1
|
Patel D, Sumner SA, Bowen D, Zwald M, Yard E, Wang J, Law R, Holland K, Nguyen T, Mower G, Chen Y, Johnson JI, Jespersen M, Mytty E, Lee JM, Bauer M, Caine E, De Choudhury M. Predicting state level suicide fatalities in the united states with realtime data and machine learning. NPJ MENTAL HEALTH RESEARCH 2024; 3:3. [PMID: 38609512 PMCID: PMC10956008 DOI: 10.1038/s44184-023-00045-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/20/2023] [Indexed: 04/14/2024]
Abstract
Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.
Collapse
Affiliation(s)
- Devashru Patel
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Steven A Sumner
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Daniel Bowen
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Marissa Zwald
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Ellen Yard
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jing Wang
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Royal Law
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Kristin Holland
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Gary Mower
- Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - Yushiuan Chen
- Tri-County Health Department, Greenwood Village, CO, USA
| | | | | | | | | | - Michael Bauer
- New York State Department of Health, Albany, NY, USA
| | - Eric Caine
- University of Rochester Medical Center, Rochester, NY, USA
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA.
| |
Collapse
|
2
|
Alemi F, Carmack S, Gustafson D, Jacobson J, Kreps GL, Nambisan P, Remezani N, Simons J, Xiao Y. Support for the Kids Online Safety Act (KOSA), With Caution. Qual Manag Health Care 2023; 32:278-280. [PMID: 37348081 DOI: 10.1097/qmh.0000000000000424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
|
3
|
Harris D, Krishnan A. Exploring the Association Between Suicide Prevention Public Service Announcements and User Comments on YouTube: A Computational Text Analysis Approach. JOURNAL OF HEALTH COMMUNICATION 2023; 28:302-311. [PMID: 37070172 DOI: 10.1080/10810730.2023.2203077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the United States, suicide rates have increased by 30% over the past few decades. Public service announcements (PSAs) are effective health promotion vehicles and social media can help spread PSAs to hard-to-engage individuals who may benefit from intervention efforts, yet the most meaningful characteristics of PSAs for influencing health promotion attitudes and behaviors are inconclusive. This study applied content and quantitative text analyses to suicide prevention PSAs and comments on YouTube to assess the relationships between message frame, message format, and the level of sentiment and help-seeking language within them. Seventy-two PSAs were analyzed for gain/loss-framing and narrative/argument-format, and 4,335 related comments were analyzed for positive/negative sentiment and frequency of help-seeking language use. Results indicate that a higher ratio of positive comments was more likely to be found on gain-framed and narrative-formatted PSAs, and a higher ratio of comments with help-seeking language was more likely to be found on narrative-formatted PSAs. Implications and future research are discussed.
Collapse
Affiliation(s)
- Donald Harris
- Information Science Department, College of Emergency Preparedness, Homeland Security and Cybersecurity, University at Albany, Albany, New York, USA
| | - Archana Krishnan
- College of Arts & Sciences, Department of Communication, University at Albany, Albany, New York, USA
| |
Collapse
|
4
|
Singh T, Roberts K, Cohen T, Cobb N, Franklin A, Myneni S. Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework. J Biomed Inform 2023; 140:104324. [PMID: 36842490 PMCID: PMC10206862 DOI: 10.1016/j.jbi.2023.104324] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 02/28/2023]
Abstract
BACKGROUND Online health communities (OHCs) have emerged as prominent platforms for behavior modification, and the digitization of online peer interactions has afforded researchers with unique opportunities to model multilevel mechanisms that drive behavior change. Existing studies, however, have been limited by a lack of methods that allow the capture of conversational context and socio-behavioral dynamics at scale, as manifested in these digital platforms. OBJECTIVE We develop, evaluate, and apply a novel methodological framework, Pragmatics to Reveal Intent in Social Media (PRISM), to facilitate granular characterization of peer interactions by combining multidimensional facets of human communication. METHODS We developed and applied PRISM to analyze peer interactions (N = 2.23 million) in QuitNet, an OHC for tobacco cessation. First, we generated a labeled set of peer interactions (n = 2,005) through manual annotation along three dimensions: communication themes (CTs), behavior change techniques (BCTs), and speech acts (SAs). Second, we used deep learning models to apply our qualitative codes at scale. Third, we applied our validated model to perform a retrospective analysis. Finally, using social network analysis (SNA), we portrayed large-scale patterns and relationships among the aforementioned communication dimensions embedded in peer interactions in QuitNet. RESULTS Qualitative analysis showed that the themes of social support and behavioral progress were common. The most used BCTs were feedback and monitoring and comparison of behavior, and users most commonly expressed their intentions using SAs-expressive and emotion. With additional in-domain pre-training, bidirectional encoder representations from Transformers (BERT) outperformed other deep learning models on the classification tasks. Content-specific SNA revealed that users' engagement or abstinence status is associated with the prevalence of various categories of BCTs and SAs, which also was evident from the visualization of network structures. CONCLUSIONS Our study describes the interplay of multilevel characteristics of online communication and their association with individual health behaviors.
Collapse
Affiliation(s)
- Tavleen Singh
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA.
| | - Kirk Roberts
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Trevor Cohen
- Biomedical Informatics and Medical Education, The University of Washington, Seattle, WA, USA
| | - Nathan Cobb
- Georgetown University Medical Center, Washington, DC, USA
| | - Amy Franklin
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Sahiti Myneni
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| |
Collapse
|
5
|
Jung W, Kim D, Nam S, Zhu Y. Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning. Arch Suicide Res 2023; 27:13-28. [PMID: 34319221 DOI: 10.1080/13811118.2021.1955783] [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] [Indexed: 10/20/2022]
Abstract
In this study, we implemented machine learning models that can detect suicidality posts on Twitter. We randomly selected and annotated 20,000 tweets and explored metadata and text features to build effective models. Metadata features were studied in great details to understand their possibility and importance in suicidality detection models. Results showed that posting type (i.e., reply or not) and time-related features such as the month, day of the week, and the time (AM vs. PM) were the most important metadata features in suicidality detection models. Specifically, the probability of a social media post being suicidal is higher if the post is a reply to other users rather than an original tweet. Moreover, tweets created in the afternoon, on Fridays and weekends, and in fall have higher probabilities of being detected as suicidality tweets compared with those created in other times. By integrating metadata and text features, we obtained a model of good performance (i.e., F1 score of 0.846) that can assist humans in the real-world setting to detect suicidality social media posts.
