scholarly journals Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

10.2196/16669 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16669
Author(s):  
Marcus D'Souza ◽  
Caspar E P Van Munster ◽  
Jonas F Dorn ◽  
Alexis Dorier ◽  
Christian P Kamm ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Data Privacy ◽  
Neurological Diseases ◽  
Weighted Kappa ◽  
Relevant Information ◽  
Machine Learning Algorithms ◽  
Motor Dysfunction ◽  
Proof Of Concept ◽  
Concept Study

Background In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non–health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. Objective The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. Methods In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. Results In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. Conclusions The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non–health-care professionals are involved.

scholarly journals Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study (Preprint)

2019 ◽  
Author(s):  
Marcus D'Souza ◽  
Caspar E P Van Munster ◽  
Jonas F Dorn ◽  
Alexis Dorier ◽  
Christian P Kamm ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Data Privacy ◽  
Neurological Diseases ◽  
Weighted Kappa ◽  
Relevant Information ◽  
Machine Learning Algorithms ◽  
Motor Dysfunction ◽  
Proof Of Concept ◽  
Concept Study

BACKGROUND In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non–health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. OBJECTIVE The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. METHODS In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. RESULTS In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. CONCLUSIONS The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non–health-care professionals are involved.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Research Priorities for Rare Neurological Diseases: A Representative View of Patient Representatives and Health Care Professionals From the European Reference Network for Rare Neurological Diseases

2020 ◽  
Author(s):  
Annemarie Post ◽  
Thomas Klockgether ◽  
G. Bernhard Landwehrmeyer ◽  
Massimo Pandolfo ◽  
Astri Arnesen ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Neurological Diseases ◽  
Research Priorities ◽  
Disease Mechanisms ◽  
Research Theme ◽  
Research Themes ◽  
Patient Representatives ◽  
The Impact

Abstract Background: Patient involvement in research increases the impact of research and the likelihood of adoption in clinical practice. A first step is to know which research themes are important for patients. We distributed a survey on research priorities to ERN-RND members, both patient representatives and health care professionals, asking them to prioritize five research themes for rare neurological diseases on a scale ranging from 1 (most important) to 5 (least important). A follow-up e-mail interview was conducted with patient representatives and professionals to assess potential reasons for differences in opinions between these two groups.Results: In total, 156 responses were analysed: 61 from professionals and 95 from patient representatives. They covered all ERN-RND disease groups and came from 20 different EU countries. Almost half of the respondents considered ‘Developing therapies and preventive strategies’ the most important research theme. In particular, patient representatives prioritized this theme more often than professionals, while professionals prioritized ‘Disease mechanisms and models’. Patient representatives indicated that therapies and prevention were of the utmost importance to them, because their lives are often heavily impacted by the disease and their main goal is to relief the burden of disease. Professionals indicated that investigating disease mechanisms will lead to more knowledge and is indispensable for finding new treatments.Conclusions: Patients and professionals have different opinions on which research theme should have priority. A qualitative follow-up shows that they respect each others’ view points. Different stakeholders involved in research should be aware of their differences in research theme priority. Explaining these differences to each other leads to more understanding, and could improve patient engagement in research.

scholarly journals AB1189 PICASO - THE PLATFORM FOR IMPROVED PERSONAL AND COORDINATED CARE OF CHRONICALLY ILL – SIX MONTHS RESULTS FROM A PROOF-OF-CONCEPT STUDY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1885.1-1885
Author(s):  
J. G. Richter ◽  
G. Chehab ◽  
M. Tomczak ◽  
C. Schwartz ◽  
E. Ricken ◽  
...  
Keyword(s):  
Health Care ◽  
Chronic Diseases ◽  
Health Care System ◽  
Health Monitoring ◽  
User Satisfaction ◽  
User Requirements ◽  
Proof Of Concept ◽  
Concept Study ◽  
Management Processes ◽  
Remote Health

