Empowering diversity: striving for inclusivity by leveraging the American Medical Informatics Association's "For Your Informatics" Podcast.

Importance: Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community. Objectives: The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement. Materials and Methods: This descriptive case study is focused on the FYI Podcast team's processes to develop a revised mission, vision, and values, increase the diversity of guests, augment listenership through social media, and track the reach through the number of followers, downloads, and impressions. Results: As of December 2023, 35 FYI Podcast episodes are available with 685 social media followers, over 20 000 downloads, and nearly 145 000 impressions. In addition to introductions to informatics and loyal listeners within AMIA, the FYI Podcast episodes have been used by students as teaching material in a graduate biomedical informatics curriculum, and as introductory material for student clubs and programs. Discussion: The Podcast relaunching led to 98% of guests from underrepresented groups and growth in listenership by 329% since May 2021. Conclusion: The FYI Podcast supports AMIA's diversity mission, and gives voices to underrepresented groups, engages the clinical informatics community in critical conversations on justice, equity, diversity and inclusion, and supports education.

Harnessing Health Information Technology in Domestic Violence in the United States: A Scoping Review.

Objectives: The following scoping review aims to identify and map the existing evidence for HIT interventions among women with DV experiences in the United States. And provide guidance for future research, and facilitate clinical and technical applications for healthcare professionals. Methods: Five databases, PubMed, EBSCOhost CINAHL, Ovid APA PsycINFO, Scopus and Google Scholar, were searched from date of inception to May 2023. Reviewers extracted classification of the intervention, descriptive details, and intervention outcomes, including physical safety, psychological, and technical outcomes, based on representations in the included studies. Results: A total of 24 studies were included, identifying seven web-based interventions and four types of abuse. A total of five studies reported safety outcomes related to physical health. Three studies reported depression, anxiety, and post-traumatic stress disorder as psychological health outcomes. The effectiveness of technology interventions was assessed in eight studies. Conclusion: Domestic violence is a major public health issue, and research has demonstrated the tremendous potential of health information technology, the use of which can support individuals, families, and communities of domestic violence survivors.

Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study.

AIMS: To develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices. METHODS: We retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively. RESULTS: In the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885-0.976) and 0.906 (0.863-0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730-0.934) to 0.930 (0.906-0.953) and 0.891 (0.836-0.945) to 0.962 (0.918-1.000), respectively. CONCLUSIONS: Our models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow.

Predicting the Information Need for Domestic Violence Survivors Based on the Fine-Tuned Large Language Model.

Women with domestic violence experiences often refuse to seek help face-to-face due to embarrassment. They begin to share their emotions and seek help from online health communities. Understanding and responding to these posts can be crucial in providing timely support to the victims. We proposed a fine-tuned large language model (LLM) capable of accurately predicting the informational need based on the content of postings. We fine-tuned the LAMMA2-7B-chat model based on the guidance of identifying the information need and a dataset comprising 273 posts from Reddit, which are manually annotated by domain experts. Furthermore, we evaluated the performance of our model using a random sample of 15 posts, and 66.6% were accurately predicted. The results demonstrate that our model can rapidly capture the information needs expressed in the posts, enabling healthcare providers to provide timely and useful support based on our predictions.

HELPeR: Interface Design Decision and Evaluation.

Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

Utilizing Digital Twin to Create Personas Representing Ovarian Cancer Patients and Their Families.

OvCa patients and caregivers perceived challenges in online health information seeking. The HELPeR recommendation system utilized digital twins to create personas reflecting real-world OvCa patients and caregivers. The aim of this study was to describe the creation of digital twins and demonstrate their use cases in the study. Digital twins of OvCa patients and caregivers were created by triangulating multiple sources, including online cancer forums, direct interviews with patients and caregivers, domain expert input, and clinical notes. 10 personas were created for both OvCa patients and caregivers who had a variety of cancer trajectories and information interests. These digital twins present a potential solution for training artificial intelligence models at the initial phase when there is a scarcity of user information.

Towards Building an E-Librarian System: Exploring Recommendation System Preferences Among Ovarian Cancer Patients and Caregivers.

Ovarian cancer (OvCa) patients encounter complex treatment decisions, and often have difficulties in searching and integrating online health information to guide their treatment decision-making. The objective of this study was to explore the preference of online health information among OvCa patients and caregivers, by exploring their preferred content, format, and function features for the design of a personalized recommender system. This study used qualitative research methods to collect data through in-depth interviews with 18 OvCa patients and 2 caregivers. A total of (N=20) face-to-face interviews were conducted, and subsequently analyzed by audio recordings, verbatim transcription, and theory-driven approach with thematic analysis. A total of 5 themes were identified for content-related design, 4 themes identified for system function and one theme identified for frequency format. The results of this study inform the preference and therefore OvCa specific features can be tailor-made in a recommendation system.

The association between psychological distress, abusive experiences, and help-seeking among people with intimate partner violence.

BACKGROUND: Intimate partner violence (IPV) is a serious public health problem associated with countless adverse physical and mental health outcomes. It places an enormous economic and public health burden on communities. The aim of this study was to examine the associations between psychological states (such as depression or hopeless) and help-seeking experiences of IPV survivors after experiencing IPV, based on the Allegheny County Health Survey (ACHS). METHODS: Data from 2015 to 2016 Allegheny County Health Survey with N = 8,012 adults were analyzed. The 6-item version of the Kessler Psychological Stress Scale, located in Module 11 of the ACHS questionnaire, was used to measure psychological stress in participants. Module 12 of the ACHS questionnaire collected information on participants' experiences of intimate partner violence and help-seeking in the past 12 months. Descriptive statistical analysis, Pearson's chi-square or two sample independent t-tests statistical analysis, and multivariate binary logistic regression models were used to analyze the relationship between IPV experience and psychological distress. RESULTS: A total of 212 of the 8,012 participants had IPV experience, with age, marital status, education, income, and race significantly different from those without IPV experience. The psychological stress of participants feeling hopeless (OR = 2.02, 95% CI = 1.37-2.99), restless or fidgety (OR = 1.83, 95% CI = 1.27-2.65), perceiving everything was an effort (OR = 1.55, 95% CI = 1.08-2.22) and worthless (OR = 1.49, 95% CI = 1.01-2.20) was associated with the IPV experience. Help-seeking behaviors of IPV survivors were associated with psychological distress, such as hopelessness (OR = 6.71, 95% CI = 1.38-32.60). CONCLUSIONS: This study explored the association between IPV experience, help-seeking and psychological distress, and the need to expand community support. It is necessary to implement targeted interventions, enhance training of professionals, and promote the identification of early IPV cases as well as collaboration between healthcare and social support departments to reduce the occurrence of IPV or psychological distress following IPV.

Displacement of Submacular Hemorrhage Using Subretinal Cocktail Injection versus Pneumatic Displacement: A Real-World Comparative Study.

INTRODUCTION: The objective of this study was to compare the outcome of submacular hemorrhage (SMH) displacement using pneumatic displacement with intravitreal expansile gas versus pars plana vitrectomy (PPV) with subretinal injection of tissue plasminogen activator (tPA), anti-vascular endothelial growth factor (VEGF) agent, and air as primary surgery. METHODS: Retrospective interventional case series of 63 patients who underwent surgical displacement of SMH secondary to neovascular age-related macular degeneration (nAMD) or polypoidal choroidal vasculopathy (PCV) from May 1, 2015, to October 31, 2022. Medical records were reviewed for diagnosis, logMAR visual acuity (VA), central subfield thickness (CST), and postoperative displacement rates and complications up to 12 months after operation. RESULTS: The diagnosis was nAMD in 24 (38.1%) and PCV in 39 (61.9%) eyes. There were 40 (63.5%) eyes in the pneumatic displacement group (38 received C3F8, 2 received SF6) and 23 (36.5%) eyes in the subretinal cocktail injection. Mean baseline VA was 1.46 and 1.62, respectively (p = 0.404). The subretinal injection group had more extensive SMH (p = 0.005), thicker CST (1,006.6 µm vs. 780.2 µm, p = 0.012), and longer interval between symptom and operation (10.65 vs. 5.53 days, p < 0.001). The mean postoperative VA at 6 months was 0.67 and 0.91 (p = 0.180) for pneumatic displacement and subretinal injection groups, respectively, though VA was significantly better in the pneumatic group at 12-month visit (0.64 vs. 1.03, p = 0.040). At least 10 mean change in VA were >10 letters gain in both groups up to 12 months. Postoperative CST reduction was greater (625.1 µm vs. 326.5 µm, p = 0.008) and complete foveal displacement (87.0% vs. 37.5%), p < 0.001, odds ratio [OR] = 11.1) and displacement to arcade or beyond (52.5% vs. 17.5%, p = 0.009, OR = 5.15) were more frequent in the subretinal injection group. Two patients with failed pneumatic displacement were successfully treated with subretinal cocktail injection as a second operation. CONCLUSION: Surgical displacement of SMH leads to clinically meaningful improvement in VA. PPV with subretinal cocktail injection is more effective than pneumatic displacement in displacing SMH with similar safety profile despite longer interval before operation, higher CST, and more extensive SMH at baseline. Retinal surgeons could consider this novel technique in cases with thick and extensive SMH or as a rescue secondary operation in selected cases.