Collapse
|
6
|
Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders. J Affect Disord 2022; 317:107-113. [PMID: 36029873 DOI: 10.1016/j.jad.2022.08.054] [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: 05/27/2022] [Revised: 08/05/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Suicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare their effectiveness. METHODS A total of 13,100 patients with mood disorders were selected. Two traditional text mining methods, logistic regression and Support vector machine (SVM), and one deep learning model (Convolutional neural network, CNN) were adopted to perform overall analysis and gender-specific subgroup analysis of patients to identify suicide attempts. The classification effectiveness of these models was evaluated by accuracy, F1-value, precision, recall, and the area under Receiver operator characteristic curve (ROC). RESULTS CNN's results were greater than the other two for all indicators except recall which was slightly smaller than SVM in male subgroup analysis. The accuracy values of the CNN were 98.4 %, 98.2 %, and 98.5 % in the overall analysis and the subgroup analysis for males and females, respectively. The results of McNemar's test showed that CNN and SVM models' predictions were statistically different from the logistic regression model's predictions in the overall analysis and the subgroup analysis for females (P < 0.050). LIMITATIONS A fixed number of features were selected based on document frequency to train models; this was a single-site study. CONCLUSIONS CNN model was a better way to detect suicide attempts in patients with mood disorders prior to hospital admission, saving time and resources in recognizing high-risk patients and preventing suicide.
Collapse
|
7
|
Khan NZ, Javed MA. Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review. Cureus 2022; 14:e27225. [PMID: 36035036 PMCID: PMC9400551 DOI: 10.7759/cureus.27225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2022] [Indexed: 11/12/2022] Open
Abstract
Mental illness leading to suicide attempts is prevalent in a large portion of the population especially in low and middle-income nations. There remains a significant social stigma associated with mental illness that can lead to stigmatization of patients. Hence, patients are reluctant to communicate their problems to health care providers. Physicians have difficulty in timely identification of patients at risk for suicide. Novel and rigorously designed strategies are needed to determine the population at risk for suicide. This would be the first step in overcoming the multitude of barriers in the management of mental illness. Clinical tools and the use of electronic medical records (EMR) are time intensive. Recently, several artificial intelligence (AI)-based predictive technologies have gained momentum. The aim of this review is to summarize the recent advances in this landscape.
Collapse
|
8
|
Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation. Healthcare (Basel) 2022; 10:healthcare10040698. [PMID: 35455874 PMCID: PMC9029735 DOI: 10.3390/healthcare10040698] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/15/2022] [Accepted: 04/05/2022] [Indexed: 12/01/2022] Open
Abstract
People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.
Collapse
|
9
|
Ebbehoj A, Thunbo MØ, Andersen OE, Glindtvad MV, Hulman A. Transfer learning for non-image data in clinical research: A scoping review. PLOS DIGITAL HEALTH 2022; 1:e0000014. [PMID: 36812540 PMCID: PMC9931256 DOI: 10.1371/journal.pdig.0000014] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/15/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. METHODS AND FINDINGS We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). CONCLUSIONS In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.
Collapse
Affiliation(s)
- Andreas Ebbehoj
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Aarhus University, Denmark
| | | | | | | | - Adam Hulman
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Denmark
- * E-mail:
| |
Collapse
|
10
|
Yang L, Li S, Luo X, Xu B, Geng Y, Zeng Z, Zhang F, Lin H. Computational personality: a survey. Soft comput 2022. [DOI: 10.1007/s00500-022-06786-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
11
|
Deep Learning-based Detection of Psychiatric Attributes from German Mental Health Records. Int J Med Inform 2022; 161:104724. [DOI: 10.1016/j.ijmedinf.2022.104724] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/07/2022] [Accepted: 02/18/2022] [Indexed: 11/24/2022]
|
12
|
Application of named entity recognition on tweets during earthquake disaster: a deep learning-based approach. Soft comput 2022. [DOI: 10.1007/s00500-021-06370-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
13
|
Kim D, Jung W, Nam S, Jeon H, Baek J, Zhu Y. Understanding information behavior of South Korean Twitter users who express suicidality on Twitter. Digit Health 2022; 8:20552076221086339. [PMID: 35340901 PMCID: PMC8943454 DOI: 10.1177/20552076221086339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 02/16/2022] [Indexed: 11/25/2022] Open
Abstract
Objective Although there were few studies on how suicidal users behave on Twitter, they only investigated partial aspects such as tweeting frequency and tweet length. Therefore, we aim to understand the various information behavior of suicidal users in South Korea. Methods To achieve this goal, we annotated 20,000 tweets and identified 1097 tweets with the expression of suicidality (i.e. suicidal tweets) and 229 suicidal users (i.e. experimental group). Using the data, a user profile analysis, comparative analysis with control group, and tweets/hashtags analysis were performed. Results Our results show that many suicidal users used suicide-related keywords in their user IDs, usernames, descriptions, and pinned tweets. We also found that, compared to the control group, the experimental group show different patterns of information behavior. The experimental group did not frequently use Twitter and, on average, wrote longer texts than the control group. A clear seasonal pattern was also identified in the experimental group's tweeting behavior. Frequently used keywords/hashtags were extracted from tweets written by the experimental group for the purpose of understanding their concerns and detecting more suicidal tweets. Conclusions We believe that our study will help in the understanding of suicidal users' information behavior on social media and lay the basis for more accurate actions for suicide prevention and early intervention on social media.
