Speech and language therapy for aphasia following stroke

Background  Aphasia is an acquired language impairment following brain damage that affects some or all language modalities: expression and understanding of speech, reading, and writing. Approximately one third of people who have a stroke experience aphasia.  Objectives  To assess the effects of speech and language therapy (SLT) for aphasia following stroke.  Search methods  We searched the Cochrane Stroke Group Trials Register (last searched 9 September 2015), CENTRAL (2015, Issue 5) and other Cochrane Library Databases (CDSR, DARE, HTA, to 22 September 2015), MEDLINE (1946 to September 2015), EMBASE (1980 to September 2015), CINAHL (1982 to September 2015), AMED (1985 to September 2015), LLBA (1973 to September 2015), and SpeechBITE (2008 to September 2015). We also searched major trials registers for ongoing trials including ClinicalTrials.gov (to 21 September 2015), the Stroke Trials Registry (to 21 September 2015), Current Controlled Trials (to 22 September 2015), and WHO ICTRP (to 22 September 2015). In an effort to identify further published, unpublished, and ongoing trials we also handsearched theInternational Journal of Language and Communication Disorders(1969 to 2005) and reference lists of relevant articles, and we contacted academic institutions and other researchers. There were no language restrictions.  Selection criteria  Randomised controlled trials (RCTs) comparing SLT (a formal intervention that aims to improve language and communication abilities, activity and participation) versus no SLT; social support or stimulation (an intervention that provides social support and communication stimulation but does not include targeted therapeutic interventions); or another SLT intervention (differing in duration, intensity, frequency, intervention methodology or theoretical approach).  Data collection and analysis  We independently extracted the data and assessed the quality of included trials. We sought missing data from investigators.  Main results  We included 57 RCTs (74 randomised comparisons) involving 3002 participants in this review (some appearing in more than one comparison). Twenty-seven randomised comparisons (1620 participants) assessed SLT versus no SLT; SLT resulted in clinically and statistically significant benefits to patients' functional communication (standardised mean difference (SMD) 0.28, 95% confidence interval (CI) 0.06 to 0.49, P = 0.01), reading, writing, and expressive language, but (based on smaller numbers) benefits were not evident at follow-up. Nine randomised comparisons (447 participants) assessed SLT with social support and stimulation; meta-analyses found no evidence of a difference in functional communication, but more participants withdrew from social support interventions than SLT. Thirty-eight randomised comparisons (1242 participants) assessed two approaches to SLT. Functional communication was significantly better in people with aphasia that received therapy at a high intensity, high dose, or over a long duration compared to those that received therapy at a lower intensity, lower dose, or over a shorter period of time. The benefits of a high intensity or a high dose of SLT were confounded by a significantly higher dropout rate in these intervention groups. Generally, trials randomised small numbers of participants across a range of characteristics (age, time since stroke, and severity profiles), interventions, and outcomes.  Authors' conclusions  Our review provides evidence of the effectiveness of SLT for people with aphasia following stroke in terms of improved functional communication, reading, writing, and expressive language compared with no therapy. There is some indication that therapy at high intensity, high dose or over a longer period may be beneficial. HIgh-intensity and high dose interventions may not be acceptable to all

Speech and language therapy for aphasia following stroke

Background  Aphasia is an acquired language impairment following brain damage that affects some or all language modalities: expression and understanding of speech, reading, and writing. Approximately one third of people who have a stroke experience aphasia.  Objectives  To assess the effects of speech and language therapy (SLT) for aphasia following stroke.  Search methods  We searched the Cochrane Stroke Group Trials Register (last searched 9 September 2015), CENTRAL (2015, Issue 5) and other Cochrane Library Databases (CDSR, DARE, HTA, to 22 September 2015), MEDLINE (1946 to September 2015), EMBASE (1980 to September 2015), CINAHL (1982 to September 2015), AMED (1985 to September 2015), LLBA (1973 to September 2015), and SpeechBITE (2008 to September 2015). We also searched major trials registers for ongoing trials including ClinicalTrials.gov (to 21 September 2015), the Stroke Trials Registry (to 21 September 2015), Current Controlled Trials (to 22 September 2015), and WHO ICTRP (to 22 September 2015). In an effort to identify further published, unpublished, and ongoing trials we also handsearched theInternational Journal of Language and Communication Disorders(1969 to 2005) and reference lists of relevant articles, and we contacted academic institutions and other researchers. There were no language restrictions.  Selection criteria  Randomised controlled trials (RCTs) comparing SLT (a formal intervention that aims to improve language and communication abilities, activity and participation) versus no SLT; social support or stimulation (an intervention that provides social support and communication stimulation but does not include targeted therapeutic interventions); or another SLT intervention (differing in duration, intensity, frequency, intervention methodology or theoretical approach).  Data collection and analysis  We independently extracted the data and assessed the quality of included trials. We sought missing data from investigators.  Main results  We included 57 RCTs (74 randomised comparisons) involving 3002 participants in this review (some appearing in more than one comparison). Twenty-seven randomised comparisons (1620 participants) assessed SLT versus no SLT; SLT resulted in clinically and statistically significant benefits to patients' functional communication (standardised mean difference (SMD) 0.28, 95% confidence interval (CI) 0.06 to 0.49, P = 0.01), reading, writing, and expressive language, but (based on smaller numbers) benefits were not evident at follow-up. Nine randomised comparisons (447 participants) assessed SLT with social support and stimulation; meta-analyses found no evidence of a difference in functional communication, but more participants withdrew from social support interventions than SLT. Thirty-eight randomised comparisons (1242 participants) assessed two approaches to SLT. Functional communication was significantly better in people with aphasia that received therapy at a high intensity, high dose, or over a long duration compared to those that received therapy at a lower intensity, lower dose, or over a shorter period of time. The benefits of a high intensity or a high dose of SLT were confounded by a significantly higher dropout rate in these intervention groups. Generally, trials randomised small numbers of participants across a range of characteristics (age, time since stroke, and severity profiles), interventions, and outcomes.  Authors' conclusions  Our review provides evidence of the effectiveness of SLT for people with aphasia following stroke in terms of improved functional communication, reading, writing, and expressive language compared with no therapy. There is some indication that therapy at high intensity, high dose or over a longer period may be beneficial. HIgh-intensity and high dose interventions may not be acceptable to all.REF Eligible with Permitted Exceptio