Women with ovarian cancer's information seeking and avoidance behaviors: an interview study.

Objectives: Despite the importance of using information for ovarian cancer (OvCa) disease management and decision-making, some women with OvCa do not actively seek out information. The purpose of this study is to investigate factors that influence information seeking behaviors and information avoidance behaviors and information resources among women with OvCa and their caregivers. Materials and methods: We conducted in-depth interviews with OvCa patients or caregivers of OvCa (n = 20) and employed deductive and inductive coding methodologies for analysis. Results: Our analysis revealed 5 emerging themes associated with active information seeking behavior, 5 themes of passive information acquisition, and 4 themes of information avoidance behavior. Additionally, we identified participants' preferred information sources for OvCa management, such as health organization or government operated resources and web-based social groups. Discussion: To enhance information access, strategies should be developed to motivate people with OvCa to seek rather than avoid information. The study emphasizes the significance of promoting patient-provider communication and leveraging strong social support networks for effective information acquisition. Conclusion: Our findings provide valuable implications for clinical practice and policymaking, emphasizing the need to improve access to information for individuals with OvCa. By addressing the identified factors influencing information seeking behaviors, healthcare professionals and policymakers can better support patients and caregivers in their information-seeking journey, ultimately enhancing disease management and decision-making outcomes.

Predictors of First-Time NCLEX-RN Pass Rate in Accelerated Bachelor of Science in Nursing Students.

BACKGROUND: An accelerated Bachelor of Science in Nursing (ABSN) program was introduced in an effort to reduce the nursing shortage in response to the growing health demands. Nursing educators strive to examine factors that promote students' success in passing the National Council Licensure Examination for Registered Nurses (NCLEX-RN) on their first attempt. However, little is known about which factors predict the NCLEX-RN first-time pass rate of ABSN students. METHOD: This descriptive retrospective study examined academic and nonacademic indicators (e.g., age) that can predict ABSN students' NCLEX-RN first-time pass rates between 2008 and 2019. RESULTS: A total of 591 ABSN students were included in the study. Both GRE (Graduate Record Examination) verbal reasoning scores (p < .001) and cumulative grade point averages (p < .001) were significant predictors. CONCLUSION: The findings of this study can help inform ABSN programs in terms of admission criteria and curriculum consideration for including standardized tests to improve ABSN students' NCLEX-RN first-time pass rate. [J Nurs Educ. 2024;63(1):10-16.].

Effectiveness of support from community health workers on the sustained use of a wearable monitoring device among community-dwelling older adults: A randomized trial protocol.

BACKGROUND: Wearable monitoring devices, such as smartwatches and fitness bands, are health technologies for enhancing self-care management among community-dwelling older adults. While the evidence suggests that these devices can promote health, older adults often struggle to use them over the long term. Community health workers can effectively motivate older adults to change their health behaviors. This study proposes an intervention involving community health workers as peer supporters to promote sustained daily use of wearable monitoring devices among community-dwelling older adults. METHODS: The intervention group in this randomized controlled trial will receive the Live with Wearable Monitoring Device program from trained community health workers with the support of a nurse and social workers through a one-time home visit and regular phone calls. The control group will receive only the wearable monitoring device. Data will be collected at baseline, 1 month, 3 months, and 6 months. DISCUSSION: Merely providing older adults with wearable monitoring devices may not lead to the realization of the potential health benefits of these devices, as long-term usage can be challenging. The results of this trial can provide evidence for a new approach to enhancing self-management and community healthcare among community-dwelling older adults, ultimately improving their health outcomes. IMPACT: Wearable monitoring devices not only enable real-time monitoring of vital signs, but can also support tailored messaging and facilitate virtual communication between users and healthcare professionals. Despite considerable health benefits, there is evidence showing that older adults largely stop using them after a few months. This study is the first to use a peer support approach to help older adults incorporate a wearable monitoring device in their daily routines in conjunction with goal setting and regular reminders. This will boost the self-care ability of the older adults, allowing them to continue physically functioning in the community. TRIAL REGISTRATION: This study was prospectively registered at clinicaltrials.gov (identifier: NCT05269303). Registration date: 24/2/2022.