Collapse
Affiliation(s)
- Donghun Kim
- Department of Library and Information Science, Sungkyunkwan University, Seoul, Republic of Korea
| | - Woojin Jung
- Department of Library and Information Science, Sungkyunkwan University, Seoul, Republic of Korea
| | - Seojin Nam
- Department of Library and Information Science, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hongjin Jeon
- Department of Psychiatry, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
- Depression Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jihyun Baek
- Department of Psychiatry, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yongjun Zhu
- Department of Library and Information Science, Yonsei University,
Seoul, Republic of Korea
| |
Collapse
|
14
|
Mishra S, Tripathy HK, Kumar Thakkar H, Garg D, Kotecha K, Pandya S. An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment. Front Public Health 2021; 9:795007. [PMID: 34976936 PMCID: PMC8718454 DOI: 10.3389/fpubh.2021.795007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/10/2021] [Indexed: 11/20/2022] Open
Abstract
Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.
Collapse
Affiliation(s)
- Sushruta Mishra
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | - Hrudaya Kumar Tripathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | | | - Deepak Garg
- Department of Computer Science and Engineering, School of Engineering and Sciences, Bennett University, Greater Noida, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbosis International (Deemed) University, Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbosis International (Deemed) University, Pune, India
| |
Collapse
|
15
|
Li J, Ma W, Zhang M, Wang P, Liu Y, Ma S. Know Yourself: Physical and Psychological Self-Awareness With Lifelog. Front Digit Health 2021; 3:676824. [PMID: 34713147 PMCID: PMC8521907 DOI: 10.3389/fdgth.2021.676824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/09/2021] [Indexed: 11/13/2022] Open
Abstract
Self-awareness is an essential concept in physiology and psychology. Accurate overall self-awareness benefits the development and well being of an individual. The previous research studies on self-awareness mainly collect and analyze data in the laboratory environment through questionnaires, user study, or field research study. However, these methods are usually not real-time and unavailable for daily life applications. Therefore, we propose a new direction of utilizing lifelog for self-awareness. Lifelog records about daily activities are used for analysis, prediction, and intervention on individual physical and psychological status, which can be automatically processed in real-time. With the help of lifelog, ordinary people are able to understand their condition more precisely, get effective personal advice about health, and even discover physical and mental abnormalities at an early stage. As the first step on using lifelog for self-awareness, we learn from the traditional machine learning problems, and summarize a schema on data collection, feature extraction, label tagging, and model learning in the lifelog scenario. The schema provides a flexible and privacy-protected method for lifelog applications. Following the schema, four topics were conducted: sleep quality prediction, personality detection, mood detection and prediction, and depression detection. Experiments on real datasets show encouraging results on these topics, revealing the significant relation between daily activity records and physical and psychological self-awareness. In the end, we discuss the experiment results and limitations in detail and propose an application, Lifelog Recorder, for multi-dimensional self-awareness lifelog data collection.
Collapse
Affiliation(s)
- Jiayu Li
- Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Weizhi Ma
- Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Min Zhang
- Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Pengyu Wang
- Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yiqun Liu
- Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Shaoping Ma
- Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| |
Collapse
|
16
|
Safa R, Bayat P, Moghtader L. Automatic detection of depression symptoms in twitter using multimodal analysis. THE JOURNAL OF SUPERCOMPUTING 2021; 78:4709-4744. [PMID: 34518741 PMCID: PMC8426595 DOI: 10.1007/s11227-021-04040-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/19/2021] [Indexed: 05/03/2023]
Abstract
Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user's psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information.
Collapse
Affiliation(s)
- Ramin Safa
- Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
| | - Peyman Bayat
- Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
| | - Leila Moghtader
- Department of Psychology, Rasht Branch, Islamic Azad University, Rasht, Iran
| |
Collapse
|
17
|
Du J, Xiang Y, Sankaranarayanapillai M, Zhang M, Wang J, Si Y, Pham HA, Xu H, Chen Y, Tao C. Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning. J Am Med Inform Assoc 2021; 28:1393-1400. [PMID: 33647938 DOI: 10.1093/jamia/ocab014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 01/14/2021] [Accepted: 01/20/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract nervous system disorder-related events from vaccine safety reports. MATERIALS AND METHODS We collected Guillain-Barré syndrome (GBS) related influenza vaccine safety reports from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016. VAERS reports were selected and manually annotated with major entities related to nervous system disorders, including, investigation, nervous_AE, other_AE, procedure, social_circumstance, and temporal_expression. A variety of conventional machine learning and deep learning algorithms were then evaluated for the extraction of the above entities. We further pretrained domain-specific BERT (Bidirectional Encoder Representations from Transformers) using VAERS reports (VAERS BERT) and compared its performance with existing models. RESULTS AND CONCLUSIONS Ninety-one VAERS reports were annotated, resulting in 2512 entities. The corpus was made publicly available to promote community efforts on vaccine AEs identification. Deep learning-based methods (eg, bi-long short-term memory and BERT models) outperformed conventional machine learning-based methods (ie, conditional random fields with extensive features). The BioBERT large model achieved the highest exact match F-1 scores on nervous_AE, procedure, social_circumstance, and temporal_expression; while VAERS BERT large models achieved the highest exact match F-1 scores on investigation and other_AE. An ensemble of these 2 models achieved the highest exact match microaveraged F-1 score at 0.6802 and the second highest lenient match microaveraged F-1 score at 0.8078 among peer models.
Collapse
Affiliation(s)
- Jingcheng Du
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yang Xiang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | | | - Meng Zhang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jingqi Wang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yuqi Si
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Huy Anh Pham
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yong Chen
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Cui Tao
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| |
Collapse
|
18
|
Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Ment Health 2021; 8:e24668. [PMID: 34110297 PMCID: PMC8262551 DOI: 10.2196/24668] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 03/11/2021] [Accepted: 04/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation. OBJECTIVE This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised. METHODS We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis. RESULTS We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance. CONCLUSIONS Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.
