Data-poor categorization and passage retrieval for Gene Ontology Annotation in Swiss-Prot

<p>Abstract</p> <p>Background</p> <p>In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and a relevant article, extraction of a short passage that justifies the GO category assignement; 2) given a Swiss-Prot pair, containing a protein and a relevant article, automatic assignement of a set of categories.</p> <p>Methods</p> <p>Sentence is the basic retrieval unit. Our classifier computes a distance between each sentence and the GO category provided with the Swiss-Prot entry. The Text Categorizer computes a distance between each GO term and the text of the article. Evaluations are reported both based on annotator judgements as established by the competition and based on mean average precision measures computed using a curated sample of Swiss-Prot.</p> <p>Results</p> <p>Our system achieved the best recall and precision combination both for passage retrieval and text categorization as evaluated by official evaluators. However, text categorization results were far below those in other data-poor text categorization experiments The top proposed term is relevant in less that 20% of cases, while categorization with other biomedical controlled vocabulary, such as the Medical Subject Headings, we achieved more than 90% precision. We also observe that the scoring methods used in our experiments, based on the retrieval status value of our engines, exhibits effective confidence estimation capabilities.</p> <p>Conclusion</p> <p>From a comparative perspective, the combination of retrieval and natural language processing methods we designed, achieved very competitive performances. Largely data-independent, our systems were no less effective that data-intensive approaches. These results suggests that the overall strategy could benefit a large class of information extraction tasks, especially when training data are missing. However, from a user perspective, results were disappointing. Further investigations are needed to design applicable end-user text mining tools for biologists.</p

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction

<p>Abstract</p> <p>Background</p> <p>This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases.</p> <p>Results</p> <p>Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%).</p> <p>Conclusions</p> <p>Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics.</p

Modular text mining for protein-protein interactions extraction

Since researchers discovered that proteins do not function isolated in a cell but act in multi-protein complexes, the number of publications about protein-protein interactions (PPI) has increased significantly. This large amount of unstructured textual information is difficult to exploit by humans as these have trouble to localize the information of interest efficiently. Therefore, it is necessary to develop techniques to automate the extraction of protein-protein interactions from free text. In this thesis, we explore the PPI extraction from the point of view of database curators and study the dependencies between the different steps of the PPI extraction process. It starts with the recognition of articles containing a PPI. Once done, the proteins are located in the selected documents. These proteins must then be unambiguously identified, and finally the interactions are extracted. These different steps allow u to study exhaustively various data mining techniques. The outcomes of this thesis confirm the crucial importance of the performance consistency of the tasks involved in a process over their individual performance. More specifically, the results reveal that each time an error occurs at a given step, it influences all the steps downstream and finally strongly reduces the precision and recall of the generated interactions

Supporting drug prescription through autocompletion

Computerized prescription is a central component in modern clinical information systems. It allows scheduling drugs delivery, exams and other types of care. It is thought to be a useful tool for the reduction of medication errors and for the improvement of medication logistics. Whereas the success of the computerized prescription depends on the unambiguous selection of the manipulated concepts, there is a strong variability between the preferred terms of clinicians of different backgrounds. Moreover, users sometimes want to use synonyms or don't know the exact spelling of the term. This makes the search for desired procedure name through large size vocabularies time-consuming for users. In order to facilitate the prescriptions process, we have built a tool that proposes the most likely terms based on the first letters inputted by the user. The tool helps selecting the most appropriate term by ranking the possible results in a clever manner. Experimental evaluation shows promising results and indicates the tool ease the terminology manipulations

