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Статті в журналах з теми "Depression analysis chatbot":
1
Ahmed, Arfan, Sarah Aziz, Mohamed Khalifa, Uzair Shah, Asma Hassan, Alaa Abd-Alrazaq, and Mowafa Househ. "Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach." JMIR Formative Research 6, no. 3 (March 11, 2022): e27654. http://dx.doi.org/10.2196/27654.
Анотація:
Background Anxiety and depression are among the most commonly prevalent mental health disorders worldwide. Chatbot apps can play an important role in relieving anxiety and depression. Users’ reviews of chatbot apps are considered an important source of data for exploring users’ opinions and satisfaction. Objective This study aims to explore users’ opinions, satisfaction, and attitudes toward anxiety and depression chatbot apps by conducting a thematic analysis of users’ reviews of 11 anxiety and depression chatbot apps collected from the Google Play Store and Apple App Store. In addition, we propose a workflow to provide a methodological approach for future analysis of app review comments. Methods We analyzed 205,581 user review comments from chatbots designed for users with anxiety and depression symptoms. Using scraper tools and Google Play Scraper and App Store Scraper Python libraries, we extracted the text and metadata. The reviews were divided into positive and negative meta-themes based on users’ rating per review. We analyzed the reviews using word frequencies of bigrams and words in pairs. A topic modeling technique, latent Dirichlet allocation, was applied to identify topics in the reviews and analyzed to detect themes and subthemes. Results Thematic analysis was conducted on 5 topics for each sentimental set. Reviews were categorized as positive or negative. For positive reviews, the main themes were confidence and affirmation building, adequate analysis, and consultation, caring as a friend, and ease of use. For negative reviews, the results revealed the following themes: usability issues, update issues, privacy, and noncreative conversations. Conclusions Using a machine learning approach, we were able to analyze ≥200,000 comments and categorize them into themes, allowing us to observe users’ expectations effectively despite some negative factors. A methodological workflow is provided for the future analysis of review comments.
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Hungerbuehler, Ines, Kate Daley, Kate Cavanagh, Heloísa Garcia Claro, and Michael Kapps. "Chatbot-Based Assessment of Employees’ Mental Health: Design Process and Pilot Implementation." JMIR Formative Research 5, no. 4 (April 21, 2021): e21678. http://dx.doi.org/10.2196/21678.
Анотація:
Background Stress, burnout, and mental health problems such as depression and anxiety are common, and can significantly impact workplaces through absenteeism and reduced productivity. To address this issue, organizations must first understand the extent of the difficulties by mapping the mental health of their workforce. Online surveys are a cost-effective and scalable approach to achieve this but typically have low response rates, in part due to a lack of interactivity. Chatbots offer one potential solution, enhancing engagement through simulated natural human conversation and use of interactive features. Objective The aim of this study was to explore if a text-based chatbot is a feasible approach to engage and motivate employees to complete a workplace mental health assessment. This paper describes the design process and results of a pilot implementation. Methods A fully automated chatbot (“Viki”) was developed to evaluate employee risks of suffering from depression, anxiety, stress, insomnia, burnout, and work-related stress. Viki uses a conversation style and gamification features to enhance engagement. A cross-sectional analysis was performed to gain first insights of a pilot implementation within a small to medium–sized enterprise (120 employees). Results The response rate was 64.2% (77/120). In total, 98 employees started the assessment, 77 of whom (79%) completed it. The majority of participants scored in the mild range for anxiety (20/40, 50%) and depression (16/28, 57%), in the moderate range for stress (10/22, 46%), and at the subthreshold level for insomnia (14/20, 70%) as defined by their questionnaire scores. Conclusions A chatbot-based workplace mental health assessment seems to be a highly engaging and effective way to collect anonymized mental health data among employees with response rates comparable to those of face-to-face interviews.
3
Greer, Stephanie, Danielle Ramo, Yin-Juei Chang, Michael Fu, Judith Moskowitz, and Jana Haritatos. "Use of the Chatbot “Vivibot” to Deliver Positive Psychology Skills and Promote Well-Being Among Young People After Cancer Treatment: Randomized Controlled Feasibility Trial." JMIR mHealth and uHealth 7, no. 10 (October 31, 2019): e15018. http://dx.doi.org/10.2196/15018.
