scholarly journals A general model of conversational dynamics and an example application in serious illness communication

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253124
Author(s):  
Laurence A. Clarfeld ◽  
Robert Gramling ◽  
Donna M. Rizzo ◽  
Margaret J. Eppstein
Keyword(s):  
Information Flow ◽  
Markov Models ◽  
Serious Illness ◽  
Sequential Dependencies ◽  
Novel Approach ◽  
Potential Applications ◽  
Primary Means ◽  
Ancient Times ◽  
Patient Values ◽  
Seriously Ill Patients

Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn’t audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique “fingerprints” of information flow.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

The Surprise Question and Serious Illness Conversations: A pilot study

2021 ◽  
pp. 096973302098339
Author(s):  
Kathy Le ◽  
Jenny Lee ◽  
Sameer Desai ◽  
Anita Ho ◽  
Holly van Heukelom
Keyword(s):  
Pilot Study ◽  
Healthcare Professionals ◽  
Post Intervention ◽  
Acute Medicine ◽  
Serious Illness ◽  
Patient Goals ◽  
Ethical Approval ◽  
Seriously Ill Patients ◽  
Surprise Question ◽  
Seriously Ill

Background: Serious Illness Conversations aim to discuss patient goals. However, on acute medicine units, seriously ill patients may undergo distressing interventions until death. Objectives: To investigate the feasibility of using the Surprise Question, “Would you be surprised if this patient died within the next year?” to identify patients who would benefit from early Serious Illness Conversations and study any changes in the interdisciplinary team’s beliefs, confidence, and engagement as a result of asking the Surprise Question. Design: A prospective cohort pilot study with two Plan-Do-Study-Act cycles. Participants/context: Fifty-eight healthcare professionals working on Acute Medicine Units participated in pre- and post-intervention questionnaires. The intervention involved asking participants the Surprise Question for each patient. Patient charts were reviewed for Serious Illness Conversation documentation. Ethical considerations: Ethical approval was granted by the institutions involved. Findings: Equivocal overall changes in the beliefs, confidence, and engagement of healthcare professionals were observed. Six out of 23 patients were indicated as needing a Serious Illness Conversation; chart review provided some evidence that these patients had more Serious Illness Conversation documentation compared with the 17 patients not flagged for a Serious Illness Conversation. Issues were identified in equating the Surprise Question to a Serious Illness Conversation. Discussion: Appropriate support for seriously ill patients is both a nursing professional and ethical duty. Flagging patients for conversations may act as a filtering process, allowing healthcare professionals to focus on conversations with patients who need them most. There are ethical and practical issues as to what constitutes a “serious illness” and if answering “no” to the Surprise Question always equates to a conversation. Conclusion: The barriers of time constraints and lack of training call for institutional change in order to prioritise the moral obligation of Serious Illness Conversations.

The Optimum Power Load: A Simple and Powerful Tool for Testing and Training

2021 ◽  
pp. 1-9
Author(s):  
Irineu Loturco ◽  
Antonio Dello Iacono ◽  
Fábio Y. Nakamura ◽  
Tomás T. Freitas ◽  
Daniel Boullosa ◽  
...  
Keyword(s):  
Strong Support ◽  
Current Evidence ◽  
Novel Approach ◽  
Power Load ◽  
Velocity Relationship ◽  
Potential Applications ◽  
Bar Velocity ◽  
Acute And Chronic Effects ◽  
Testing Protocols ◽  
And Training

Purpose: The optimal power load is defined as the load that maximizes power output in a given exercise. This load can be determined through the use of various instruments, under different testing protocols. Specifically, the “optimum power load” (OPL) is derived from the load–velocity relationship, using only bar force and bar velocity in the power computation. The OPL is easily assessed using a simple incremental testing protocol, based on relative percentages of body mass. To date, several studies have examined the associations between the OPL and different sport-specific measures, as well as its acute and chronic effects on athletic performance. The aim of this brief review is to present and summarize the current evidence regarding the OPL, highlighting the main lines of research on this topic and discussing the potential applications of this novel approach for testing and training. Conclusions: The validity and simplicity of OPL-based schemes provide strong support for their use as an alternative to more traditional strength–power training strategies. The OPL method can be effectively used by coaches and sport scientists in different sports and populations, with different purposes and configurations.

scholarly journals Conserved Peptides Recognition by Ensemble of Neural Networks for Mining Protein Data – LPMO Case Study

2020 ◽  
Vol 15 (2) ◽  
pp. 429-440
Author(s):  
G.S. Dotsenko ◽  
A.S. Dotsenko
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Markov Models ◽  
Amino Acid Sequences ◽  
Peptide Pattern ◽  
Novel Approach ◽  
Polysaccharide Monooxygenases ◽  
Promising Area ◽  
Peptide Pattern Recognition ◽  
Bacterial Proteomes

