubMed - indexed for MEDLINE]

Flexibility of thought in high creative individuals represented by percolation analysis.

Proc Natl Acad Sci U S A. 2018 01 30;115(5):867-872

Authors: Kenett YN, Levy O, Kenett DY, Stanley HE, Faust M, Havlin S

Abstract
Flexibility of thought is theorized to play a critical role in the ability of high creative individuals to generate novel and innovative ideas. However, this has been examined only through indirect behavioral measures. Here we use network percolation analysis (removal of links in a network whose strength is below an increasing threshold) to computationally examine the robustness of the semantic memory networks of low and high creative individuals. Robustness of a network indicates its flexibility and thus can be used to quantify flexibility of thought as related to creativity. This is based on the assumption that the higher the robustness of the semantic network, the higher its flexibility. Our analysis reveals that the semantic network of high creative individuals is more robust to network percolation compared with the network of low creative individuals and that this higher robustness is related to differences in the structure of the networks. Specifically, we find that this higher robustness is related to stronger links connecting between different components of similar semantic words in the network, which may also help to facilitate spread of activation over their network. Thus, we directly and quantitatively examine the relation between flexibility of thought and creative ability. Our findings support the associative theory of creativity, which posits that high creative ability is related to a flexible structure of semantic memory. Finally, this approach may have further implications, by enabling a quantitative examination of flexibility of thought, in both healthy and clinical populations.

PMID: 29339514 [PubMed - indexed for MEDLINE]

A document-centric approach for developing the tolAPC ontology.

J Biomed Semantics. 2017 Nov 28;8(1):54

Authors: Blfgeh A, Warrender J, Hilkens CMU, Lord P

Abstract
BACKGROUND: There are many challenges associated with ontology building, as the process often touches on many different subject areas; it needs knowledge of the problem domain, an understanding of the ontology formalism, software in use and, sometimes, an understanding of the philosophical background. In practice, it is very rare that an ontology can be completed by a single person, as they are unlikely to combine all of these skills. So people with these skills must collaborate. One solution to this is to use face-to-face meetings, but these can be expensive and time-consuming for teams that are not co-located. Remote collaboration is possible, of course, but one difficulty here is that domain specialists use a wide-variety of different "formalisms" to represent and share their data - by the far most common, however, is the "office file" either in the form of a word-processor document or a spreadsheet. Here we describe the development of an ontology of immunological cell types; this was initially developed by domain specialists using an Excel spreadsheet for collaboration. We have transformed this spreadsheet into an ontology using highly-programmatic and pattern-driven ontology development. Critically, the spreadsheet remains part of the source for the ontology; the domain specialists are free to update it, and changes will percolate to the end ontology.
RESULTS: We have developed a new ontology describing immunological cell lines built by instantiating ontology design patterns written programmatically, using values from a spreadsheet catalogue.
CONCLUSIONS: This method employs a spreadsheet that was developed by domain experts. The spreadsheet is unconstrained in its usage and can be freely updated resulting in a new ontology. This provides a general methodology for ontology development using data generated by domain specialists.

PMID: 29179777 [PubMed - indexed for MEDLINE]

Continuity of Primary Care and Emergency Hospital Admissions Among Older Patients in England.