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.Siirretty Doriast

Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation

Background: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline. Methods: As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources. Results: A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%. Conclusion: The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte

Pushing Drugs: Genomics and Genetics, the Pharmaceutical Industry, and the Law of Negligence

This article presents a piece of a larger, ongoing project on the phenomenon of market-driven manufacturing (MDM) and how tort law should address it. In contrast to the larger project, this article provides a relatively brief overview of the general phenomenon of MDM, but zeros in on how pharmaceutical manufacturers specifically practice MDM. MDM is a well-documented, much practiced activity, although American courts do not recognize MDM as a discrete category of conduct. The basic idea of MDM is that marketing considerations should continuously control every aspect and stage of a product\u27s lifecycle. When a company engages in MDM, it completely inverts the conception of product design, development, and dissemination that seems natural to those unfamiliar with modern producer practices

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

mHealth: opportunities and challenges for diabetes intervention research

Background: Traditionally, health intervention evaluations provide long-term evidence of efficacy and safety via validated protocols, following a positivist paradigm, or approach, to research. However, modern mobile health (mHealth) technologies develop too quickly and outside of medical regulation, making it challenging for health research to keep pace. Objective: This thesis explored and tested how research can incorporate mHealth approaches and resources to evaluate mHealth interventions comprehensively, which follows the pragmatism paradigm. The works described herein were part of a larger project that designed, developed, and tested a data-sharing system between patients and their healthcare providers (HCPs) during diabetes consultations. Methods: The pragmatism paradigm underpins the mixed-methods, multi-phase design approach to exploring this overall objective. The following methods were performed using a sequential exploratory strategy. First, co-design workshops invited individuals with diabetes and HCPs to design an mHealth data-sharing system. Next, a scoping literature review identified research practices for evaluating mHealth interventions to-date. Then, app usage-logs, collected from a previous longitudinal study, were analyzed to explore how much additional information they could provide about patients’ self-management. Finally, a mixed-method study was designed to test the feasibility of combining both traditional and mHealth approaches and resources to evaluate an intervention. Results: Using the pragmatist paradigm as a scaffolding, these works provide evidence of how research can provide more comprehensive knowledge about mHealth interventions for diabetes care and self-management. Nine individuals with diabetes and six HCPs participated in the co-design workshops. Feedback included how a data-sharing system should work between patients and providers. The literature review identified how both traditional and mHealth-based approaches (n=15 methods, n=21 measures) were used together to evaluate mHealth interventions. Usage-log analysis revealed that changes in Glycosylated haemoglobin (HbA1c) differed between groups organized by usage patterns and duration of use of mHealth. The mixed-method study demonstrated how to collect comprehensive and complementary information when combining traditional and mHealth-centered approaches and resources. Conclusion: Traditional positivist approaches and resources are not adequate, on their own, to comprehensively understand the impact of mHealth interventions. The presented studies demonstrate that it is both feasible and prudent to combine traditional research with mHealth approaches, such as analyzing usage-logs, arranging co-design workshops, and other patient-centered methods in a pragmatist approach to produce comprehensive evidence of mHealth’s impacts on both patients and HCPs

Evaluating the Intervention Fidelity of Self-managed Computer Therapy for Aphasia Post-stroke

Introduction: Intervention fidelity refers to whether an intervention is delivered as intended by the designer, which can affect intervention success. This study aimed to evaluate fidelity to the StepByStep approach to aphasia computer therapy delivered in the Big CACTUS trial. Methods: A mixed methods approach was adopted comprising five interrelated studies. Firstly, a narrative literature review explored the methods used in fidelity evaluation in stroke rehabilitation research. Secondly, a qualitative interview study with StepByStep approach experts identified the key components of the intervention delivered in the Big CACTUS trial. Both of these studies informed the data to be collected in the third study, a process evaluation of intervention fidelity in the Big CACTUS trial. The fourth study explored the factors associated with adherence to computer therapy practice through secondary analysis of trial data and qualitative interviews with people with aphasia (PWA) and their carers who had used the computer therapy in the trial. The final study identified ‘essential’ components of the intervention associated with improved word-finding in the Big CACTUS trial. Results: Key informants identified four key components of the StepByStep approach: the StepByStep software, therapy set-up (tailoring and personalising), regular independent practice, and supporting and monitoring use. All components of the intervention were delivered with moderate to high fidelity in the Big CACTUS trial. Factors associated with increased adherence to independent practice included: the PWA having had their stroke longer ago; the PWA’s perceived and actual capability to engage with computer therapy; having the opportunity to carry out practice, which was aided by having the computer therapy for longer; having more input from a speech and language therapist; and a number of motivational factors. Exploratory data analysis indicated that the components of the intervention associated with change in word-finding ability were: rigorous tailoring of the computer therapy exercises and spending more time on naming words in functional sentences exercises. Conclusion: The StepByStep approach was delivered with moderate to high fidelity. This study has informed the interpretation of trial results, recommendations for clinicians delivering the intervention in clinical practice and will inform further intervention refinement