Collapse
Affiliation(s)
- Piers Gooding
- Melbourne Law School, University of Melbourne, Melbourne, Australia
- Mozilla Foundation, Mountain View, CA, United States
| | - Timothy Kariotis
- Melbourne School of Government, University of Melbourne, Melbourne, Australia
| |
Collapse
|
19
|
Rassy J, Bardon C, Dargis L, Côté LP, Corthésy-Blondin L, Mörch CM, Labelle R. Information and Communication Technology Use in Suicide Prevention: Scoping Review. J Med Internet Res 2021; 23:e25288. [PMID: 33820754 PMCID: PMC8132980 DOI: 10.2196/25288] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/10/2021] [Accepted: 03/16/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The use of information and communication technology (ICT) in suicide prevention has progressed rapidly over the past decade. ICT plays a major role in suicide prevention, but research on best and promising practices has been slow. OBJECTIVE This paper aims to explore the existing literature on ICT use in suicide prevention to answer the following question: what are the best and most promising ICT practices for suicide prevention? METHODS A scoping search was conducted using the following databases: PubMed, PsycINFO, Sociological Abstracts, and IEEE Xplore. These databases were searched for articles published between January 1, 2013, and December 31, 2018. The five stages of the scoping review process were as follows: identifying research questions; targeting relevant studies; selecting studies; charting data; and collating, summarizing, and reporting the results. The World Health Organization suicide prevention model was used according to the continuum of universal, selective, and indicated prevention. RESULTS Of the 3848 studies identified, 115 (2.99%) were selected. Of these, 10 regarded the use of ICT in universal suicide prevention, 53 referred to the use of ICT in selective suicide prevention, and 52 dealt with the use of ICT in indicated suicide prevention. CONCLUSIONS The use of ICT plays a major role in suicide prevention, and many promising programs were identified through this scoping review. However, large-scale evaluation studies are needed to further examine the effectiveness of these programs and strategies. In addition, safety and ethics protocols for ICT-based interventions are recommended.
Collapse
Affiliation(s)
- Jessica Rassy
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
- Research Center, Institut universitaire en santé mentale de Montréal, Montréal, QC, Canada
- School of Nursing, Université de Sherbrooke, Longueuil, QC, Canada
- Quebec Network on Nursing Intervention Research, Montréal, QC, Canada
| | - Cécile Bardon
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
| | - Luc Dargis
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
| | - Louis-Philippe Côté
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
| | - Laurent Corthésy-Blondin
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
| | - Carl-Maria Mörch
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
- Algora Lab, Université de Montréal, Montréal, QC, Canada
- Mila, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Réal Labelle
- Center for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices, Université du Québec à Montréal, Montréal, QC, Canada
- Research Center, Institut universitaire en santé mentale de Montréal, Montréal, QC, Canada
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
- Department of Psychiatry, Université de Montréal, Montréal, QC, Canada
| |
Collapse
|
20
|
Côté D, Williams M, Zaheer R, Niederkrotenthaler T, Schaffer A, Sinyor M. Suicide-related Twitter Content in Response to a National Mental Health Awareness Campaign and the Association between the Campaign and Suicide Rates in Ontario. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2021; 66:460-467. [PMID: 33563028 PMCID: PMC8107951 DOI: 10.1177/0706743720982428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Mental health awareness (MHA) campaigns have been shown to be successful in improving mental health literacy, decreasing stigma, and generating public discussion. However, there is a dearth of evidence regarding the effects of these campaigns on behavioral outcomes such as suicides. Therefore, the objective of this article is to characterize the association between the event and suicide in Canada's most populous province and the content of suicide-related tweets referencing a Canadian MHA campaign (Bell Let's Talk Day [BLTD]). METHODS Suicide counts during the week of BTLD were compared to a control window (2011 to 2016) to test for associations between the BLTD event and suicide. Suicide tweets geolocated to Ontario, posted in 2016 with the BLTD hashtag were coded for specific putatively harmful and protective content. RESULTS There was no associated change in suicide counts. Tweets (n = 3,763) mainly included content related to general comments about suicide death (68%) and suicide being a problem (42.8%) with little putatively helpful content such as stories of resilience (0.6%) and messages of hope (2.2%). CONCLUSIONS In Ontario, this national mental health media campaign was associated with a high volume of suicide-related tweets but not necessarily including content expected to diminish suicide rates. Campaigns like BLTD should strongly consider greater attention to suicide-related messaging that promotes help-seeking and resilience. This may help to further decrease stigmatization, and potentially, reduce suicide rates.
Collapse
Affiliation(s)
- David Côté
- Department of Psychiatry, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,University of Toronto, Ontario, Canada
| | - Marissa Williams
- Department of Psychiatry, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Athabasca University, Alberta, Canada
| | - Rabia Zaheer
- Department of Psychiatry, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,University of Waterloo, Ontario, Canada
| | - Thomas Niederkrotenthaler
- Center for Public Health, Department of Social and Preventive Medicine, Medical University of Vienna, Unit Suicide Research & Mental Health Promotion, Vienna, Austria
| | - Ayal Schaffer
- Department of Psychiatry, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Ontario Canada
| | - Mark Sinyor
- Department of Psychiatry, 71545Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Ontario Canada
| |
Collapse
|
21
|
Kim J, Lee D, Park E. Machine Learning for Mental Health in Social Media: Bibliometric Study. J Med Internet Res 2021; 23:e24870. [PMID: 33683209 PMCID: PMC7985801 DOI: 10.2196/24870] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 02/17/2021] [Indexed: 12/11/2022] Open
Abstract
Background Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. Objective We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. Methods Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. Results We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. Conclusions The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.