Mining protein data is a recent promising area of modern bioinformatics. In this work, we suggested a novel approach for mining protein data – conserved peptides recognition by ensemble of neural networks (CPRENN). This approach was applied for mining lytic polysaccharide monooxygenases (LPMOs) in 19 ascomycete, 18 basidiomycete, and 18 bacterial proteomes. LPMOs are recently discovered enzymes and their mining is of high relevance for biotechnology of lignocellulosic materials. CPRENN was compared with two conventional bioinformatic methods for mining protein data – profile hidden Markov models (HMMs) search (HMMER program) and peptide pattern recognition (PPR program combined with Hotpep application). The maximum number of hypothetical LPMO amino acid sequences was discovered by HMMER. Profile HMMs search proved to be more sensitive method for mining LPMOs than conserved peptides recognition. Totally, CPRENN found 76 %, 67 %, and 65 % of hypothetical ascomycete, basidiomycete, and bacterial LPMOs discovered by HMMER, respectively. For AA9, AA10, and AA11 families which contain the major part of all LPMOs in the carbohydrate-active enzymes database (CAZy), CPRENN and PPR + Hotpep found 69–98 % and 62–95 % of amino acid sequences discovered by HMMER, respectively. In contrast with PPR + Hotpep, CPRENN possessed perfect precision and provided more complete mining of basidiomycete and bacterial LPMOs.

A new approach to characterization of the transition temperatures of thin film NiTi shape memory alloys

2004 ◽  
Vol 19 (6) ◽  
pp. 1762-1767
Author(s):  
Nicholas W. Botterill ◽  
David M. Grant ◽  
Jianxin Zhang ◽  
Clive J. Roberts
Keyword(s):  
Thin Film ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Transition Temperatures ◽  
New Approach ◽  
Novel Approach ◽  
Potential Applications ◽  
Niti Shape Memory Alloys

A novel approach in determining the transition temperatures of NiTi shape memory alloys was investigated and compared with conventional techniques. The technique is based on microthemal analysis using a scanning thermal microscope (SThM). In particular, this method has the potential to allow the transformation temperatures of thin films to be investigated in situ. Thin film shape memory alloys have potential applications, such as microactuators, where conventional analysis techniques are either not directly applicable to such samples or are difficult to perform.

Effect of Electron Beam Irradiation on the Rheological Properties and Phase Transition Temperature of Poly(N-Vinylcaprolactam)

2020 ◽  
Vol 865 ◽  
pp. 19-24
Author(s):  
Shane C. Halligan ◽  
Kieran A. Murray ◽  
Olivier Vrain ◽  
John G. Lyons ◽  
Luke M. Geever
Keyword(s):  
Phase Transition ◽  
Electron Beam ◽  
Rheological Properties ◽  
Smart Materials ◽  
Electron Beam Irradiation ◽  
Beam Irradiation ◽  
Beam Radiation ◽  
Rheological Analysis ◽  
Novel Approach ◽  
Potential Applications

Exposing smart materials to electron beam radiation can induce free radical reactions, such as chain branching or crosslinking, hence enhancing the characteristics of the polymers. Poly (N-vinylcaprolactam) (PNVCL) is a smart material which was synthesised by photopolymerisation. Subsequently, samples were exposed to electron beam technology, where electron beam irradiation was utilised in a novel approach. This led to the modification of the rheological and phase transition properties. Modifying PNVCL through electron beam irradiation opens new avenues and potential applications in the biomedical field. Physically cross-linked PNVCL polymers were prepared by photopolymerisation and samples were subsequently irradiated at different dose ranges (5kGy, 25kGy and 50 kGy). The rheological properties of the PNVCL based samples were established by rheological analysis. Similarly, the PNVCL based sample polymers were further characterised in solution to determine the phase transition of PNVCL.

Improving Dependability of Robotics Systems, Experience from Application of Fault Tree Synthesis to Analysis of Transport Systems

2015 ◽  
Vol 3 (2) ◽  
pp. 38-62 ◽  
Author(s):  
Nidhal Mahmud
Keyword(s):  
Electric Vehicles ◽  
Fault Tolerant ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Transport Systems ◽  
Sequential Dependencies ◽  
Novel Approach ◽  
Assessment Techniques ◽  
Tree Synthesis

The use of robotics systems is increasingly widespread and spans a variety of application areas. From manufacturing, to surgeries, to chemical, these systems can be required to perform difficult, dangerous and critical tasks. The nature of such tasks places high demands on the dependability of robotics systems. Fault tree analysis is among the most often used dependability assessment techniques in various domains of robotics. However, there is still a lack of adjustment methods that can efficiently cope with the sequential dependencies among the components of such systems. In this paper, the authors first introduce some relevant techniques to analyze the dependability of robotics systems. Thereafter, an experience from research projects such as MAENAD (European automotive project investigating development of dependable Fully Electric Vehicles) is presented; emphasis is put on a novel approach to synthesizing fault trees from the components and that is suitable for modern high-technology robotics. Finally, the benefits of the approach are highlighted by using a fault-tolerant case study.

