Online Health Community Members Research Articles

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

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. 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. 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. 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. 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.

Knowledge Acquisition and Social Support in Online Health Communities: Analysis of an Online Ovarian Cancer Community.

BackgroundPatients and caregivers widely use online health communities (OHCs) to acquire knowledge from peers. Questions posed in OHCs reflect participants’ learning objectives and differ in their level of cognitive complexity. However, little is known about the topics and levels of participants’ learning objectives and the corresponding support they receive from members of OHCs.ObjectiveThis study aimed to investigate the knowledge acquisition of patients and caregivers in an OHC. Specifically, we investigated the distribution and topics of posts with learning objectives at different cognitive complexity levels, the type and amount of social support provided to meet users’ learning objectives at different cognitive complexity levels, and the influence of social support on the change in learning objectives.MethodsWe collected 10 years of discussion threads from one of the most active ovarian cancer (OvCa) OHCs. A mixed methods approach was used, including qualitative content analysis and quantitative statistical analysis. Initial posts with questions were manually classified into 1 of the 3 learning objectives with increasing cognitive complexity levels, from low to high, based on the Anderson and Krathwohl taxonomy: understand, analyze, and evaluate. Manual content analysis and automatic classification models were used to identify the types of social support in the comments, including emotional support and 5 types of informational support: advice, referral, act, personal experience, and opinion.ResultsThe original data set contained 909 initial posts and 14,816 comments, and the final data set for the analysis contained 560 posts with questions and 3998 comments. Our results showed that patients with OvCa and their caregivers mainly used OHCs to acquire knowledge for low- to medium-level learning objectives. Of the questions, 82.3% (461/560) were either understand- or analyze-level questions, in which users were seeking to learn basic facts and medical concepts or draw connections among different situations and conditions. Only 17.7% (99/560) of the questions were at the evaluate level, in which users asked other OHC members to help them make decisions or judgments. Notably, OvCa treatment was the most popular topic of interest among all the questions, regardless of the level of learning objectives. Regarding the social support received for different levels of learning objectives, significant differences were found in the advice (F2437.84=9.69; P<.001), opinion (F2418.18=11.56; P<.001), and emotional support (F2395.88=3.24; P=.01), as determined by one-way ANOVA, whereby questions at the evaluate level were more likely to receive advice, opinion, and emotional support than questions at the lower levels. Additionally, receiving social support tends to drive users to increase the cognitive complexity of the learning objective in the next post.ConclusionsOur study establishes that OHCs are promising resources for acquiring knowledge of OvCa. Our findings have implications for designing better OHCs that serve the growing OvCa community.

Effect of privacy concerns and engagement on social support behaviour in online health community platforms

The growth of interactive technologies has fostered different online health communities (OHCs) where individuals share similar interests in health-related information and exchange social support to facilitate health outcomes. While OHCs offer a variety of benefits to society, it is challenged by surrounding issues of privacy concerns. Breach of privacy poses undesirable consequences for people, and thus privacy concerns can influence individuals' social support behaviour in OHC platforms. Moreover, willingness to engage in the community can be an outcome of prosocial behaviour, motivating people to offer additional social support on OHC platforms. Hence, addressing the role of engagement in a multi-actor online environment requires further attention. Drawing on social support theory, by examining the effects of privacy concerns, control of information, and community engagement, this study develops a framework to create an informed and sharing online community. Using survey data collected from different OHC platforms on Facebook, our study presents some interesting conclusions. Our results show that community engagement and privacy concerns can influence certain types of social support (i.e., information or emotional support), leading to OHC members' intention to participate. Our conceptual model and findings will inform both future research and policymakers.

The role of social support in obesity online health communities. A literature review

In the treatment of obesity, social support is considered key to successful weight loss and behavioural change, and online health communities to complement obesity treatment programmes have been shown to increase obesity treatment effectiveness. This study reviews the latest literature regarding how social support provided through an online health community can help people tackle obesity, identifying the different effects of social support on online health community members, specifically, the promotion of behavioural change and increased self-efficacy. This study also reveals how the recent literature points to both a direct and indirect relationship between social support in online health communities for obesity treatment and actual weight loss. This review is likely to provide useful insights to both healthcare professionals and social platform developers.

