Inside a Digital Experiment: Co-producing Telecare Services for Older People

The problem of the user remains central to information systems research and practice, more so given the importance now given to user-led innovation. Telecare is a much-vaunted example of e-enabled health and social care which, over the past decade or more has received considerable policy attention and investment in Europe and beyond. However, it appears that the technological opportunities offered have not been taken-up in everyday practice and that the engagement of users—service providers and end users—has been identified as a major barrier. This article presents the experience of a European level project that sought to use a form of co-production to engage users in the development of a telecare system for older people. The outcome was a platform with infrastructural properties and a service-orientated architecture better able to support subsequent innovation in use

Doing Infrastructural Work: The Role of Boundary Objects in Health Information Infrastructure Projects

By their nature information infrastructures require the co-operation of a broad range of diverse stakeholders and interests in order emerge and evolve over-time. Boundary objects provide a means through which those from different social worlds can collaborate without having to reach a consensus in order to do so. In this article we explore the role of such objects, whose infrastructural properties have often been overlooked. We respond to calls to examine the different types of objects used to elicit feedback from potential users and other stakeholders in complex information system projects. Our focus is specifically on health information systems and in particular those involving the implementation of electronic record systems at a national or regional scale. Such projects are notoriously complex and are frequently marked by a diversity of intentions and lack of agreement. When attempted at a national scale at least, they typically fail to meet intended objectives and projects are often abandoned altogether. We suggest that understanding how different types of boundary object—repositories or ideal types—inhibit infrastructural development can assist in understanding these difficulties and point to ways of better supporting the generativity required for the infrastructuralisaton of complex information system

Improved i-Vector Representation for Speaker Diarization

This paper proposes using a previously well-trained deep neural network (DNN) to enhance the i-vector representation used for speaker diarization. In effect, we replace the Gaussian Mixture Model (GMM) typically used to train a Universal Background Model (UBM), with a DNN that has been trained using a different large scale dataset. To train the T-matrix we use a supervised UBM obtained from the DNN using filterbank input features to calculate the posterior information, and then MFCC features to train the UBM instead of a traditional unsupervised UBM derived from single features. Next we jointly use DNN and MFCC features to calculate the zeroth and first order Baum-Welch statistics for training an extractor from which we obtain the i-vector. The system will be shown to achieve a significant improvement on the NIST 2008 speaker recognition evaluation (SRE) telephone data task compared to state-of-the-art approaches

What makes audio event detection harder than classification?

Audio event classification and detection (AEC/D) have been an active field of research in recent years [1]–[3]. So far, beside a majority of works focusing on the improving overall performance in terms of accuracy [2], [1], [4], [5], many other aspects have also been studied, including noise robustness [6]–[7], [8], overlapping event handling [9], [10], [11], [12], early event detection [13], multi-channel fusion [14], as well as generic representation [15]. However, little attention has been paid to the important aspect of event detection systems on continuous streams: false positive reduction. False positives, i.e., event instances that are spuriously detected by a detection system, and subsequently draw attention to them, are arguably one of the most important problems faced by different applications like ambient intelligence and surveillance. To the best knowledge of the authors, this is the first work explicitly addressing this problem

Agreement between telehealth and in-person assessment of patients with chronic musculoskeletal conditions presenting to an advanced-practice physiotherapy screening clinic

Objective: To determine the level of agreement between a telehealth and in-person assessment of a representative sample of patients with chronic musculoskeletal conditions referred to an advanced-practice physiotherapy screening clinic. Design: Repeated-measures study design. Participants: 42 patients referred to the Neurosurgical & Orthopaedic Physiotherapy Screening Clinic (Queensland, Australia) for assessment of their chronic lumbar spine, knee or shoulder condition. Intervention: Participants underwent two consecutive assessments by different physiotherapists within a single clinic session. In-person assessments were conducted as per standard clinical practice. Telehealth assessments took place remotely via videoconferencing. Six Musculoskeletal Physiotherapists were paired together to perform both assessment types. Main outcome measures: Clinical management decisions including (i) recommended management pathways, (ii) referral to allied health professions, (iii) clinical diagnostics, and (iv) requirement for further investigations were compared using reliability and agreement statistics. Results: There was substantial agreement (83.3%; 35/42 cases) between in-person and telehealth assessments for recommended management pathways. Moderate to near perfect agreement (AC1 = 0.58–0.9) was reached for referral to individual allied health professionals. Diagnostic agreement was 83.3% between the two delivery mediums, whilst there was substantial agreement (81%; AC1 = 0.74) when requesting further investigations. Overall, participants were satisfied with the telehealth assessment. Conclusion: There is a high level of agreement between telehealth and in-person assessments with respect to clinical management decisions and diagnosis of patients with chronic musculoskeletal conditions managed in an advanced-practice physiotherapy screening clinic. Telehealth can be considered as a viable and effective medium to assess those patients who are unable to attend these services in person