Анотація:
Background Positive psychology interventions show promise for reducing psychosocial distress associated with health adversity and have the potential to be widely disseminated to young adults through technology. Objective This pilot randomized controlled trial examined the feasibility of delivering positive psychology skills via the Vivibot chatbot and its effects on key psychosocial well-being outcomes in young adults treated for cancer. Methods Young adults (age 18-29 years) were recruited within 5 years of completing active cancer treatment by using the Vivibot chatbot on Facebook messenger. Participants were randomized to either immediate access to Vivibot content (experimental group) or access to only daily emotion ratings and access to full chatbot content after 4 weeks (control). Created using a human-centered design process with young adults treated for cancer, Vivibot content includes 4 weeks of positive psychology skills, daily emotion ratings, video, and other material produced by survivors, and periodic feedback check-ins. All participants were assessed for psychosocial well-being via online surveys at baseline and weeks 2, 4, and 8. Analyses examined chatbot engagement and open-ended feedback on likability and perceived helpfulness and compared experimental and control groups with regard to anxiety and depression symptoms and positive and negative emotion changes between baseline and 4 weeks. To verify the main effects, follow-up analyses compared changes in the main outcomes between 4 and 8 weeks in the control group once participants had access to all chatbot content. Results Data from 45 young adults (36 women; mean age: 25 [SD 2.9]; experimental group: n=25; control group: n=20) were analyzed. Participants in the experimental group spent an average of 74 minutes across an average of 12 active sessions chatting with Vivibot and rated their experience as helpful (mean 2.0/3, SD 0.72) and would recommend it to a friend (mean 6.9/10; SD 2.6). Open-ended feedback noted its nonjudgmental nature as a particular benefit of the chatbot. After 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units. A mixed-effects models revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41. Those in the experimental group also experienced greater reductions in anxiety when they engaged in more sessions (z=–1.9, P=.06). There were no significant (or trend level) effects by group on changes in depression, positive emotion, or negative emotion. Conclusions The chatbot format provides a useful and acceptable way of delivering positive psychology skills to young adults who have undergone cancer treatment and supports anxiety reduction. Further analysis with a larger sample size is required to confirm this pattern.
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Trappey, Amy J. C., Aislyn P. C. Lin, Kevin Y. K. Hsu, Charles V. Trappey, and Kevin L. K. Tu. "Development of an Empathy-Centric Counseling Chatbot System Capable of Sentimental Dialogue Analysis." Processes 10, no. 5 (May 8, 2022): 930. http://dx.doi.org/10.3390/pr10050930.
Анотація:
College students encounter various types of stresses in school due to schoolwork, personal relationships, health issues, and future career concerns. Some students are susceptible to the strikes of failures and are inexperienced with or fearful of dealing with setbacks. When these negative emotions gradually accumulate without resolution, they can cause long-term negative effects on students’ physical and mental health. Some potential health problems include depression, anxiety, and disorders such as eating disorders. Universities commonly offer counseling services; however, the demand often exceeds the counseling capacities due to limited numbers of counsellors/psychologists. Thus, students may not receive immediate counseling or treatments. If students are not treated, some repercussions may lead to severe abnormal behavior and even suicide. In this study, combining immersive virtual reality (VR) technique with psychological knowledge base, we developed a VR empathy-centric counseling chatbot (VRECC) that can complementarily support troubled students when counsellors cannot provide immediate support. Through multi-turn (verbal or text) conversations with the chatbot, the system can demonstrate empathy and give therapist-like responses to the users. During the study, more than 120 students were required to complete a questionnaire and 34 subjects with an above-median stress level were randomly drawn for the VRECC experiment. We observed decreasing average stress level and psychological sensitivity scores among subjects after the experiment. Although the system did not yield improvement in life-impact scores (e.g., behavioral and physical impacts), the significant outcomes of lowering stress level and psychological sensitivity have given us a very positive outlook for continuing to integrate VR, AI sentimental natural language process, and counseling chatbot for advanced VRECC research in helping students improve their psychological well-being and life quality at schools.