Collapse
Affiliation(s)
- Jina Kim
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
| | - Daeun Lee
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
| | - Eunil Park
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, Republic of Korea
| |
Collapse
|
22
|
Fowler JC, Madan A, Bruce CR, Frueh BC, Kash B, Jones SL, Sasangohar F. Improving Psychiatric Care Through Integrated Digital Technologies. J Psychiatr Pract 2021; 27:92-100. [PMID: 33656814 DOI: 10.1097/pra.0000000000000535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This manuscript provides an overview of our efforts to implement an integrated electronic monitoring and feedback platform to increase patient engagement, improve care delivery and outcome of treatment, and alert care teams to deterioration in functioning. Patients First utilizes CareSense, a digital care navigation and data collection system, to integrate traditional patient-reported outcomes monitoring with novel biological monitoring between visits to provide patients and caregivers with real-time feedback on changes in symptoms such as stress, anxiety, and depression. The next stage of project development incorporates digital therapeutics (computerized therapeutic interventions) for patients, and video resources for primary care physicians and nurse practitioners who serve as the de facto front line for psychiatric care. Integration of the patient-reported outcomes monitoring with continuous biological monitoring, and digital supports is a novel application of existing technologies. Video resources pushed to care providers whose patients trigger a symptom severity alert is, to our knowledge, an industry first.
Collapse
|
23
|
|
24
|
Sinyor M, Williams M, Zaheer R, Loureiro R, Pirkis J, Heisel MJ, Schaffer A, Redelmeier DA, Cheung AH, Niederkrotenthaler T. The association between Twitter content and suicide. Aust N Z J Psychiatry 2021; 55:268-276. [PMID: 33153274 DOI: 10.1177/0004867420969805] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE A growing body of research has established that specific elements of suicide-related news reporting can be associated with increased or decreased subsequent suicide rates. This has not been systematically investigated for social media. The aim of this study was to identify associations between specific social media content and suicide deaths. METHODS Suicide-related tweets (n = 787) geolocated to Toronto, Canada and originating from the highest level influencers over a 1-year period (July 2015 to June 2016) were coded for general, putatively harmful and putatively protective content. Multivariable logistic regression was used to examine whether tweet characteristics were associated with increases or decreases in suicide deaths in Toronto in the 7 days after posting, compared with a 7-day control window. RESULTS Elements independently associated with increased subsequent suicide counts were tweets about the suicide of a local newspaper reporter (OR = 5.27, 95% CI = [1.27, 21.99]), 'other' social causes of suicide (e.g. cultural, relational, legal problems; OR = 2.39, 95% CI = [1.17, 4.86]), advocacy efforts (OR = 2.34, 95% CI = [1.48, 3.70]) and suicide death (OR = 1.52, 95% CI = [1.07, 2.15]). Elements most strongly independently associated with decreased subsequent suicides were tweets about murder suicides (OR = 0.02, 95% CI = [0.002, 0.17]) and suicide in first responders (OR = 0.17, 95% CI = [0.05, 0.52]). CONCLUSIONS These findings largely comport with the theory of suicide contagion and associations observed with traditional news media. They specifically suggest that tweets describing suicide deaths and/or sensationalized news stories may be harmful while those that present suicide as undesirable, tragic and/or preventable may be helpful. These results suggest that social media is both an important exposure and potential avenue for intervention.
Collapse
Affiliation(s)
- Mark Sinyor
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Marissa Williams
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Graduate Centre for Applied Psychology, Athabasca University, Athabasca, AB, Canada
| | - Rabia Zaheer
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Faculty of Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Raisa Loureiro
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jane Pirkis
- Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Marnin J Heisel
- Departments of Psychiatry and of Epidemiology and Biostatistics, University of Western Ontario, London, ON, Canada
| | - Ayal Schaffer
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Donald A Redelmeier
- Department of Medicine, University of Toronto, Toronto, ON, Canada.,Sunnybrook Research Institute, Toronto, ON, Canada.,Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Institute for Clinical Evaluative Sciences, Toronto, ON, Canada
| | - Amy H Cheung
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Thomas Niederkrotenthaler
- Unit Suicide Research & Mental Health Promotion, Department of Social and Preventive Medicine, Center for Public Health, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
25
|
Ibrahim MA, Ghani Khan MU, Mehmood F, Asim MN, Mahmood W. GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification. J Biomed Inform 2021; 116:103699. [PMID: 33601013 DOI: 10.1016/j.jbi.2021.103699] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 11/30/2020] [Accepted: 02/02/2021] [Indexed: 01/16/2023]
Abstract
Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.
Collapse
Affiliation(s)
- Muhammad Ali Ibrahim
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
| | - Muhammad Usman Ghani Khan
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; Department of Computer Science, University of Engineering and Technology (UET), Lahore, Pakistan
| | - Faiza Mehmood
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan
| | - Muhammad Nabeel Asim
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan; German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
| | - Waqar Mahmood
- Intelligent Criminology Research Lab, National Center of Artificial Intelligence, Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan
| |
Collapse
|
26
|
Jacobucci R, Ammerman BA, Tyler Wilcox K. The use of text-based responses to improve our understanding and prediction of suicide risk. Suicide Life Threat Behav 2021; 51:55-64. [PMID: 33624877 DOI: 10.1111/sltb.12668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Text-based responses may provide significant contributions to suicide risk prediction, yet research including text data is limited. This may be due to a lack of exposure and familiarity with statistical analyses for this data structure. METHOD The current study provides an overview of data processing and statistical algorithms for text data, guided by an empirical example of 947 online participants who completed both open-ended items and traditional self-report measures. We give an introduction to a number of text-based statistical approaches, including dictionary-based methods, topic modeling, word embeddings, and deep learning. RESULTS We analyze responses from the open-ended question "How do you feel today?", detailing characteristics of the responses, as well as predicting past-year suicidal ideation. CONCLUSIONS We see the analysis of text from social media, open-ended questions, and other text sources (i.e., medical records) as an important form of complementary assessment to traditional scales, shedding insight on what we are missing in our current set of questionnaires, which may ultimately serve to improve both our understanding and prediction of suicide.
