Computer analysis of non verbal behavioral parameters in unipolar major depression

2011 ◽  
Vol 26 (S2) ◽  
pp. 1712-1712
Author(s):  
V.V. Enatescu ◽  
V.R. Enatescu ◽  
I. Enatescu
Keyword(s):  
Artificial Intelligence ◽  
Automatic System ◽  
Dimensional Space ◽  
Signs And Symptoms ◽  
Unipolar Depression ◽  
Diagnostic Value ◽  
Structured Interviews ◽  
Behavioral Parameters ◽  
Unipolar Major Depression ◽  
Depressive States

Background and aimsBeside the interpretation and processing of content of communication, an important part of psychiatric diagnosis is made on behavioral signs and symptoms. While the semantic assessment of the content of thinking through communication was enriched by the development of several psychopathological scales, schedule and structured or semi-structured interviews the assessment of non verbal parameters remains uncovered. Our aims was to analyses the non verbal parameters, by an automatic system conceived by Dr. Enatescu et colab., in patients with mood disorders.MethodsThe instrument we used are: original traductors, systems of calculation and programming belonging to the artificial intelligence which create new pattern of representation of the gait, gesture, sonorous background of the speech, the dynamic of the writing which can be represented or through a matrix or in a n-dimensional space on specific clusters or to some human typology or to some psychical disorders.ResultsThe non verbal parameters processed by computer were sensible altered along with switching in the depressive states of subjects. The informatics data has had both diagnostic value and screening value for the course of unipolar depression.ConclusionsWe demonstrate that there is the chances for a new semiology which have objective paraclinic value for psychiatry field of automate analyses, nonverbal behavior parameters having the name “Extraverbale Analysis”.

Diagnostic Value of a Carpal Tunnel Corticosteroid Injection in Patients with Negative Electrodiagnostic Studies

2020 ◽  
Author(s):  
Brian M. Katt ◽  
Casey Imbergamo ◽  
Fortunato Padua ◽  
Joseph Leider ◽  
Daniel Fletcher ◽  
...  
Keyword(s):  
Carpal Tunnel ◽  
Corticosteroid Injection ◽  
Carpal Tunnel Release ◽  
False Negative ◽  
False Negative Rate ◽  
Signs And Symptoms ◽  
Diagnostic Value ◽  
Symptom Improvement ◽  
Study Cohort ◽  
Chi Square Analysis

Abstract Introduction There is a known false negative rate when using electrodiagnostic studies (EDS) to diagnose carpal tunnel syndrome (CTS). This can pose a management dilemma for patients with signs and symptoms that correlate with CTS but normal EDS. While corticosteroid injection into the carpal tunnel has been used in this setting for diagnostic purposes, there is little data in the literature supporting this practice. The purpose of this study is to evaluate the prognostic value of a carpal tunnel corticosteroid injection in patients with a normal electrodiagnostic study but exhibiting signs and symptoms suggestive of carpal tunnel, who proceed with a carpal tunnel release. Materials and Methods The group included 34 patients presenting to an academic orthopedic practice over the years 2010 to 2019 who had negative EDS, a carpal tunnel corticosteroid injection, and a carpal tunnel release. One patient (2.9%), where the response to the corticosteroid injection was not documented, was excluded from the study, yielding a study cohort of 33 patients. Three patients had bilateral disease, yielding 36 hands for evaluation. Statistical analysis was performed using Chi-square analysis for nonparametric data. Results Thirty-two hands (88.9%) demonstrated complete or partial relief of neuropathic symptoms after the corticosteroid injection, while four (11.1%) did not experience any improvement. Thirty-one hands (86.1%) had symptom improvement following surgery, compared with five (13.9%) which did not. Of the 32 hands that demonstrated relief following the injection, 29 hands (90.6%) improved after surgery. Of the four hands that did not demonstrate relief after the injection, two (50%) improved after surgery. This difference was statistically significant (p = 0.03). Conclusion Patients diagnosed with a high index of suspicion for CTS do well with operative intervention despite a normal electrodiagnostic test if they have had a positive response to a preoperative injection. The injection can provide reassurance to both the patient and surgeon before proceeding to surgery. Although patients with a normal electrodiagnostic test and no response to cortisone can still do well with surgical intervention, the surgeon should carefully review both the history and physical examination as surgical success may decrease when both diagnostic tests are negative. Performing a corticosteroid injection is an additional diagnostic tool to consider in the management of patients with CTS and normal electrodiagnostic testing.

scholarly journals Which triggers could support timely identification of primary antibody deficiency? A qualitative study using the patient perspective

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Lisanne M. A. Janssen ◽  
Kim van den Akker ◽  
Mohamed A. Boussihmad ◽  
Esther de Vries
Keyword(s):  
Respiratory Infections ◽  
Diagnostic Delay ◽  
Signs And Symptoms ◽  
Negative Influence ◽  
Primary Antibody ◽  
Emotional Impact ◽  
Structured Interviews ◽  
Timely Treatment ◽  
The Way