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Chaix, Benjamin, Guillaume Delamon, Arthur Guillemassé, Benoît Brouard, and Jean-Emmanuel Bibault. "Psychological distress during the COVID-19 pandemic in France: a national assessment of at-risk populations." General Psychiatry 33, no. 6 (November 2020): e100349. http://dx.doi.org/10.1136/gpsych-2020-100349.
Анотація:
BackgroundLockdowns were implemented to limit the spread of COVID-19. Peritraumatic distress (PD) and post-traumatic stress disorder have been reported after traumatic events, but the specific effect of the pandemic is not well known.AimThe aim of this study was to assess PD in France, a country where COVID-19 had such a dramatic impact that it required a country-wide lockdown.MethodsWe recruited patients in four groups of chatbot users followed for breast cancer, asthma, depression and migraine. We used the Psychological Distress Inventory (PDI), a validated scale to measure PD during traumatic events, and correlated PD risk with patients’ characteristics in order to better identify the ones who were the most at risk.ResultsThe study included 1771 participants. 91.25% (n=1616) were female with a mean age of 32.8 (13.71) years and 7.96% (n=141) were male with a mean age of 28.0 (8.14) years. In total, 38.06% (n=674) of the respondents had psychological distress (PDI ≥14). An analysis of variance showed that unemployment and depression were significantly associated with a higher PDI score. Patients using their smartphones or computers for more than 1 hour a day also had a higher PDI score (p=0.026).ConclusionPrevalence of PD in at-risk patients is high. These patients are also at an increased risk of developing post-traumatic stress disorder. Specific steps should be implemented to monitor and prevent PD through dedicated mental health policies if we want to limit the public health impact of COVID-19 in time.Trial registration numberNCT04337047.
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Leo, Ashwin J., Matthew J. Schuelke, Devyani M. Hunt, John P. Metzler, J. Philip Miller, Patricia A. Areán, Melissa A. Armbrecht, and Abby L. Cheng. "A Digital Mental Health Intervention in an Orthopedic Setting for Patients With Symptoms of Depression and/or Anxiety: Feasibility Prospective Cohort Study." JMIR Formative Research 6, no. 2 (February 21, 2022): e34889. http://dx.doi.org/10.2196/34889.
Анотація:
Background Symptoms of depression and anxiety commonly coexist with chronic musculoskeletal pain, and when this occurs, standard orthopedic treatment is less effective. However, mental health intervention is not yet a routine part of standard orthopedic treatment, in part because of access-related barriers. Digital mental health intervention is a potential scalable resource that could be feasibly incorporated into orthopedic care. Objective This study’s primary purpose was to assess the feasibility of introducing a digital mental health intervention (Wysa) in an outpatient orthopedic setting to patients with coexisting symptoms of depression and/or anxiety. The secondary purpose was to perform a preliminary effectiveness analysis of the intervention. Methods In this single-arm, prospective cohort study, participants included adult patients (18 years and older) who presented to a nonsurgical orthopedic specialist at a single tertiary care academic center for evaluation of a musculoskeletal condition and who self-reported symptoms of depression and/or anxiety (Patient-Reported Outcomes Measurement Information System [PROMIS] Depression and/or Anxiety score ≥55). Face-to-face enrollment was performed by a research coordinator immediately after the participant’s encounter with an orthopedic clinician. Participants were provided 2 months of access to a mobile app called Wysa, which is an established, multicomponent digital mental health intervention that uses chatbot technology and text-based access to human counselors to deliver cognitive behavioral therapy, mindfulness training, and sleep tools, among other features. For this study, Wysa access also included novel, behavioral activation–based features specifically developed for users with chronic pain. Primary feasibility outcomes included the study recruitment rate, retention rate, and engagement rate with Wysa (defined as engagement with a therapeutic Wysa tool at least once during the study period). Secondary effectiveness outcomes were between-group differences in mean longitudinal PROMIS mental and physical health score changes at 2-month follow-up between high and low Wysa users, defined by a median split. Results The recruitment rate was 29.3% (61/208), retention rate was 84% (51/61), and engagement rate was 72% (44/61). Compared to low users, high users reported greater improvement in PROMIS Anxiety scores (between-group difference −4.2 points, 95% CI −8.1 to −0.2; P=.04) at the 2-month follow-up. Between-group differences in PROMIS Depression (−3.2 points, 95% CI −7.5 to 1.2; P=.15) and Pain Interference scores (−2.3 points, 95% CI −6.3 to 1.7; P=.26) favored high users but did not meet statistical significance. Improvements in PROMIS Physical Function scores were comparable between groups. Conclusions Delivery of a digital mental health intervention within the context of orthopedic care is feasible and has the potential to improve mental health and pain-related impairment to a clinically meaningful degree. Participants’ engagement rates exceeded industry standards, and additional opportunities to improve recruitment and retention were identified. Further pilot study followed by a definitive, randomized controlled trial is warranted. Trial Registration ClinicalTrials.gov NCT04640090; https://clinicaltrials.gov/ct2/show/NCT04640090
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Fulmer, Russell, Angela Joerin, Breanna Gentile, Lysanne Lakerink, and Michiel Rauws. "Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial." JMIR Mental Health 5, no. 4 (December 13, 2018): e64. http://dx.doi.org/10.2196/mental.9782.