Collapse
Affiliation(s)
- Ross Jacobucci
- Department of Psychology, University of Notre Dame, Notre Dame, Indiana
| | - Brooke A Ammerman
- Department of Psychology, University of Notre Dame, Notre Dame, Indiana
| | | |
Collapse
|
27
|
Tassone J, Yan P, Simpson M, Mendhe C, Mago V, Choudhury S. Utilizing deep learning and graph mining to identify drug use on Twitter data. BMC Med Inform Decis Mak 2020; 20:304. [PMID: 33380324 PMCID: PMC7772918 DOI: 10.1186/s12911-020-01335-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 11/16/2020] [Indexed: 11/26/2022] Open
Abstract
Background The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. Methods Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. Results To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC’s of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. Conclusion Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased accuracy scores and improves the predictive capability.
Collapse
Affiliation(s)
- Joseph Tassone
- Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada
| | - Peizhi Yan
- Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada
| | - Mackenzie Simpson
- Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada
| | - Chetan Mendhe
- Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada
| | - Vijay Mago
- Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada.
| | - Salimur Choudhury
- Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada
| |
Collapse
|
28
|
Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. J Med Syst 2020; 44:205. [PMID: 33165729 PMCID: PMC7649702 DOI: 10.1007/s10916-020-01669-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/25/2020] [Indexed: 12/16/2022]
Abstract
According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.
Collapse
Affiliation(s)
- Gema Castillo-Sánchez
- Department of Signal Theory and Communications, and Telematics Engineering, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Gonçalo Marques
- Department of Signal Theory and Communications, and Telematics Engineering, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
| | - Enrique Dorronzoro
- Electronic Technology Department, Universidad de Sevilla, Sevilla, Spain
| | | | | | - Isabel De la Torre-Díez
- Department of Signal Theory and Communications, and Telematics Engineering, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| |
Collapse
|
29
|
Bickman L. Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2020; 47:795-843. [PMID: 32715427 PMCID: PMC7382706 DOI: 10.1007/s10488-020-01065-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.
Collapse
Affiliation(s)
- Leonard Bickman
- Center for Children and Families; Psychology, Academic Health Center 1, Florida International University, 11200 Southwest 8th Street, Room 140, Miami, FL, 33199, USA.
| |
Collapse
|
30
|
Obeid JS, Dahne J, Christensen S, Howard S, Crawford T, Frey LJ, Stecker T, Bunnell BE. Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach. JMIR Med Inform 2020; 8:e17784. [PMID: 32729840 PMCID: PMC7426805 DOI: 10.2196/17784] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/25/2020] [Accepted: 05/21/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Suicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum. OBJECTIVE This study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events. METHODS We extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words-based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance. RESULTS The area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance. CONCLUSIONS The strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.
Collapse
Affiliation(s)
- Jihad S Obeid
- Medical University of South Carolina, Charleston, SC, United States
| | - Jennifer Dahne
- Medical University of South Carolina, Charleston, SC, United States
| | - Sean Christensen
- Medical University of South Carolina, Charleston, SC, United States
| | - Samuel Howard
- Medical University of South Carolina, Charleston, SC, United States
| | - Tami Crawford
- Medical University of South Carolina, Charleston, SC, United States
| | - Lewis J Frey
- Medical University of South Carolina, Charleston, SC, United States
| | - Tracy Stecker
- Medical University of South Carolina, Charleston, SC, United States
| | | |
Collapse
|
31
|
Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med 2020; 3:78. [PMID: 32509975 PMCID: PMC7250902 DOI: 10.1038/s41746-020-0287-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 04/28/2020] [Indexed: 12/31/2022] Open
Abstract
Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10-71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.
Collapse
Affiliation(s)
- Arunima Roy
- The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada
| | - Katerina Nikolitch
- The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada
| | - Rachel McGinn
- The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada
| | - Safiya Jinah
- The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada
| | - William Klement
- Division of Thoracic Surgery, The Ottawa Research Hospital Research Institute and Ottawa University, Ottawa, ON Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS Canada
| | - Zachary A. Kaminsky
- The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON Canada
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| |
Collapse
|
32
|
Chen Q, Du J, Kim S, Wilbur WJ, Lu Z. Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records. BMC Med Inform Decis Mak 2020; 20:73. [PMID: 32349758 PMCID: PMC7191680 DOI: 10.1186/s12911-020-1044-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP2018 organizers have made the first attempt to annotate 1068 sentence pairs from clinical notes and have called for a community effort to tackle the Semantic Textual Similarity (BioCreative/OHNLP STS) challenge. Methods We developed models using traditional machine learning and deep learning approaches. For the post challenge, we focused on two models: the Random Forest and the Encoder Network. We applied sentence embeddings pre-trained on PubMed abstracts and MIMIC-III clinical notes and updated the Random Forest and the Encoder Network accordingly. Results The official results demonstrated our best submission was the ensemble of eight models. It achieved a Person correlation coefficient of 0.8328 – the highest performance among 13 submissions from 4 teams. For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~ 13%. During the challenge task, no end-to-end deep learning models had better performance than machine learning models that take manually-crafted features. In contrast, with the sentence embeddings pre-trained on biomedical corpora, the Encoder Network now achieves a correlation of ~ 0.84, which is higher than the original best model. The ensembled model taking the improved versions of the Random Forest and Encoder Network as inputs further increased performance to 0.8528. Conclusions Deep learning models with sentence embeddings pre-trained on biomedical corpora achieve the highest performance on the test set. Through error analysis, we find that end-to-end deep learning models and traditional machine learning models with manually-crafted features complement each other by finding different types of sentences. We suggest a combination of these models can better find similar sentences in practice.
Collapse
Affiliation(s)
- Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Jingcheng Du
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA.,School of Biomedical Informatics, UTHealth, Houston, USA
| | - Sun Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - W John Wilbur
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA.
| |
Collapse
|
33
|
Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 2020; 10:116. [PMID: 32532967 PMCID: PMC7293215 DOI: 10.1038/s41398-020-0780-3] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 02/17/2020] [Accepted: 02/26/2020] [Indexed: 12/17/2022] Open
Abstract
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
Collapse
Affiliation(s)
- Chang Su
- grid.5386.8000000041936877XDepartment of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY USA
| | - Zhenxing Xu
- grid.5386.8000000041936877XDepartment of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY USA
| | - Jyotishman Pathak
- grid.5386.8000000041936877XDepartment of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY USA
| | - Fei Wang
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA.
| |
Collapse
|
34
|
|
35
|
Burr C, Morley J, Taddeo M, Floridi L. Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing. ACTA ACUST UNITED AC 2020. [DOI: 10.1109/tts.2020.2977059] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
36
|
Priya A, Garg S, Tigga NP. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.procs.2020.03.442] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
37
|
Abstract
Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.