Abstract Background Patients with predominantly (primary) antibody deficiencies (PADs) commonly develop recurrent respiratory infections which can lead to bronchiectasis, long-term morbidity and increased mortality. Recognizing symptoms and making a diagnosis is vital to enable timely treatment. Studies on disease presentation have mainly been conducted using medical files rather than direct contact with PAD patients. Our study aims to analyze how patients appraised their symptoms and which factors were involved in a decision to seek medical care. Methods 14 PAD-patients (11 women; median 44, range 16-68 years) were analyzed using semi-structured interviews until saturation of key emergent themes was achieved. Results Being always ill featured in all participant stories. Often from childhood onwards periods of illness were felt to be too numerous, too bad, too long-lasting, or antibiotics were always needed to get better. Recurrent or persistent respiratory infections were the main triggers for patients to seek care. All participants developed an extreme fatigue, described as a feeling of physical and mental exhaustion and thus an extreme burden on daily life that was not solved by taking rest. Despite this, participants tended to normalize their symptoms and carry on with usual activities. Non-immunologists, as well as patients, misattributed the presenting signs and symptoms to common, self-limiting illnesses or other ‘innocent’ explanations. Participants in a way understood the long diagnostic delay. They know that the disease is rare and that doctors have to cover a broad medical area. But they were more critical about the way the doctors communicate with them. They feel that doctors often don’t listen very well to their patients. The participants’ symptoms as well as the interpretation of these symptoms by their social environment and doctors had a major emotional impact on the participants and a negative influence on their future perspectives. Conclusions To timely identify PAD, ‘pattern recognition’ should not only focus on the medical ‘red flags’, but also on less differentiating symptoms, such as ‘being always ill’ and ‘worn out’ and the way patients cope with these problems. And, most important, making time to really listen to the patient remains the key.

scholarly journals Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries

2021 ◽  
Vol 14 (8) ◽  
pp. 339
Author(s):  
Tatjana Vasiljeva ◽  
Ilmars Kreituss ◽  
Ilze Lulle
Keyword(s):  
Artificial Intelligence ◽  
Primary Data ◽  
Structured Interviews ◽  
The Public ◽  
Qualitative And Quantitative ◽  
Public Survey ◽  
Significant Difference ◽  
Main Factors ◽  
Negative Factors ◽  
Near Future

This paper looks at public and business attitudes towards artificial intelligence, examining the main factors that influence them. The conceptual model is based on the technology–organization–environment (TOE) framework and was tested through analysis of qualitative and quantitative data. Primary data were collected by a public survey with a questionnaire specially developed for the study and by semi-structured interviews with experts in the artificial intelligence field and management representatives from various companies. This study aims to evaluate the current attitudes of the public and employees of various industries towards AI and investigate the factors that affect them. It was discovered that attitude towards AI differs significantly among industries. There is a significant difference in attitude towards AI between employees at organizations with already implemented AI solutions and employees at organizations with no intention to implement them in the near future. The three main factors which have an impact on AI adoption in an organization are top management’s attitude, competition and regulations. After determining the main factors that influence the attitudes of society and companies towards artificial intelligence, recommendations are provided for reducing various negative factors. The authors develop a proposition that justifies the activities needed for successful adoption of innovative technologies.

scholarly journals Patient’s experience of psoriatic arthritis: a conceptual model based on qualitative interviews

RMD Open ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e001321
Author(s):  
Alexis Ogdie ◽  
Kaleb Michaud ◽  
Miroslawa Nowak ◽  
Rachel Bruce ◽  
Sarah Cantor ◽  
...  
Keyword(s):  
Psoriatic Arthritis ◽  
Conceptual Model ◽  
Patient Experience ◽  
Qualitative Interviews ◽  
Signs And Symptoms ◽  
Structured Interviews ◽  
Skin Symptoms ◽  
Patient’S Experience ◽  
Significant Burden

IntroductionPsoriatic arthritis (PsA) is a heterogeneous chronic inflammatory musculoskeletal disorder that manifests as peripheral arthritis, dactylitis, enthesitis and spondylitis. PsA results in significant burden that impacts quality of life of patients. We examined the signs, symptoms and impacts reported by patients with PsA, to characterise the patient experience of PsA and develop a conceptual model representing this patient experience.MethodsSemi-structured interviews were conducted with patients with PsA recruited through the FORWARD databank. Spontaneous and probed signs, symptoms and impacts of PsA were assessed. Patients rated the disturbance of these concepts on their lives using a scale from 0 (‘does not disturb’) to 10 (‘greatly disturbs’). Signs, symptoms and impacts reported by >80% of patients with a disturbance rating of ≥5 were defined as salient concepts. Recruitment continued until concept saturation was achieved.Results19 patients with PsA were interviewed. The interviews elicited 42 symptoms of which 8 had not been identified in a previous literature review encompassing 15 relevant articles. The most salient signs and symptoms elicited in the interviews were joint pain, skin symptoms, stiffness, swollen/inflamed joints and fatigue all with moderate to high disturbance ratings (range: 5.5–7.8). The most salient impacts were sleep disturbance, physical disability, effects on daily activities and feelings of frustration with also moderate to high disturbance ratings (range: 6.1–7.4).ConclusionsThe interviews highlighted the adverse impact PsA has on the patient’s life and may inform on outcome variables or areas suitable to be assessed in PsA studies.