Evaluating the effectiveness of video-game based swallowing function training in patients with dysphagia: study protocol for a randomized controlled trial.

BACKGROUND: Dysphagia can lead to serious complications such as aspiration and aspiration pneumonia, timely and effective rehabilitation training can improve the swallowing function of patients. However, the conventional rehabilitation training methods used in clinical settings have shortcomings such as poor adherence of patients. We present the study design of a randomized controlled trial that evaluated whether video-game based swallowing rehabilitation training can effectively improve swallowing in patients with dysphagia and whether it has additional benefits compared with conventional training methods to improve swallowing function and training compliance among patients with dysphagia. METHODS: A randomized controlled trial with 4 weeks of intervention and 4 weeks of follow-up will be conducted in a rehabilitation center in Beijing, China. We will enroll 78 patients aged 18-80 years with dysphagia. Participants will be randomly assigned to the experimental group (video-game based swallowing function training) and the control group (conventional swallowing function training). All participants will receive 30 min of training per day, 5 times per week, for a total of 4 weeks. The primary outcome is swallowing function. Secondary outcomes include patients' quality of life, training compliance, and training satisfaction. Outcomes are assessed at baseline (pre-treatment), 4 weeks of treatment (post-treatment), and 8 weeks (follow-up), and the assessor is not aware of the participants' grouping. DISCUSSION: The protocol describes a new rehabilitation training method for dysphagia, which involves participant eligibility recruitment, recruitment strategies, and data analysis plan. The results of the study will inform the rehabilitation training and clinical care management of swallowing function in patients with dysphagia. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05978700. Registered on 28 July 2023.

Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.

BACKGROUND: Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. OBJECTIVE: The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. METHODS: We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ≥18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. RESULTS: Overall, 250 postings were included, and 200 (80%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. CONCLUSIONS: OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively.

Effect of a telecare-based intervention on stress levels in informal caregivers of older adults: protocol for a randomized controlled trial.

Significance: Due to caregiving commitments, caregivers of older adults may not have the time to make use of the onsite community services available to them during the day. With the support of advanced technology, telecare could be a convenient and easily accessible channel for providing individualized caregiving advice to caregivers. Objective: The aim of the study is to describe a research protocol that highlights the development of a telecare-based intervention program for reducing stress levels in informal caregivers of community-dwelling older adults. Methods: It is a randomized controlled trial. The study is supported by two community centers. The study participants will be randomly assigned to either the telecare-based intervention group or the control group. The former will receive a 3-month program comprised of three components: online nurse case management supported by a health and social care team, an online resource center, and a discussion forum. The latter will receive the usual services that provided by the community centers. Data will be collected at two time points - pre-intervention (T1) and post-intervention (T2). The primary outcome is stress levels, while secondary outcomes include self-efficacy, depression levels, quality of life, and caregiving burden. Discussion: Besides taking care of one or more older adults, informal caregivers have to deal with work, chores, and take care of their children. This study will add valuable information to the knowledge gap on whether telecare-based interventions with the support of an integrated health-social team can alleviate the stress levels of informal caregivers of community-dwelling older adults. If successful, policymakers and healthcare professionals should consider incorporating telecare modalities in a primary health setting for informal caregivers to correspond with them, to relieve their caregiving stress and promote a healthy life. Clinical trial registration: https://www.clinicaltrials.gov/, NCT05636982.

Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models.

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time, the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

Assessment of Parafoveal Diabetic Macular Ischemia on Optical Coherence Tomography Angiography Images to Predict Diabetic Retinal Disease Progression and Visual Acuity Deterioration.

Importance: The presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management. Objective: To investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes. Design, Setting, and Participants: In this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022. Main Outcomes and Measures: DR progression, DME development, and VA deterioration. Results: A total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus-DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus-DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus-DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell-inner plexiform layer thickness, axial length, and smoking at baseline. Conclusions and Relevance: In this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.

Harnessing Machine Learning in Tackling Domestic Violence-An Integrative Review.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. METHODS: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. RESULTS: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. CONCLUSIONS: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.