Анотація:
Background Students in need of mental health care face many barriers including cost, location, availability, and stigma. Studies show that computer-assisted therapy and 1 conversational chatbot delivering cognitive behavioral therapy (CBT) offer a less-intensive and more cost-effective alternative for treating depression and anxiety. Although CBT is one of the most effective treatment methods, applying an integrative approach has been linked to equally effective posttreatment improvement. Integrative psychological artificial intelligence (AI) offers a scalable solution as the demand for affordable, convenient, lasting, and secure support grows. Objective This study aimed to assess the feasibility and efficacy of using an integrative psychological AI, Tess, to reduce self-identified symptoms of depression and anxiety in college students. Methods In this randomized controlled trial, 75 participants were recruited from 15 universities across the United States. All participants completed Web-based surveys, including the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Positive and Negative Affect Scale (PANAS) at baseline and 2 to 4 weeks later (T2). The 2 test groups consisted of 50 participants in total and were randomized to receive unlimited access to Tess for either 2 weeks (n=24) or 4 weeks (n=26). The information-only control group participants (n=24) received an electronic link to the National Institute of Mental Health’s (NIMH) eBook on depression among college students and were only granted access to Tess after completion of the study. Results A sample of 74 participants completed this study with 0% attrition from the test group and less than 1% attrition from the control group (1/24). The average age of participants was 22.9 years, with 70% of participants being female (52/74), mostly Asian (37/74, 51%), and white (32/74, 41%). Group 1 received unlimited access to Tess, with daily check-ins for 2 weeks. Group 2 received unlimited access to Tess with biweekly check-ins for 4 weeks. The information-only control group was provided with an electronic link to the NIMH’s eBook. Multivariate analysis of covariance was conducted. We used an alpha level of .05 for all statistical tests. Results revealed a statistically significant difference between the control group and group 1, such that group 1 reported a significant reduction in symptoms of depression as measured by the PHQ-9 (P=.03), whereas those in the control group did not. A statistically significant difference was found between the control group and both test groups 1 and 2 for symptoms of anxiety as measured by the GAD-7. Group 1 (P=.045) and group 2 (P=.02) reported a significant reduction in symptoms of anxiety, whereas the control group did not. A statistically significant difference was found on the PANAS between the control group and group 1 (P=.03) and suggests that Tess did impact scores. Conclusions This study offers evidence that AI can serve as a cost-effective and accessible therapeutic agent. Although not designed to appropriate the role of a trained therapist, integrative psychological AI emerges as a feasible option for delivering support. Trial Registration International Standard Randomized Controlled Trial Number: ISRCTN61214172; https://doi.org/10.1186/ISRCTN61214172.
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Shan, Yi, Meng Ji, Wenxiu Xie, Kam-Yiu Lam, and Chi-Yin Chow. "Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis." JMIR Human Factors 9, no. 4 (December 2, 2022): e38799. http://dx.doi.org/10.2196/38799.