Big data and artificial intelligence for health system sustainability: The case of Veneto Region

2021 ◽  
pp. 31-52
Author(s):  
Grazia Dicuonzo ◽  
Francesca Donofrio ◽  
Antonio Fusco ◽  
Vittorio Dell’Atti
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Healthcare System ◽  
Care Delivery ◽  
Research Question ◽  
Health Agency ◽  
Time Frame ◽  
Structured Interviews ◽  
Case Study Methodology ◽  
The Impact

This paper investigates the digitalization challenges facing the Italian healthcare system. The aim of the paper is to support healthcare organizations as they take advantage of the potential of big data and artificial intelligence (AI) to promote sustainable healthcare systems. Both the development of innovative processes in the management of health care activities and the introduction of healthcare forecasting systems are valuable resources for clinical and care activities and enable a more efficient use of inputs in essential-level care delivery. By examining an innovative project developed by the Regional Social Health Agency (ARSS) of Veneto, this study analyses the impact of big data and AI on the sustainability of a healthcare system. In order to answer the research question, we used a case study methodology. We conducted semi-structured interviews with key members of the organizational group involved in the case. The results show that the implementation of AI algorithms based on big data in healthcare both improves the interpretation and processing of data, and reduces the time frame necessary for clinical processes, having a positive effect on sustainability.

scholarly journals Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective

2020 ◽  
Vol 12 (14) ◽  
pp. 5568 ◽  
Author(s):  
Thomas K.F. Chiu ◽  
Ching-sing Chai
Keyword(s):  
Artificial Intelligence ◽  
Curriculum Design ◽  
Teaching Experience ◽  
Self Determination Theory ◽  
Curriculum Planning ◽  
Self Determination ◽  
Structured Interviews ◽  
Theoretical Frameworks ◽  
Intelligence Education ◽  
K 12

The teaching of artificial intelligence (AI) topics in school curricula is an important global strategic initiative in educating the next generation. As AI technologies are new to K-12 schools, there is a lack of studies that inform schools’ teachers about AI curriculum design. How to prepare and engage teachers, and which approaches are suitable for planning the curriculum for sustainable development, are unclear. Therefore, this case study aimed to explore the views of teachers with and without AI teaching experience on key considerations for the preparation, implementation and continuous refinement of a formal AI curriculum for K-12 schools. It drew on the self-determination theory (SDT) and four basic curriculum planning approaches—content, product, process and praxis—as theoretical frameworks to explain the research problems and findings. We conducted semi-structured interviews with 24 teachers—twelve with and twelve without experience in teaching AI—and used thematic analysis to analyze the interview data. Our findings revealed that genuine curriculum creation should encompass all four forms of curriculum design approach that are coordinated by teachers’ self-determination to be orchestrators of student learning experiences. This study also proposed a curriculum development cycle for teachers and curriculum officers.

School leaders' adoption and implementation of artificial intelligence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Matthew Mark Tyson ◽  
Nicholas J. Sauers
Keyword(s):  
Artificial Intelligence ◽  
School Leadership ◽  
School Leaders ◽  
Implementation Process ◽  
The Body ◽  
Snowball Sampling ◽  
Structured Interviews ◽  
Content Type ◽  
Adoption And Implementation

PurposeThe purpose of this study is to examine school leaders' experiences with adoption and implementation of artificial intelligence systems in their schools. It examined the factors that led educational administrators to adopt one artificial intelligence program (ALEKS) and their perceptions around the implementation process.Design/methodology/approachThis was a qualitative case study that included structured interviews with seven individuals who have adopted artificial intelligence programs in their schools. Participants were identified through purposive and snowball sampling. Interview transcripts were analyzed and put through a two-step coding process involving in vivo coding as well as pattern coding.FindingsTwo major themes emerged from this study pertaining to the state of the diffusion of artificial intelligence through the adoption and implementation process. The findings indicated that school leaders were actively engaged in conversations related to AI adoption and implementation. They also created structures (organizational) to ensure the successful adoption and implementation of artificial intelligence.Originality/valueThis is an original study that examined the experiences of school leaders who have adopted and implemented artificial intelligence in their schools. The body of literature related to artificial intelligence and school leadership is extremely limited.

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