Background:Cross-sectoral coordination of treatment plans and efficient management of patients with chronic diseases and co-morbidities are of great importance. In rheumatoid arthritis (RA) it is essential to orchestrate information available for a patient at various locations, to allow (cost) efficient data use, to optimize management processes and to avoid redundant diagnostics. The information and communication platform developed in the Horizon2020 funded PICASO project (www.picaso-project.eu) supports the management of patients and their data along the continuum of care, consisting of hospitals, outpatient departments, practices, other health service providers via remote health monitoring. The platform might empower patients to improve their self-management of their illnesses.Objectives:What technological expertise and resources do RA patients and physicians have, who are willing to participate in a proof-of-concept study using a modern ICT platform? What is the user satisfaction? What are platform`s clinical implications?Methods:PICASO pursued a user-centered design approach. Platform`s user requirements were determined through workshops and interviews with physicians from various disciplines, patients and other stakeholders in the health care system (e.g. data protection officers). The development was accompanied by so-called “expert walkthroughs” to ensure a user-friendly design. An evaluation concept assessing the usability of the applications, user satisfaction and clinical relevance of the platform was part of the 6-month proof-of-concept study with RA patients and their physicians (rheumatologists and family doctors). A positive ethics vote was obtained.Results:111 user requirements were identified and used to develop the platform. Conformity with the GDPR as well as national regulations were precisely adhered to. All developments are based on the new ‘Fast Healthcare Interoperability Resources’ standard enabling data exchange with other software systems in the healthcare sector. This offers many advantages, e.g. a semantic model for describing the smallest units in the health care system (e.g. medication intake times, diagnostic procedures). Thus information can be linked and made available across sectors. Data can remain with the data owner and role-specific data access is ensured.30 RA patients (80% female) participated, mean age was 58.6±10.8 years, disease duration 12.6±8.5 years, DAS28 2.6±0.9, average number of comorbidities 3.0±1.6. Patients’ IT-experience was heterogeneous. After 6 months evaluations showed a good platform acceptance with an overall rating of 2.3±1.1 (n=27, Likert scale (LS) 1-6) and evaluation of ‘ease of use’ at 2.3±1.2 (n=27, LS 1-6). Usability tests showed that for patients the presentation of (1) tasks to be performed for the management of their disease, (2) results from their remote health monitoring, and (3) patient-reported outcome instruments in a dashboard was clear and easy to understand. Time required for documentation and daily tasks was rated as appropriate by 75.9% of the patients. No major technical problems or impairments due to RA where experienced when using the dashboard. 8 physicians (37.5 % female) participated in the evaluation; overall the platform was rated at 2.2±0.5 (LS 1-6).Conclusion:The platform offers cross-sectoral orchestration of patient data and thus innovative capabilities for modern management processes (e.g. treat-to-target, tele-monitoring). The PICASO platform is available for RA patients as well as for other chronic diseases.Acknowledgments:This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689209.Disclosure of Interests:None declared

scholarly journals Evaluation of a Language Translation App in an Undergraduate Medical Communication Course: Proof-of-Concept and Usability Study

10.2196/31559 ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. e31559
Author(s):  
Anne Herrmann-Werner ◽  
Teresa Loda ◽  
Stephan Zipfel ◽  
Martin Holderried ◽  
Friederike Holderried ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Gold Standard ◽  
Language Translation ◽  
Language Barriers ◽  
Free Text ◽  
Practical Training ◽  
Proof Of Concept ◽  
Medical Communication ◽  
Teaching Unit

Background Language barriers in medical encounters pose risks for interactions with patients, their care, and their outcomes. Because human translators, the gold standard for mitigating language barriers, can be cost- and time-intensive, mechanical alternatives such as language translation apps (LTA) have gained in popularity. However, adequate training for physicians in using LTAs remains elusive. Objective A proof-of-concept pilot study was designed to evaluate the use of a speech-to-speech LTA in a specific simulated physician-patient situation, particularly its perceived usability, helpfulness, and meaningfulness, and to assess the teaching unit overall. Methods Students engaged in a 90-min simulation with a standardized patient (SP) and the LTA iTranslate Converse. Thereafter, they rated the LTA with six items—helpful, intuitive, informative, accurate, recommendable, and applicable—on a 7-point Likert scale ranging from 1 (don’t agree at all) to 7 (completely agree) and could provide free-text responses for four items: general impression of the LTA, the LTA’s benefits, the LTA’s risks, and suggestions for improvement. Students also assessed the teaching unit on a 6-point scale from 1 (excellent) to 6 (insufficient). Data were evaluated quantitatively with mean (SD) values and qualitatively in thematic content analysis. Results Of 111 students in the course, 76 (68.5%) participated (59.2% women, age 20.7 years, SD 3.3 years). Values for the LTA’s being helpful (mean 3.45, SD 1.79), recommendable (mean 3.33, SD 1.65) and applicable (mean 3.57, SD 1.85) were centered around the average of 3.5. The items intuitive (mean 4.57, SD 1.74) and informative (mean 4.53, SD 1.95) were above average. The only below-average item concerned its accuracy (mean 2.38, SD 1.36). Students rated the teaching unit as being excellent (mean 1.2, SD 0.54) but wanted practical training with an SP plus a simulated human translator first. Free-text responses revealed several concerns about translation errors that could jeopardize diagnostic decisions. Students feared that patient-physician communication mediated by the LTA could decrease empathy and raised concerns regarding data protection and technical reliability. Nevertheless, they appreciated the LTA’s cost-effectiveness and usefulness as the best option when the gold standard is unavailable. They also reported wanting more medical-specific vocabulary and images to convey all information necessary for medical communication. Conclusions This study revealed the feasibility of using a speech-to-speech LTA in an undergraduate medical course. Although human translators remain the gold standard, LTAs could be valuable alternatives. Students appreciated the simulated teaching and recognized the LTA’s potential benefits and risks for use in real-world clinical settings. To optimize patients’ and health care professionals’ experiences with LTAs, future investigations should examine specific design options for training interventions and consider the legal aspects of human-machine interaction in health care settings.

scholarly journals Evaluation of a Language Translation App in an Undergraduate Medical Communication Course: Proof-of-Concept and Usability Study (Preprint)