Delivering more, earlier, and better goals-of-care conversations to seriously ill oncology patients.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 39-39 ◽  
Author(s):  
Rachelle Bernacki ◽  
Joanna Paladino ◽  
Daniela Lamas ◽  
Mathilde Hutchings ◽  
Josh Lakin ◽  
...  
Keyword(s):  
Chart Review ◽  
Systematic Approach ◽  
Care Program ◽  
Cluster Randomized Trial ◽  
Patient Centered ◽  
Goals Of Care ◽  
Serious Illness ◽  
Clinician Training ◽  
Patient Values

39 Background: Patients with serious illness routinely receive treatments that are not aligned with their goals. Earlier clinical conversations about patients’ values and priorities lead to more goal-concordant care and improved quality of life, but these conversations often happen too late or not at all. The Serious Illness Care Program has designed a systematic approach to train and support clinicians in conducting more, earlier, and better conversations about goals of care with their patients. Objectives: Evaluate clinician adoption and acceptability of the training program and the Serious Illness Conversation Guide; determine the frequency, timing, and quality of goals of care documentation before death. Methods: Cluster-randomized trial including oncology clinicians and their patients. Intervention: clinician identification of high risk patients; 2½ hour clinician training on the Serious Illness Conversation Guide; email trigger/reminder; EMR documentation. Preliminary chart review to extract goals-of-care conversations for deceased patients in intervention and control. Results: 90 oncology clinicians: 47 intervention; 43 control. Of 47 intervention clinicians, 46 are trained and rate the training as effective (4.3/5). 97% of trained clinicians who have been triggered have adopted the Conversation Guide and find it acceptable (4.2/5). 342 patients enrolled: 176 intervention; 166 control; 38% have died (n = 131). A preliminary chart review revealed more goals-of-care conversations occurred before death in intervention compared to control (92% versus 70%, p = 0.0037); intervention conversations took place four months earlier than control (median 143 days versus 63 days, p = 0.0008). In addition, conversations were more patient centered (95% versus 45%, p < 0.001) and more readily retrievable in the EMR (68% versus 28%, p < 0.001). Conclusions: Preliminary data about the Serious Illness Care systematic approach demonstrate strong clinician adoption and acceptability. The intervention results in more, earlier, and better conversations about patient values and priorities, in addition to more patient-centered and retrievable documentation of goals of care in the medical record. Clinical trial information: NCT01786811.

Living goals: Prepare to die.

2017 ◽  
Vol 35 (31_suppl) ◽  
pp. 157-157
Author(s):  
Zankhana Mehta ◽  
Susan Smith ◽  
Jane Henrichs ◽  
Andrea Berger ◽  
Loreen Comstock ◽  
...  
Keyword(s):  
Palliative Medicine ◽  
Timely Manner ◽  
Median Length ◽  
Serious Illness ◽  
Home Based ◽  
Hospice Patients ◽  
One Year ◽  
Seriously Ill Patients ◽  
Surprise Question ◽  
Seriously Ill

157 Background: Living Goals (LGs), a home-based program, is a collaboration between Geisinger Health Plan, Geisinger Home care and Hospice and Palliative Medicine. Program investigated an early intervention in those with serious illness and transition to hospice in a timely manner during FY 2015- 2017. Methods: LGs visits were led by a registered nurse after referral from physicians based on the surprise question (it would not be a surprise if this patient died within one year). Nurse facilitated end-of-life discussions and provided resources available for supportive care. LGs visits were free up to 10 visits to each patient. Results: 94 patients were enrolled in LGs from 128 referrals.59 (63%) patients transitioned to hospice and 41 (69%) were transitioned within first month of LGs visit averaging 1.4 visits. 59 patients enrolled in hospice. The median length of time between LG Start of care (SOC) and Hospice SOC is 9 days (IQR: 3-73).57 LGs patients discharged from hospice had a median length of stay (LOS) of 39 days (IQR: 10-85). For the subset of patients in FY 2015 (n = 13) median LOS was 26 days (IQR: 10-57)), in FY 2016 (n = 26) the median LOS was 27 days (IQR: 7-73), in FY 2017 (n = 18) the median LOS is 66 days (IQR: 28-144). In FY 2014, 513 hospice patients were discharged before initiation of LGs and the median LOS was 19 days (IQR: 6-61). As of now, there is a significantly longer stay for LG patients than FY 2014 hospice patients (p = 0.0130). Conclusions: This innovative home based program, appears to have a great potential in the future for delivering ongoing hospice and palliative needs to seriously ill patients.

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