Early detection of continuous and partial audio events using CNN

Sound event detection is an extension of the static auditory classification task into continuous environments, where performance depends jointly upon the detection of overlapping events and their correct classification. Several approaches have been published to date which either develop novel classifiers or employ well-trained static classifiers with a detection front-end. This paper takes the latter approach, by combining a proven CNN classifier acting on spectrogram image features, with time-frequency shaped energy detection that identifies seed regions within the spectrogram that are characteristic of auditory energy events. Furthermore, the shape detector is optimised to allow early detection of events as they are developing. Since some sound events naturally have longer durations than others, waiting until completion of entire events before classification may not be practical in a deployed system. The early detection capability of the system is thus evaluated for the classification of partial events. Performance for continuous event detection is shown to be good, with accuracy being maintained well when detecting partial events

Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks

Zazo R, Lozano-Diez A, Gonzalez-Dominguez J, T. Toledano D, Gonzalez-Rodriguez J (2016) Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks. PLoS ONE 11(1): e0146917. doi:10.1371/journal.pone.0146917Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (similar to 3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.This work has been supported by project CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Señal de Voz (TEC2012-37585-C02-01), funded by Ministerio de Economia y Competitividad, Spain

Cell salvage and donor blood transfusion during cesarean section: A pragmatic, multicentre randomised controlled trial (SALVO)

BACKGROUND: Excessive haemorrhage at cesarean section requires donor (allogeneic) blood transfusion. Cell salvage may reduce this requirement. METHODS AND FINDINGS: We conducted a pragmatic randomised controlled trial (at 26 obstetric units; participants recruited from 4 June 2013 to 17 April 2016) of routine cell salvage use (intervention) versus current standard of care without routine salvage use (control) in cesarean section among women at risk of haemorrhage. Randomisation was stratified, using random permuted blocks of variable sizes. In an intention-to-treat analysis, we used multivariable models, adjusting for stratification variables and prognostic factors identified a priori, to compare rates of donor blood transfusion (primary outcome) and fetomaternal haemorrhage ≥2 ml in RhD-negative women with RhD-positive babies (a secondary outcome) between groups. Among 3,028 women randomised (2,990 analysed), 95.6% of 1,498 assigned to intervention had cell salvage deployed (50.8% had salvaged blood returned; mean 259.9 ml) versus 3.9% of 1,492 assigned to control. Donor blood transfusion rate was 3.5% in the control group versus 2.5% in the intervention group (adjusted odds ratio [OR] 0.65, 95% confidence interval [CI] 0.42 to 1.01, p = 0.056; adjusted risk difference -1.03, 95% CI -2.13 to 0.06). In a planned subgroup analysis, the transfusion rate was 4.6% in women assigned to control versus 3.0% in the intervention group among emergency cesareans (adjusted OR 0.58, 95% CI 0.34 to 0.99), whereas it was 2.2% versus 1.8% among elective cesareans (adjusted OR 0.83, 95% CI 0.38 to 1.83) (interaction p = 0.46). No case of amniotic fluid embolism was observed. The rate of fetomaternal haemorrhage was higher with the intervention (10.5% in the control group versus 25.6% in the intervention group, adjusted OR 5.63, 95% CI 1.43 to 22.14, p = 0.013). We are unable to comment on long-term antibody sensitisation effects. CONCLUSIONS: The overall reduction observed in donor blood transfusion associated with the routine use of cell salvage during cesarean section was not statistically significant. TRIAL REGISTRATION: This trial was prospectively registered on ISRCTN as trial number 66118656 and can be viewed on http://www.isrctn.com/ISRCTN66118656

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy