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Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. J Affect Disord 2024; 361:445-456. [PMID: 38889858 DOI: 10.1016/j.jad.2024.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/27/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions. METHODS This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type. RESULTS The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management. LIMITATIONS This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population. CONCLUSIONS To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
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Affiliation(s)
- Yoonseo Park
- Department of Convergence Healthcare Medicine, Ajou University, Suwon, South Korea.
| | - Sewon Park
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
| | - Munjae Lee
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
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Yuan Y, Kasson E, Taylor J, Cavazos-Rehg P, De Choudhury M, Aledavood T. Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach. JMIR Form Res 2024; 8:e54433. [PMID: 38713904 PMCID: PMC11109860 DOI: 10.2196/54433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/15/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. OBJECTIVE By leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals' social media posts that could indicate escalating or de-escalating patterns in substance use. METHODS We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. RESULTS Our results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. CONCLUSIONS This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions.
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Affiliation(s)
- Yunhao Yuan
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Erin Kasson
- School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Jordan Taylor
- Carnegie Mellon University, Pittsburgh, PA, United States
| | - Patricia Cavazos-Rehg
- School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
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Tudehope L, Harris N, Vorage L, Sofija E. What methods are used to examine representation of mental ill-health on social media? A systematic review. BMC Psychol 2024; 12:105. [PMID: 38424653 PMCID: PMC10905888 DOI: 10.1186/s40359-024-01603-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/18/2024] [Indexed: 03/02/2024] Open
Abstract
There has been an increasing number of papers which explore the representation of mental health on social media using various social media platforms and methodologies. It is timely to review methodologies employed in this growing body of research in order to understand their strengths and weaknesses. This systematic literature review provides a comprehensive overview and evaluation of the methods used to investigate the representation of mental ill-health on social media, shedding light on the current state of this field. Seven databases were searched with keywords related to social media, mental health, and aspects of representation (e.g., trivialisation or stigma). Of the 36 studies which met inclusion criteria, the most frequently selected social media platforms for data collection were Twitter (n = 22, 61.1%), Sina Weibo (n = 5, 13.9%) and YouTube (n = 4, 11.1%). The vast majority of studies analysed social media data using manual content analysis (n = 24, 66.7%), with limited studies employing more contemporary data analysis techniques, such as machine learning (n = 5, 13.9%). Few studies analysed visual data (n = 7, 19.4%). To enable a more complete understanding of mental ill-health representation on social media, further research is needed focussing on popular and influential image and video-based platforms, moving beyond text-based data like Twitter. Future research in this field should also employ a combination of both manual and computer-assisted approaches for analysis.
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Affiliation(s)
- Lucy Tudehope
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia.
| | - Neil Harris
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia
| | - Lieke Vorage
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia
| | - Ernesta Sofija
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, 1 Parklands Drive, 4222, Southport, Gold Coast, QLD, Australia
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Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. NPJ MENTAL HEALTH RESEARCH 2023; 2:20. [PMID: 38609509 PMCID: PMC10955993 DOI: 10.1038/s44184-023-00040-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/27/2023] [Indexed: 04/14/2024]
Abstract
Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.
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Affiliation(s)
- Kaining Mao
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Yuqi Wu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Jie Chen
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
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Pan W, Wang X, Zhou W, Hang B, Guo L. Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2688. [PMID: 36768053 PMCID: PMC9915029 DOI: 10.3390/ijerph20032688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/18/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Depression is one of the most common mental illnesses but remains underdiagnosed. Suicide, as a core symptom of depression, urgently needs to be monitored at an early stage, i.e., the suicidal ideation (SI) stage. Depression and subsequent suicidal ideation should be supervised on social media. In this research, we investigated depression and concomitant suicidal ideation by identifying individuals' linguistic characteristics through machine learning approaches. On Weibo, we sampled 487,251 posts from 3196 users from the depression super topic community (DSTC) as the depression group and 357,939 posts from 5167 active users on Weibo as the control group. The results of the logistic regression model showed that the SCLIWC (simplified Chinese version of LIWC) features such as affection, positive emotion, negative emotion, sadness, health, and death significantly predicted depression (Nagelkerke's R2 = 0.64). For model performance: F-measure = 0.78, area under the curve (AUC) = 0.82. The independent samples' t-test showed that SI was significantly different between the depression (0.28 ± 0.5) and control groups (-0.29 ± 0.72) (t = 24.71, p < 0.001). The results of the linear regression model showed that the SCLIWC features, such as social, family, affection, positive emotion, negative emotion, sadness, health, work, achieve, and death, significantly predicted suicidal ideation. The adjusted R2 was 0.42. For model performance, the correlation between the actual SI and predicted SI on the test set was significant (r = 0.65, p < 0.001). The topic modeling results were in accordance with the machine learning results. This study systematically investigated depression and subsequent SI-related linguistic characteristics based on a large-scale Weibo dataset. The findings suggest that analyzing the linguistic characteristics on online depression communities serves as an efficient approach to identify depression and subsequent suicidal ideation, assisting further prevention and intervention.
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Affiliation(s)
- Wei Pan
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
- School of Psychology, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
| | - Xianbin Wang
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
- School of Psychology, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
| | - Wenwei Zhou
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
- School of Psychology, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
| | - Bowen Hang
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
- School of Psychology, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
| | - Liwen Guo
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
- School of Psychology, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
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Lyu S, Ren X, Du Y, Zhao N. Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Front Psychiatry 2023; 14:1121583. [PMID: 36846219 PMCID: PMC9947407 DOI: 10.3389/fpsyt.2023.1121583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Abstract
INTRODUCTION In recent years, research has used psycholinguistic features in public discourse, networking behaviors on social media and profile information to train models for depression detection. However, the most widely adopted approach for the extraction of psycholinguistic features is to use the Linguistic Inquiry Word Count (LIWC) dictionary and various affective lexicons. Other features related to cultural factors and suicide risk have not been explored. Moreover, the use of social networking behavioral features and profile features would limit the generalizability of the model. Therefore, our study aimed at building a prediction model of depression for text-only social media data through a wider range of possible linguistic features related to depression, and illuminate the relationship between linguistic expression and depression. METHODS We collected 789 users' depression scores as well as their past posts on Weibo, and extracted a total of 117 lexical features via Simplified Chinese Linguistic Inquiry Word Count, Chinese Suicide Dictionary, Chinese Version of Moral Foundations Dictionary, Chinese Version of Moral Motivation Dictionary, and Chinese Individualism/Collectivism Dictionary. RESULTS Results showed that all the dictionaries contributed to the prediction. The best performing model occurred with linear regression, with the Pearson correlation coefficient between predicted values and self-reported values was 0.33, the R-squared was 0.10, and the split-half reliability was 0.75. DISCUSSION This study did not only develop a predictive model applicable to text-only social media data, but also demonstrated the importance taking cultural psychological factors and suicide related expressions into consideration in the calculation of word frequency. Our research provided a more comprehensive understanding of how lexicons related to cultural psychology and suicide risk were associated with depression, and could contribute to the recognition of depression.
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Affiliation(s)
- Sihua Lyu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Ren
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yihua Du
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
| | - Nan Zhao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022:1-22. [PMID: 36467993 PMCID: PMC9684805 DOI: 10.1007/s11571-022-09904-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/26/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
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Affiliation(s)
- Prabal Datta Barua
- School of Management and Enterprise, University of Southern Queensland, Springfield, Australia
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Elizabeth Emma Palmer
- Discipline of Pediatric and Child Health, School of Clinical Medicine, University of New South Wales, Kensington, Australia
- Sydney Children’s Hospitals Network, Sydney, Australia
| | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taizhong, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Comput Sci 2022; 8:e1070. [PMID: 36092010 PMCID: PMC9455273 DOI: 10.7717/peerj-cs.1070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.
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Affiliation(s)
- Nadiah A. Baghdadi
- Nursing Management and Education Department, College of Nursing, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Yousry AbdulAzeem
- Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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Kesler SR, Henneghan AM, Thurman W, Rao V. Identifying themes for assessing cancer-related cognitive impairment identified by topic modeling and qualitative content analysis of public online comments (Preprint). JMIR Cancer 2021; 8:e34828. [PMID: 35612878 PMCID: PMC9178450 DOI: 10.2196/34828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 04/28/2022] [Accepted: 05/01/2022] [Indexed: 11/28/2022] Open
Abstract
Background Cancer-related cognitive impairment (CRCI) is a common and significant adverse effect of cancer and its therapies. However, its definition and assessment remain difficult due to limitations of currently available measurement tools. Objective This study aims to evaluate qualitative themes related to the cognitive effects of cancer to help guide development of assessments that are more specific than what is currently available. Methods We applied topic modeling and inductive qualitative content analysis to 145 public online comments related to cognitive effects of cancer. Results Topic modeling revealed 2 latent topics that we interpreted as representing internal and external factors related to cognitive effects. These findings lead us to hypothesize regarding the potential contribution of locus of control to CRCI. Content analysis suggested several major themes including symptoms, emotional/psychological impacts, coping, “chemobrain” is real, change over time, and function. There was some conceptual overlap between the 2 methods regarding internal and external factors related to patient experiences of cognitive effects. Conclusions Our findings indicate that coping mechanisms and locus of control may be important themes to include in assessments of CRCI. Future directions in this field include prospective acquisition of free-text responses to guide development of assessments that are more sensitive and specific to cognitive function in patients with cancer.
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Affiliation(s)
- Shelli R Kesler
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Ashley M Henneghan
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Whitney Thurman
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Vikram Rao
- School of Nursing, University of Texas at Austin, Austin, TX, United States
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