Supporting elderly homecare with smartwatches: advantages and drawbacks

The demographic transition in industrialized countries leads to a growth of elderly population. This population is more prone to chronic diseases and puts an increasing pressure on the healthcare system. One way to reduce the cost associated to the support of this population is to improve its autonomy to keep it independent as long as possible. Many assistive technologies and environmental interventions can be implemented to achieve this goal. In this paper, we are looking at the advantages and drawbacks of smartwatches as a platform to support elderly at home. By doing a literature search and by performing expert interview, we have identified the advantages of this technology to insure the success of promising applications as well as the obstacles that should be gone beyond. Among the advantages, the ubiquity of smartwatches makes possible a continuous medical surveillance, harder to achieve with other devices. Moreover, the versatility of smartwatches provides an appropriate ground to implement a centralized platform providing multiples services facilitating elderly homecare. However, the physical constraints of the watches such as the tiny screen size, the small connectors and the limited power autonomy can be significant barriers to the adoption of these tools. In conclusion, beside the actual homecare system, improving the autonomy and the independence of elderly at home can be leveraged by a combination of environmental and assistive technologies. Smartwatches have definitively the potential to become close assistants to help elderly in their daily life. However, this will not be achieved without dedicating a significant effort in designing appropriate user interfaces and certainly dedicated hardware to respond to the constraints associated with potential physical and cognitive impairments

Integrating Patient-Generated Health Data in an Electronic Medical Record: Stakeholders' Perspectives

Patient-generated health data (PGHD), when shared with the provider, provides potential as an approach to improve quality of care. Based on interviews and a focus group with stakeholders involved in PGHD integration in the electronic medical record (EMR), we explore the benefits, barriers and possible risks. We propose solutions to address liability concerns, such as clarifying patient and provider expectations for the analyses of PGHD and emphasize considerations for future steps, which include the need to screen PGHD for patient safety

PedAMINES: a disruptive mHealth app to tackle paediatric medication errors

Medication errors are among the most common medical adverse events and an important cause of patient morbidity and mortality, affecting millions of people worldwide each year. This problem is especially acute in paediatric settings, where most drugs given intravenously to children are provided in vials prepared for the adult population. This leads to the need for a specific, individual, weight-based drug-dose calculation and preparation for each child, which varies widely across age groups. This error-prone process places children at a high risk for life-threatening medication errors, particularly in stressful and critical situations, such as cardiopulmonary resuscitation. To limit and mitigate the likelihood of their occurrence, hospitals are increasingly adopting eHealth interventions aimed at supporting and securing each individual stage along the whole medication process, but there is mixed evidence regarding their positive contribution. These technologies are helpful as long as they are used within the scope of their application and users are aware of their limitations, as their introduction has sometimes led to new, often unforeseen, types of errors. The aim of the present work is to provide an overview of some of the main eHealth interventions used across the various stages of the medication process and to highlight areas that require attention in order to implement successful digital technologies. More specifically, the contribution of eHealth technologies in paediatrics is discussed, including the out-of-hospital setting, as well as barriers to their implementation in low- and middle-income countries. Finally, we describe our own work in this field with regards to the development and use of an innovative, evidence-based mobile device application (PedAMINES) to address the unmet need of reducing paediatric medication errors, especially during cardiopulmonary resuscitation. The PedAMINES app has also the potential to make a very effective contribution to the goals of the Third World Health Organization Global Patient Safety Challenge to reduce severe, avoidable medication-associated harm by 50% in all countries over the next 5 years, including low- and middle-income countries

Using of Patient-Generated mHealth Data for Patient Care: a Comparison of Four Models

The rapid adoption of mobile applications for wellness and health tracking has resulted in vast amounts of patient-generated data. However, these data are often underutilised in traditional patient care. In this paper, we explore how to use these patient-generated data to improve patient care. Based on a review of healthcare models and recommendations, we proposed and compared four models with increasing integration with electronic health records (EHRs). We also compared the freedom of choice of apps, as well as content validity and expected effectiveness. In the first model, patients have the full range of app choice, and full control over their data, in particular for sharing with healthcare providers. In the second model, patients use a selection of apps to export their data to a repository, which can be accessed by their providers (without integration into the EHR). In the third model, interoperability between the apps and the EHR allows full integration, but restricts app choice. Finally, the last model adds the notion of cost-effectiveness to the previous model. Although the EHR-integrated models limit app choice for patients, the app content is medically validated and patient-generated data are more easily accessed to improve patient care. However, these integrated models require decision-support algorithms to avoid overwhelming the healthcare providers with data, and may not necessarily imply better quality patient care