Анотація:
Background Mental disorders (MDs) impose heavy burdens on health care (HC) systems and affect a growing number of people worldwide. The use of mobile health (mHealth) apps empowered by artificial intelligence (AI) is increasingly being resorted to as a possible solution. Objective This study adopted a topic modeling (TM) approach to investigate the public trust in AI apps in mental health care (MHC) by identifying the dominant topics and themes in user reviews of the 8 most relevant mental health (MH) apps with the largest numbers of reviewers. Methods We searched Google Play for the top MH apps with the largest numbers of reviewers, from which we selected the most relevant apps. Subsequently, we extracted data from user reviews posted from January 1, 2020, to April 2, 2022. After cleaning the extracted data using the Python text processing tool spaCy, we ascertained the optimal number of topics, drawing on the coherence scores and used latent Dirichlet allocation (LDA) TM to generate the most salient topics and related terms. We then classified the ascertained topics into different theme categories by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand the status of public trust in AI apps in MHC. Results From the top 20 MH apps with the largest numbers of reviewers retrieved, we chose the 8 (40%) most relevant apps: (1) Wysa: Anxiety Therapy Chatbot; (2) Youper Therapy; (3) MindDoc: Your Companion; (4) TalkLife for Anxiety, Depression & Stress; (5) 7 Cups: Online Therapy for Mental Health & Anxiety; (6) BetterHelp-Therapy; (7) Sanvello; and (8) InnerHour. These apps provided 14.2% (n=559), 11.0% (n=431), 13.7% (n=538), 8.8% (n=356), 14.1% (n=554), 11.9% (n=468), 9.2% (n=362), and 16.9% (n=663) of the collected 3931 reviews, respectively. The 4 dominant topics were topic 4 (cheering people up; n=1069, 27%), topic 3 (calming people down; n=1029, 26%), topic 2 (helping figure out the inner world; n=963, 25%), and topic 1 (being an alternative or complement to a therapist; n=870, 22%). Based on topic coherence and intertopic distance, topics 3 and 4 were combined into theme 3 (dispelling negative emotions), while topics 2 and 1 remained 2 separate themes: theme 2 (helping figure out the inner world) and theme 1 (being an alternative or complement to a therapist), respectively. These themes and topics, though involving some dissenting voices, reflected an overall high status of trust in AI apps. Conclusions This is the first study to investigate the public trust in AI apps in MHC from the perspective of user reviews using the TM technique. The automatic text analysis and complementary manual interpretation of the collected data allowed us to discover the dominant topics hidden in a data set and categorize these topics into different themes to reveal an overall high degree of public trust. The dissenting voices from users, though only a few, can serve as indicators for health providers and app developers to jointly improve these apps, which will ultimately facilitate the treatment of prevalent MDs and alleviate the overburdened HC systems worldwide.
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Abd-Alrazaq, Alaa Ali, Asma Rababeh, Mohannad Alajlani, Bridgette M. Bewick, and Mowafa Househ. "Effectiveness and Safety of Using Chatbots to Improve Mental Health: Systematic Review and Meta-Analysis." Journal of Medical Internet Research 22, no. 7 (July 13, 2020): e16021. http://dx.doi.org/10.2196/16021.
Анотація:
Background The global shortage of mental health workers has prompted the utilization of technological advancements, such as chatbots, to meet the needs of people with mental health conditions. Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual language. While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. Objective This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies. Methods A systematic review was carried out to achieve this objective. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate. Results Of 1048 citations retrieved, we identified 12 studies examining the effect of using chatbots on 8 outcomes. Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported. Conclusions Chatbots have the potential to improve mental health. However, the evidence in this review was not sufficient to definitely conclude this due to lack of evidence that their effect is clinically important, a lack of studies assessing each outcome, high risk of bias in those studies, and conflicting results for some outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42019141219; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019141219
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Dysthe, Kim Kristoffer, Jan Ivar Røssberg, Petter Bae Brandtzaeg, Marita Skjuve, Ole Rikard Haavet, Asbjørn Følstad, and Atle Klovning. "Analyzing User-Generated Web-Based Posts of Adolescents’ Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis." Journal of Medical Internet Research 25 (January 24, 2023): e37289. http://dx.doi.org/10.2196/37289.