Come for Information, Stay for Support: Harnessing the Power of Online Health Communities for Social Connectedness during the COVID-19 Pandemic.

The COVID-19 pandemic created a globally shared stressor that saw a rise in the emphasis on mental and emotional wellbeing. However, historically, these topics were not openly discussed, leaving those struggling without professional support. One powerful tool to bridge the gap and facilitate connectedness during times of isolation is online health communities (OHCs). This study surveyed Health Union OHC members during the pandemic to determine the degree of COVID-19 concern, social isolation, and mental health distress they are facing, as well as to assess where they are receiving information about COVID-19 and what sources of support they desire. The survey was completed in six independent waves between March 2020 and April 2021, and garnered 10,177 total responses. In the United States, OHCs were utilized significantly more during peak lockdown times, and the desire for emotional and/or mental health support increased over time. Open-ended responses demonstrated a strong desire for connection and validation, which are quintessential characteristics of OHCs. Through active moderation utilizing trained moderators, OHCs can provide a powerful, intermediate and safe space where conversations about mental and emotional wellbeing can be normalized and those in need are encouraged to seek additional assistance from healthcare professionals if warranted.

Attraction, selection, and attrition in online health communities: Initial conversations and their association with subsequent activity levels

BackgroundThe effectiveness of online health communities (OHCs) for improving outcomes for health care consumers, health professionals, and health services has already been well investigated. However, research on determinants of OHC users’ activity levels, what is associated with attrition or attraction to these communities, and the impacts of initial posts is limited. ObjectivesWe sought to explore topic exchanges in OHCs and determine how users’ initial posts and community reactions to them are associated with their subsequent activity levels. We also aimed to extend the theory of Attraction-Selection-Attrition for Online Communities (OCASA) to this area. MethodsWe examined exchanges in a major Australian OHC for cancer patients, analyzing about 2500 messages posted over 2009–18. We developed a novel annotation scheme to examine new members’ initial posts and the community’s reactions to them. ResultsThe annotation scheme includes five themes: informational support provision, emotional support provision, requests for help, self-reflection & disclosures, and conversational cues. Initial conversations were associated with future activity levels in terms of active posting versus non-active engagement in the community. We found that most OHC members disclosed personal reflections to bond with the community, and many actively posted to the community solely to provide informational and emotional support to others. ConclusionOur work extends OCASA theory to bond-based contexts, presents a new annotation scheme for OHC support topics, and makes an important contribution to knowledge about the relationship between users’ activity levels and their initial posts. The findings help managers and owners understand how members use OHCs and how to encourage active participation. They also suggest how to attract new members and minimize attrition among existing members.

Social support, belongingness, and value co-creation behaviors in online health communities

Community members become part of the value chain, and their value co-creation behaviors have progressively elicited extensive attention from scholars and practitioners. However, the mechanisms on how members co-create value in online health communities (OHCs) have been poorly investigated. Therefore, this study develops a model of how belongingness and social support affect the four value co-creation behaviors (i.e., information sharing, responsible, feedback, and advocacy behaviors) in OHCs based on need-to-belong theory and social support literature. Our survey data from 237 OHC members empirically show that informational, emotional, esteem, and companionship supports positively influence members’ belongingness to OHCs, which in turn affects information sharing, responsible, feedback, and advocacy behaviors. Members’ belongingness to OHCs mediates the relationships between the four forms of social support and value co-creation behavior. In addition, OHC members’ perceived health competence positively moderates the influence of belongingness on information-sharing behavior, whereas their mental health negatively moderates the relationships of belongingness with all four value co-creation behaviors. Therefore, promoting value co-creation relies on the improvement of member belongingness, but it should also consider the health competence and status of members.