Ann Fam Med. 2017 Nov;15(6):515-522

Authors: Tammes P, Purdy S, Salisbury C, MacKichan F, Lasserson D, Morris RW

Abstract
PURPOSE: Secondary health care services have been under considerable pressure in England as attendance rates increase, resulting in longer waiting times and greater demands on staff. This study's aim was to examine the association between continuity of care and risk of emergency hospital admission among older adults.
METHODS: We analyzed records from 10,000 patients aged 65 years and older in 2012 within 297 English general practices obtained from the Clinical Practice Research Datalink and linked with Hospital Episode Statistics. We used the Bice and Boxerman (BB) index and the appointed general practitioner index (last general practitioner consulted before hospitalization) to quantify patient-physician continuity. The BB index was used in a prospective cohort approach to assess impact of continuity on risk of admission. Both indices were used in a separate retrospective nested case-control approach to test the effect of changing physician on the odds of hospital admission in the following 30 days.
RESULTS: In the prospective cohort analysis, the BB index showed a graded, non-significant inverse relationship of continuity of care with risk of emergency hospital admission, although the hazard ratio for patients experiencing least continuity was 2.27 (95% CI, 1.37-3.76) compared with those having complete continuity. In the retrospective nested case-control analysis, we found a graded inverse relationship between continuity of care and emergency hospital admission for both BB and appointed general practitioner indices: for the latter, the odds ratio for those experiencing least continuity was 2.32 (95% CI, 1.48-3.63) relative to those experiencing most continuity.
CONCLUSIONS: Marked discontinuity of care might contribute to increased unplanned hospital admissions among patients aged 65 years and older. Schemes to enhance continuity of care have the potential to reduce hospital admissions.

PMID: 29133489 [PubMed - indexed for MEDLINE]

A guide to evaluating linkage quality for the analysis of linked data.

Int J Epidemiol. 2017 Oct 01;46(5):1699-1710

Authors: Harron KL, Doidge JC, Knight HE, Gilbert RE, Goldstein H, Cromwell DA, van der Meulen JH

Abstract
Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data.

PMID: 29025131 [PubMed - indexed for MEDLINE]

Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs.

Proc Int World Wide Web Conf. 2018 Apr;2018:489-498

Authors: Yang X, Nicholson PK, Ajwani D, Riedewald M, Gatterbauer W, Sala A

Abstract
Many problems in areas as diverse as recommendation systems, social network analysis, semantic search, and distributed root cause analysis can be modeled as pattern search on labeled graphs (also called "heterogeneous information networks" or HINs). Given a large graph and a query pattern with node and edge label constraints, a fundamental challenge is to find the top-k matches according to a ranking function over edge and node weights. For users, it is difficult to select value k. We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, return as many of the top-ranked results as possible. Then, given additional time, produce the next lower-ranked results quickly as well. It can be stopped anytime, but may have to continue until all results are returned. This paper focuses on acyclic patterns over arbitrary labeled graphs. We are interested in practical algorithms that effectively exploit (1) properties of heterogeneous networks, in particular selective constraints on labels, and (2) that the users often explore only a fraction of the top-ranked results. Our solution, KARPET, carefully integrates aggressive pruning that leverages the acyclic nature of the query, and incremental guided search. It enables us to prove strong non-trivial time and space guarantees, which is generally considered very hard for this type of graph search problem. Through experimental studies we show that KARPET achieves running times in the order of milliseconds for tree patterns on large networks with millions of nodes and edges.

PMID: 30003197 [PubMed]

OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies.

Front Pharmacol. 2018;9:609

Authors: Natsiavas P, Boyce RD, Jaulent MC, Koutkias V

Abstract
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.

PMID: 29997499 [PubMed]

Meta-path methods for prioritizing candidate disease miRNAs.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov 22;:

Authors: Zhang X, Zou Q, Rodruguez-Paton A, Zeng X

Abstract
MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Predicting potential miRNAdisease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNAdisease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA target association and disease miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. A web service for the download and prediction of relationships between diseases and miRNAs is available at http://lab.malab.cn/soft/MDPredict/.

PMID: 29990255 [PubMed - as supplied by publisher]

Peculiarities of Precocious Puberty in Boys and Girls With McCune-Albright Syndrome.