Анотація:
Background Depression is common during adolescence. Early intervention can prevent it from developing into more progressive mental disorders. Combining information technology and clinical psychoeducation is a promising way to intervene at an earlier stage. However, data-driven research on the cognitive response to health information targeting adolescents with symptoms of depression is lacking. Objective This study aimed to fill this knowledge gap through a new understanding of adolescents’ cognitive response to health information about depression. This knowledge can help to develop population-specific information technology, such as chatbots, in addition to clinical therapeutic tools for use in general practice. Methods The data set consists of 1870 depression-related questions posted by adolescents on a public web-based information service. Most of the posts contain descriptions of events that lead to depression. On a sample of 100 posts, we conducted a qualitative thematic analysis based on cognitive behavioral theory investigating behavioral, emotional, and symptom responses to beliefs associated with depression. Results Results were organized into four themes. (1) Hopelessness, appearing as a set of negative beliefs about the future, possibly results from erroneous beliefs about the causal link between risk factors and the course of depression. We found beliefs about establishing a sturdy therapy alliance as a responsibility resting on the patient. (2) Therapy hesitancy seemed to be associated with negative beliefs about therapy prognosis and doubts about confidentiality. (3) Social shame appeared as a consequence of impaired daily function when the cause is not acknowledged. (4) Failing to attain social interaction appeared to be associated with a negative symptom response. In contrast, actively obtaining social support reduces symptoms and suicidal thoughts. Conclusions These results could be used to meet the clinical aims stated by earlier psychoeducation development, such as instilling hope through direct reattribution of beliefs about the future; challenging causal attributions, thereby lowering therapy hesitancy; reducing shame through the mechanisms of externalization by providing a tentative diagnosis despite the risk of stigmatizing; and providing initial symptom relief by giving advice on how to open up and reveal themselves to friends and family and balance the message of self-management to fit coping capabilities. An active counseling style advises the patient to approach the social environment, demonstrating an attitude toward self-action.
Дисертації з теми "Depression analysis chatbot":
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Kaywan, Payam. "Human Depression Analysis: An Experimental Study of the Use of AI Botics for Early Detection." Thesis, 2022. https://vuir.vu.edu.au/43946/.
Анотація:
The world is facing a shortage of professional medical staff, a situation which has been exacerbated
by the COVID-19 pandemic which has significantly increased challenges globally and has had an
adverse impact on the health care system. This has also led to additional barriers to patient care access,
specifically for individuals who are in need of constant special care. To address the issue of limited
access to medical professionals, medical assistance can be provided to patients in the form of a chatbot
which acts as a proxy between psychiatrists and patients and is available and accessible 24/7.
Although there has been a degree of success in developing medical chatbots, many medical
professionals believe that the use of chatbots in early depression detection needs to be more practical
which will require further research. In this research, we address the well-known and common
shortcomings which have been discussed in the recent literature. Three of these shortcomings are as
follows: firstly, there is a lack of open-ended questions to enable participants to interact openly and
without any restrictions about their moods and emotions as most bots in the literature constrain the
participants’ responses by limiting them to multiple choice questions which means the participants are
not able to open up and describe their real feelings freely. Secondly, there is a lack of semantic analysis
to draw exact meaning from a text. Thirdly, there is a requirement for participants to make a long-term
commitment in terms of their involvement in the research.
This research introduces a depression analysis chatbot, DEPRA, which aims to resolve some of
these shortcomings and challenges by asking open-ended questions, providing semantic analyses and
automatic depression scoring. DEPRA is developed using contemporary bot platforms, Dialogflow on
Google cloud-based infrastructure, and is integrated with social network platforms such as Facebook.
Most chatbots today are designed for therapeutic purposes. However, the DEPRA chatbot is designed
with a focus on the detection of depression in its early stages. DEPRA is designed based on a structured
early detection depression Standard Interview Guideline the Hamilton Depression Scale (SIGH-D) and
Inventory of Depressive Symptomatology (IDS-C), which is used by professional psychiatrists in triage sessions with patients. DEPRA has been trained with personalized utterances from a focus group.
This research utilizes Natural Language Processing (NLP) to identify the depression level of
participants based on their recorded conversation. DEPRA uses a scoring system to determine the
participant’s depression level and severity.