Understanding the satisfaction and continuance intention of knowledge contribution by health professionals in online health communities

ABSTRACTParticipation of health-care professionals in online health communities is essential for these communities to achieve their goals of improving health outcomes. However, little research has been conducted to understand what motivates health-care providers to participate in such communities. This study utilizes the expectancy/value theory to identify the factors that might affect health professionals’ intentions to continue contributing health knowledge in online health communities (OHCs). Specific motivators driving health professionals’ continuance intentions in OHCs include intrinsic motivators (helping motivator, self-efficacy, and moral obligation), and extrinsic motivators (reputation). This study also investigates how health professionals’ satisfaction in the OHC mediates the relationship between the study motivators and the continuance intentions. The study also suggests that health professional characteristics have a moderating effect on continuance intentions. The study employed a sample of 234 health professionals who contribute to a well-regarded OHC in Jordan to test the theoretical model developed for this study. The findings suggest that helping motivator, self-efficacy motivator, and reputation motivator affect OHC members’ satisfaction. The helping motivator, reputation motivator, moral obligation motivator, and satisfaction affect OHC members’ contribution continuance intentions. Finally, the study finds that age, work experience, experience in the OHC, and the daily time spent in the OHC, moderate the relationship between the helping motivator and continuance intentions.

A critical perspective of engagement in online health communities

ABSTRACTMany individuals go to online health communities to obtain emotional and information support. This study employs critical discourse analysis (CDA) to examine the discourse in five online childhood immunization forums to identify online practices related to engagement and emancipation. We identify four online engagement discourse types in online health communities: cognitive, emotional, behavioural, and political. Consistent with our critical perspective, we identify drivers and obstacles to emancipation. The obstacles to emancipation include the adverse consequences of building online knowledge and collective intelligence, shifts in patient–physician relationships, and “Big” institutional power. Members’ suggestions for empowering community members include encouraging members to conduct their own research on childhood immunization and to differentiate between facts and opinions. These findings suggest that sensemaking is one way to emancipate online health community members, while highlighting the challenges of using online communities to promote emancipation.

Research on Evolutionary Game of Information Sharing with Online Health Community

Online health community has become an important way for patients to share information and has received more and more attention. Based on the theory of evolutionary game theory, this paper establishes a game model of information sharing of online health community members, and studies the factors that affect the information sharing among patients. Results show that the information sharing behavior of the online health community can evolve into a good state and can also be “locked” in a bad state. Online health community managers need to improve the information sharing revenue coefficient, reduce information sharing risk cost, and promote the sustainable information sharing of online community patients.

Longitudinal Changes in Psychological States in Online Health Community Members: Understanding the Long-Term Effects of Participating in an Online Depression Community.

BackgroundMajor depression is a serious challenge at both the individual and population levels. Although online health communities have shown the potential to reduce the symptoms of depression, emotional contagion theory suggests that negative emotion can spread within a community, and prolonged interactions with other depressed individuals has potential to worsen the symptoms of depression.ObjectiveThe goals of our study were to investigate longitudinal changes in psychological states that are manifested through linguistic changes in depression community members who are interacting with other depressed individuals.MethodsWe examined emotion-related language usages using the Linguistic Inquiry and Word Count (LIWC) program for each member of a depression community from Reddit. To measure the changes, we applied linear least-squares regression to the LIWC scores against the interaction sequence for each member. We measured the differences in linguistic changes against three online health communities focusing on positive emotion, diabetes, and irritable bowel syndrome.ResultsOn average, members of an online depression community showed improvement in 9 of 10 prespecified linguistic dimensions: “positive emotion,” “negative emotion,” “anxiety,” “anger,” “sadness,” “first person singular,” “negation,” “swear words,” and “death.” Moreover, these members improved either significantly or at least as much as members of other online health communities.ConclusionsWe provide new insights into the impact of prolonged participation in an online depression community and highlight the positive emotion change in members. The findings of this study should be interpreted with caution, because participating in an online depression community is not the sole factor for improvement or worsening of depressive symptoms. Still, the consistent statistical results including comparative analyses with different communities could indicate that the emotion-related language usage of depression community members are improving either significantly or at least as much as members of other online communities. On the basis of these findings, we contribute practical suggestions for designing online depression communities to enhance psychosocial benefit gains for members. We consider these results to be an important step toward a better understanding of the impact of prolonged participation in an online depression community, in addition to providing insights into the long-term psychosocial well-being of members.