2021 ◽  
Author(s):  
Anne Herrmann-Werner ◽  
Teresa Loda ◽  
Stephan Zipfel ◽  
Martin Holderried ◽  
Friederike Holderried ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Gold Standard ◽  
Language Translation ◽  
Language Barriers ◽  
Free Text ◽  
Practical Training ◽  
Proof Of Concept ◽  
Medical Communication ◽  
Teaching Unit

BACKGROUND Language barriers in medical encounters pose risks for interactions with patients, their care, and their outcomes. Because human translators, the gold standard for mitigating language barriers, can be cost- and time-intensive, mechanical alternatives such as language translation apps (LTA) have gained in popularity. However, adequate training for physicians in using LTAs remains elusive. OBJECTIVE A proof-of-concept pilot study was designed to evaluate the use of a speech-to-speech LTA in a specific simulated physician-patient situation, particularly its perceived usability, helpfulness, and meaningfulness, and to assess the teaching unit overall. METHODS Students engaged in a 90-min simulation with a standardized patient (SP) and the LTA iTranslate Converse. Thereafter, they rated the LTA with six items—<i>helpful</i>, <i>intuitive</i>, <i>informative</i>, <i>accurate</i>, <i>recommendable</i>, and <i>applicable—</i>on a 7-point Likert scale ranging from 1 (<i>don’t agree at all</i>) to 7 (<i>completely agree</i>) and could provide free-text responses for four items: general impression of the LTA, the LTA’s benefits, the LTA’s risks, and suggestions for improvement. Students also assessed the teaching unit on a 6-point scale from 1 (<i>excellent</i>) to 6 (<i>insufficient</i>). Data were evaluated quantitatively with mean (SD) values and qualitatively in thematic content analysis. RESULTS Of 111 students in the course, 76 (68.5%) participated (59.2% women, age 20.7 years, SD 3.3 years). Values for the LTA’s being <i>helpful</i> (mean 3.45, SD 1.79), <i>recommendable</i> (mean 3.33, SD 1.65) and <i>applicable</i> (mean 3.57, SD 1.85) were centered around the average of 3.5. The items <i>intuitive</i> (mean 4.57, SD 1.74) and <i>informative</i> (mean 4.53, SD 1.95) were above average. The only below-average item concerned its <i>accuracy</i> (mean 2.38, SD 1.36). Students rated the teaching unit as being excellent (mean 1.2, SD 0.54) but wanted practical training with an SP plus a simulated human translator first. Free-text responses revealed several concerns about translation errors that could jeopardize diagnostic decisions. Students feared that patient-physician communication mediated by the LTA could decrease empathy and raised concerns regarding data protection and technical reliability. Nevertheless, they appreciated the LTA’s cost-effectiveness and usefulness as the best option when the gold standard is unavailable. They also reported wanting more medical-specific vocabulary and images to convey all information necessary for medical communication. CONCLUSIONS This study revealed the feasibility of using a speech-to-speech LTA in an undergraduate medical course. Although human translators remain the gold standard, LTAs could be valuable alternatives. Students appreciated the simulated teaching and recognized the LTA’s potential benefits and risks for use in real-world clinical settings. To optimize patients’ and health care professionals’ experiences with LTAs, future investigations should examine specific design options for training interventions and consider the legal aspects of human-machine interaction in health care settings.

scholarly journals Mathematical 3-D Model For Whiplash Simulation

1996 ◽  
Vol 13 (2) ◽  
Author(s):  
Donn N. Peterson
Keyword(s):  
Health Care ◽  
Soft Tissue ◽  
American Academy ◽  
Health Care Professionals ◽  
San Antonio ◽  
Equations Of Motion ◽  
Soft Tissue Injuries ◽  
Relevant Information ◽  
Forensic Sciences ◽  
D Model

Several Years Ago, The Author Developed A Mathematical 2-D Model To Quantitatively Describe The Forces, Moments, And Motions Of Vehicles And Dummy Occupants During Collisions. The Equations Of Motion And Applicable Logic Statements Were Programmed In Basic To Use On Pcs (Ibm Compatible Personal Computers). Simulations Of Many Crashes Have Been Successfully Run On Pcs Using The 2-D Model. Clients Are Usually Interested In The Responses Of The Dummy Head And Torso And The Calculated Magnitudes Of Forces And Moments In The Neck Joint. These Data Are Particularly Useful In Litigation Cases Where Medical Findings Are Subjective (E.G. Soft Tissue Injuries) And Opinions Of The Treating And Adverse Health Care Professionals Differ Significantly. When Additional Relevant Information Has Been Received, The Model Has Been Refined. The Model Has Been Modified To Accommodate Crashes In Which The Target And Bullet Vehicles Are Off-Center And Reasonably Close In Axial Alignment. A Description Of The 2-D Model Was Presented At The 1993 Annual Meeting Of The American Academy Of Forensic Sciences At San Antonio, Texas. In A Particular Recent Case, The Nature Of The Collision And The Posture Of The Occupant Driver Are Not Within The Inherent Constraints Of The 2-D Model. In Order To Adequately Study This Case, A Mathematical 3-D Model Would Be Needed.

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