This research also details a non-clinical trial with 50 participants who interacted with the DEPRA
chatbot. Due to the ethical limitations of this research, such as only residents of Australia and
participants to be in the age group of 18 to 80 years old, we have approached a dataset with only
50 participants. This size of dataset was suitable to conduct and run the research. However, the
future studies will target a more comprehensive dataset. This study was a first stage of utilizing
Chatbot for early detection of depression. In this stage our goal was to develop the system not to run
the clinical trial. Therefore, we required a sample that could assist us mainly to identify the accuracy
of the system developed. Future work is to access evaluation by human expert which goes into the
next phase of the project and could also include extending the sample and/or enhancing the system
further and the assistance would be offered to western health. Therefore, at this stage 50+ sample
sufficed to capture various responses by the people that had participants of different level of
depression.
To evaluate the autoscoring feature of DEPRA, the accuracy of the Machine Learning (ML)
algorithms is calculated. Accordingly, manual scoring is compared with the calculated depression
scores. The average accuracy of the 27 questions related to the linear SVC of the 26 participants’
experiment is 88%, the SGD algorithm of 40 participants’ experiment is 80%, and the linear SVC of
50 participants’ experiment is 87%. Furthermore, the overall satisfaction rate of using DEPRA was
79% indicating that the participants had a high rate of user satisfaction and engagement.
Частини книг з теми "Depression analysis chatbot":
1
Kaywan, Payam, Khandakar Ahmed, Yuan Miao, Ayman Ibaida, and Bruce Gu. "DEPRA: An Early Depression Detection Analysis Chatbot." In Health Information Science, 193–204. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90885-0_18.
2
Pathan, Mohd Shafi, Rushikesh Jain, Rohan Aswani, Kshitij Kulkarni, and Sanchit Gupta. "Anti-Depression Psychotherapist Chatbot for Exam and Study-Related Stress." In Applied Machine Learning for Smart Data Analysis, 21–40. First edition. | New York, NY : CRC Press/Taylor & Francis Group, 2019. | Series: Computational Intelligence in Engineering Problem Solving: CRC Press, 2019. http://dx.doi.org/10.1201/9780429440953-2.
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Giunti, G., M. Isomursu, E. Gabarron, and Y. Solad. "Designing Depression Screening Chatbots." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210719.
Анотація:
Advances in voice recognition, natural language processing, and artificial intelligence have led to the increasing availability and use of conversational agents (chatbots) in different settings. Chatbots are systems that mimic human dialogue interaction through text or voice. This paper describes a series of design considerations for integrating chatbots interfaces with health services. The present paper is part of ongoing work that explores the overall implementation of chatbots in the healthcare context. The findings have been created using a research through design process, combining (1) literature survey of existing body of knowledge on designing chatbots, (2) analysis on state-of-the-practice in using chatbots as service interfaces, and (3) generative process of designing a chatbot interface for depression screening. In this paper we describe considerations that would be useful for the design of a chatbot for a healthcare context.
Тези доповідей конференцій з теми "Depression analysis chatbot":
1
Sharma, Bhuvan, Harshita Puri, and Deepika Rawat. "Digital Psychiatry - Curbing Depression using Therapy Chatbot and Depression Analysis." In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018. http://dx.doi.org/10.1109/icicct.2018.8472986.
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Mai, Hanwen, and Yu Sun. "ChatForSenior: An Intelligent ChatBot Communication System for Depression Relief using Artificial Intelligence and Natural Language Processing." In 3rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121221.
Анотація:
In recent years, loneliness has appeared in lives for both young and old individuals. As cases of the COVID-19 virus are going up people have dealt more with loneliness and depression especially the seniors [5]. Some have even changed their whole lifestyle because they feel empty and isolated. Others will either try to isolate themselves more or use dangerous ways to quickly get rid of the feeling.To solve this major problem, I have created a digital online communication app which young individuals can have long chats with seniors who are alone and lonely. My application uses real time communication systems which can directly be sent to other users without any issues [6]. Our main goal is to have users have their own way of communicating, using familiar designs of applications we all have used before. By using new features we have created a more user-friendly based user experience which can be experienced throughout our application. Using immersive layouts of applications designs, advanced network connections, visual and data based analytic we are able to solve this major problem.