Collapse
|
38
|
Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. J Am Med Inform Assoc 2019; 26:1279-1285. [PMID: 31233120 PMCID: PMC7647240 DOI: 10.1093/jamia/ocz085] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 04/21/2019] [Accepted: 05/08/2019] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts. MATERIALS AND METHODS ML-Net combines a label prediction network with an automated label count prediction mechanism to provide an optimal set of labels. This is accomplished by leveraging both the predicted confidence score of each label and the deep contextual information (modeled by ELMo) in the target document. We evaluate ML-Net on 3 independent corpora in 2 text genres: biomedical literature and clinical notes. For evaluation, we use example-based measures, such as precision, recall, and the F measure. We also compare ML-Net with several competitive machine learning and deep learning baseline models. RESULTS Our benchmarking results show that ML-Net compares favorably to state-of-the-art methods in multi-label classification of biomedical text. ML-Net is also shown to be robust when evaluated on different text genres in biomedicine. CONCLUSION ML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.
Collapse
Affiliation(s)
- Jingcheng Du
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
- The University of Texas School of Biomedical Informatics, Houston, Texas, USA
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Yifan Peng
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Yang Xiang
- The University of Texas School of Biomedical Informatics, Houston, Texas, USA
| | - Cui Tao
- The University of Texas School of Biomedical Informatics, Houston, Texas, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA
| |
Collapse
|
39
|
Fonseka TM, Bhat V, Kennedy SH. The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Aust N Z J Psychiatry 2019; 53:954-964. [PMID: 31347389 DOI: 10.1177/0004867419864428] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within 'big data' to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. METHODS This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. RESULTS At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. CONCLUSION Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.
Collapse
Affiliation(s)
- Trehani M Fonseka
- Centre for Mental Health and Krembil Research Centre, University Health Network, Toronto, ON, Canada.,Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada.,School of Social Work, King's University College, Western University, London, ON, Canada
| | - Venkat Bhat
- Centre for Mental Health and Krembil Research Centre, University Health Network, Toronto, ON, Canada.,Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Centre for Mental Health and Krembil Research Centre, University Health Network, Toronto, ON, Canada.,Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| |
Collapse
|
40
|
Wu JL, Xiao X, Yu LC, Ye SZ, Lai KR. Using an analogical reasoning framework to infer language patterns for negative life events. BMC Med Inform Decis Mak 2019; 19:173. [PMID: 31455389 PMCID: PMC6712629 DOI: 10.1186/s12911-019-0895-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Accepted: 08/14/2019] [Indexed: 11/15/2022] Open
Abstract
Background Feelings of depression can be caused by negative life events (NLE) such as the death of a family member, a quarrel with one’s spouse, job loss, or strong criticism from an authority figure. The automatic and accurate identification of negative life event language patterns (NLE-LP) can help identify individuals potentially in need of psychiatric services. An NLE-LP combines a person (subject) and a reasonable negative life event (action), e.g. <parent:divorce> or < boyfriend:break_up>. Methods This paper proposes an analogical reasoning framework which combines a word representation approach and a pattern inference method to mine/extract NLE-LPs from psychiatric consultation documents. Word representation approaches such as skip-gram (SG) and continuous bag-of-words (CBOW) are used to generate word embeddings. Pattern inference methods such as cosine similarity (COSINE) and cosine multiplication similarity (COSMUL) are used to infer patterns. Results Experimental results show our proposed analogical reasoning framework outperforms the traditional methods such as positive pairwise mutual information (PPMI) and hyperspace analog to language (HAL), and can effectively mine highly precise NLE-LPs based on word embeddings. CBOW with COSINE of analogical reasoning is the best word representation and inference engine. In addition, both word embeddings and the inference engine provided by the analogical reasoning framework can further be used to improve the HAL model. Conclusions Our proposed framework is a very simple matching function based on these word representation approaches and is applied to significantly improve HAL model mining performance.
Collapse
Affiliation(s)
- Jheng-Long Wu
- School of Big Data Management, Soochow University, Taipei City, Taiwan
| | - Xiang Xiao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan.,Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan City, Taiwan.,College of Mathematics and Computer Science, FuZhou University, FuZhou City, China
| | - Liang-Chih Yu
- Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan City, Taiwan. .,Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan.
| | - Shao-Zhen Ye
- College of Mathematics and Computer Science, FuZhou University, FuZhou City, China
| | - K Robert Lai
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan.,Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan
| |
Collapse
|
41
|
Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou MH. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLoS One 2019; 14:e0219550. [PMID: 31295294 PMCID: PMC6622529 DOI: 10.1371/journal.pone.0219550] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 06/26/2019] [Indexed: 12/03/2022] Open
Abstract
Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010-2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.