Front Endocrinol (Lausanne). 2018;9:337

Authors: Corica D, Aversa T, Pepe G, De Luca F, Wasniewska M

Abstract
McCune-Albright Syndrome (MAS; OMIM # 174800) is a rare, sporadic disease caused by a post-zygotic, activating mutation in the guanine-nucleotide binding protein α-subunit (GNAS1) gene. MAS is characterized by the clinical triad of polyostotic fibrous dysplasia of bone, café-au-lait skin pigmentation and peripheral precocious puberty. However, clinical presentation is highly variable depending on mosaic tissue distribution of mutant-bearing cells. Precocious puberty is the most common endocrine manifestation of MAS and is often the presenting, and sometimes the only, clinical sign of MAS. Due to the very low prevalence of MAS, data on course of precocious puberty, effectiveness of treatments and gonadal function during post-pubertal period are lacking. Our knowledge on this issue derives essentially from case reports and small cohorts of patients. The aim of this review is to report all available literature data on clinical aspects, therapeutic management and outcomes of precocious puberty in children with MAS. A systematic research was carried out through MEDLINE via PubMed, EMBASE, Web of Science, Semantic Scholar, Cochrane Library.

PMID: 29988390 [PubMed]

Relationship between out-of-home care placement history characteristics and educational achievement: A population level linked data study.

Child Abuse Negl. 2017 Aug;70:146-159

Authors: Maclean MJ, Taylor CL, O'Donnell M

Abstract
Studies generally show children who have entered out-of-home care have worse educational outcomes than the general population, although recent research suggests maltreatment and other adversities are major contributing factors. Children's out-of-home care experiences vary and may affect their outcomes. This study examined the influence of placement stability, reunification, type of care, time in care and age at entry to care on children's educational outcomes. We conducted a population-based record-linkage study of children born in Western Australia between 1990 and 2010 who sat State or national Year 3 reading achievement tests (N=235,045 children, including 2160 children with a history of out-of-home care). Children's educational outcomes varied with many aspects of their care experience. Children placed in residential care were particularly likely to have low scores, with an unadjusted OR 6.81, 95% CI[4.94, 9.39] for low reading scores, which was partially attenuated after adjusting for background risk factors but remained significant (OR=1.50, 95% CIs [1.08, 2.08]). Reading scores were also lower for children who had experienced changes in care arrangements in the year of the test. A dose-response effect for multiple placements was expected but not found. Older age at entering care was also associated with worse reading scores. Different characteristics of a child's care history were interwoven with each other as well as child, family and neighbourhood characteristics, highlighting a need for caution in attributing causality. Although the level of educational difficulties varied, the findings suggest a widespread need for additional educational support for children who have entered care, including after reunification.

PMID: 28609694 [PubMed - indexed for MEDLINE]

SteeringWheel: A Locality-Preserving Magnification Interface for Low Vision Web Browsing.

Proc SIGCHI Conf Hum Factor Comput Syst. 2018 Apr;2018:

Authors: Billah SM, Ashok V, Porter DE, Ramakrishnan IV

Abstract
Low-vision users struggle to browse the web with screen magnifiers. Firstly, magnifiers occlude significant portions of the webpage, thereby making it cumbersome to get the webpage overview and quickly locate the desired content. Further, magnification causes loss of spatial locality and visual cues that commonly define semantic relationships in the page; reconstructing semantic relationships exclusively from narrow views dramatically increases the cognitive burden on the users. Secondly, low-vision users have widely varying needs requiring a range of interface customizations for different page sections; dynamic customization in extant magnifiers is disruptive to users' browsing. We present SteeringWheel, a magnification interface that leverages content semantics to preserve local context. In combination with a physical dial, supporting simple rotate and press gestures, users can quickly navigate different webpage sections, easily locate desired content, get a quick overview, and seamlessly customize the interface. A user study with 15 low-vision participants showed that their web-browsing efficiency improved by at least 20 percent with SteeringWheel compared to extant screen magnifiers.

PMID: 29978857 [PubMed]

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples.

Proc IEEE WIC ACM Int Conf Web Intell Intell Agent Technol. 2017 Aug;2017:1-9

Authors: Sheth A, Perera S, Wijeratne S, Thirunarayan K

Abstract
Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

PMID: 29962511 [PubMed]