Collapse
Affiliation(s)
- Joseph Gibbons
- Department of Sociology, San Diego State University, San Diego, California, United States of America
| | - Robert Malouf
- Department of Linguistics and Asian/Middle Eastern Languages, San Diego State University, San Diego, California, United States of America
| | - Brian Spitzberg
- School of Communication, San Diego State University, San Diego, California, United States of America
| | - Lourdes Martinez
- School of Communication, San Diego State University, San Diego, California, United States of America
| | - Bruce Appleyard
- School of Public Affairs and Fine Arts, San Diego State University, San Diego, California, United States of America
| | - Caroline Thompson
- School of Public Health, San Diego State University, San Diego, California, United States of America
| | - Atsushi Nara
- Department of Geography, San Diego State University, San Diego, California, United States of America
| | - Ming-Hsiang Tsou
- Department of Geography, San Diego State University, San Diego, California, United States of America
| |
Collapse
|
42
|
Allen NB, Nelson BW, Brent D, Auerbach RP. Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough? J Affect Disord 2019; 250:163-169. [PMID: 30856493 PMCID: PMC6481940 DOI: 10.1016/j.jad.2019.03.044] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 03/04/2019] [Accepted: 03/05/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Suicide is one of the leading causes of death among adolescents, and developing effective methods to improve short-term prediction of suicidal thoughts and behaviors (STBs) is critical. Currently, the most robust predictors of STBs are demographic or clinical indicators that have relatively weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has identified a number of promising candidates, including (but not limited to) rapid escalation of: (a) emotional distress, (b) social dysfunction (e.g., bullying, rejection), and (c) sleep disturbance. However, these prior studies are limited in two critical ways. First, they rely almost entirely on self-report. Second, most studies have not focused on assessment of these risk factors using intensive longitudinal assessment techniques that are able to capture the dynamics of changes in risk states at the individual level. METHOD In this paper we explore how to capitalize on recent developments in real-time monitoring methods and computational analysis in order to address these fundamental problems. RESULTS We now have the capacity to use: (a) smartphone, wearable computing, and smart home technology to conduct intensive longitudinal assessments monitoring of putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. Current research and theory on short-term risk processes for STBs, combined with the emergent capabilities of new technologies, suggest that this is an important research agenda for the future. LIMITATIONS Although these approaches have enormous potential to create new knowledge, the current empirical literature is limited. Moreover, passive monitoring of risk for STBs raises complex ethical issues that will need to be resolved before large scale clinical applications are feasible. CONCLUSIONS Smartphone, wearable, and smart home technology may provide one point of access that might facilitate both early identification and intervention implementation, and thus, represents a key area for future STB research.
Collapse
Affiliation(s)
- Nicholas B Allen
- Department of Psychology, University of Oregon, Eugene, Oregon, United States; Center for Digital Mental Health, University of Oregon, Eugene, Oregon, United States.
| | - Benjamin W Nelson
- Department of Psychology, University of Oregon, Eugene, Oregon, United States; Center for Digital Mental Health, University of Oregon, Eugene, Oregon, United States
| | - David Brent
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Randy P Auerbach
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York City, United States; Division of Clinical Developmental Neuroscience, Sackler Institute, New York City, United States
| |
Collapse
|
43
|
Du J, Cunningham RM, Xiang Y, Li F, Jia Y, Boom JA, Myneni S, Bian J, Luo C, Chen Y, Tao C. Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media. NPJ Digit Med 2019; 2:27. [PMID: 31304374 PMCID: PMC6550201 DOI: 10.1038/s41746-019-0102-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 03/26/2019] [Indexed: 11/09/2022] Open
Abstract
Our aim was to characterize health beliefs about the human papillomavirus (HPV) vaccine in a large set of Twitter posts (tweets). We collected a Twitter data set related to the HPV vaccine from 1 January 2014, to 31 December 2017. We proposed a deep-learning-based framework to mine health beliefs on the HPV vaccine from Twitter. Deep learning achieved high performance in terms of sensitivity, specificity, and accuracy. A retrospective analysis of health beliefs found that HPV vaccine beliefs may be evolving on Twitter.
Collapse
Affiliation(s)
- Jingcheng Du
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | | | - Yang Xiang
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Fang Li
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Yuxi Jia
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA.,3School of Public Health, Jilin University, Changchun, China
| | - Julie A Boom
- 2Texas Children's Hospital, Houston, TX USA.,4Baylor College of Medicine, Houston, TX USA
| | - Sahiti Myneni
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Jiang Bian
- 5Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL USA
| | - Chongliang Luo
- 6Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA
| | - Yong Chen
- 6Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA.,7Institute for Biomedical Informatics, The University of Pennsylvania, Philadelphia, PA USA.,8Center for Evidence-based Practice, The University of Pennsylvania, Philadelphia, PA USA
| | - Cui Tao
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| |
Collapse
|
44
|
Luo J, Du J, Tao C, Xu H, Zhang Y. Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics. Health Informatics J 2019; 26:738-752. [PMID: 30866708 DOI: 10.1177/1460458219832043] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
A valid mechanism for suicide detection and intervention to a wider population online has not yet been fully established. With the increasing suicide rate, we proposed an approach that aims to examine temporal patterns of potential suicidal ideations and behaviors on Twitter to better understand their risk factors and time-varying features. It identifies latent suicide topics and then models the suicidal topic-related score time series to quantitatively represent behavior patterns on Twitter. After evaluated on a collection of suicide-related tweets in 2016, 13 key risk factors were discovered and the temporal patterns of suicide behavior on different days during 1 week were identified to highlight the distinct time-varying features related to different risk factors. This study is practical to help public health services and others to develop refined prevention strategies, to monitor and support a population of high-risk at right moments.
Collapse
Affiliation(s)
| | | | | | | | - Yaoyun Zhang
- The University of Texas School of Biomedical Informatics, USA
| |
Collapse
|
45
|
He Z, Tao C, Bian J, Zhang R, Huang J. Introduction: selected extended articles from the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017). BMC Med Inform Decis Mak 2018; 18:56. [PMID: 30066636 PMCID: PMC6069756 DOI: 10.1186/s12911-018-0624-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In this editorial, we first summarize the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017) held on November 13, 2017 in Kansas City, Missouri, U.S.A., and then briefly introduce 13 research articles included in this supplement issue, covering topics such as Semantic Integration, Deep Learning, Knowledge Base Construction, and Natural Language Processing.
Collapse
Affiliation(s)
- Zhe He
- School of Information, Florida State University, 142 Collegiate Loop, Tallahassee, 32306, FL, USA.
| | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Rui Zhang
- Institute for Health Informatics and College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Jingshan Huang
- School of Computing, University of South Alabama, Mobile, AL, USA
| |
Collapse
|