"How Did We Get Here?": Topic Drift in Online Health Discussions.

BackgroundPatients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic—topic drift—can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts.ObjectiveOur goals were to understand topic drift in online health communities and then to develop and evaluate an automated approach to detect both topic drift and efforts of community members to counteract such drift.MethodsWe manually analyzed 721 posts from 184 threads from 7 online health communities within WebMD to understand topic drift, members’ reaction towards topic drift, and their efforts to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction efforts. We detected topic drift by calculating cosine similarity between 229,156 posts from 37,805 threads and measuring change of cosine similarity scores from the threads’ first posts to their sequential posts. Using a similar approach, we detected counteractions to topic drift in threads by focusing on the irregular increase of similarity scores compared to the previous post in threads. Finally, we evaluated the performance of our automated approaches to detect topic drift and counteracting efforts by using a manually developed gold standard.ResultsOur qualitative analyses revealed that in threads of online health communities, topics change gradually, but usually stay within the global frame of topics for the specific community. Members showed frustration when topic drift occurred in the middle of threads but reacted positively to off-topic stories shared as separate threads. Although all types of members helped to counteract topic drift, original posters provided the most effort to keep threads on topic. Cosine similarity scores show promise for automatically detecting topical changes in online health discussions. In our manual evaluation, we achieved an F1 score of .71 and .73 for detecting topic drift and counteracting efforts to stay on topic, respectively.ConclusionsOur analyses expand our understanding of topic drift in a health context and highlight practical implications, such as promoting off-topic discussions as a function of building rapport in online health communities. Furthermore, the quantitative findings suggest that an automated tool could help detect topic drift, support counteraction efforts to bring the conversation back on topic, and improve communication in these important communities. Findings from this study have the potential to reduce topic drift and improve online health community members’ experience of computer-mediated communication. Improved communication could enhance the personal health management of members who seek essential information and support during times of difficulty.

Peer-Generated Health Information: The Role of Online Communities in Patient and Caregiver Health Decisions

Individuals increasingly access peer-generated health information (PGHI) through social media, especially online health communities (OHCs). Previous research has documented PGHI topics, credibility assessment strategies, and PGHI’s connection with well-being. However, there is limited evidence on where, when, and why individuals seek PGHI and how they use PGHI in health decisions. We conducted in-person and online focus groups with verified OHC members (N = 89)—representing 50 different medical conditions and 77 OHCs—to explore these topics. Two researchers independently coded transcripts with NVivo 9.2 and thematically analyzed responses. Most individuals accidentally discovered PGHI during Web searches rather than intentionally seeking it. Individuals valued PGHI primarily as an alternative information source about treatment options, self-care activities, and health care provider questions rather than a source of emotional support, and they acknowledged PGHI’s limitation as anecdotal evidence. Individuals used PGHI as a springboard for additional research and patient–provider discussions, ultimately making treatment decisions alongside providers. These findings suggest that individuals use PGHI in much the same way they use traditional online health information and that PGHI facilitates, rather than obstructs, shared decision making with health care providers.

Lessons Learned for Online Health Community Moderator Roles: A Mixed-Methods Study of Moderators Resigning From WebMD Communities.

BackgroundOnline health community (OHC) moderators help facilitate conversations and provide information to members. However, the necessity of the moderator in helping members achieve goals by providing the support they need remains unclear, with some prior research suggesting that moderation is unnecessary or even harmful for close-knit OHCs. Similarly, members’ perceptions of moderator roles are underexplored. Starting January of 2013, WebMD moderators stopped working for WebMD communities. This event provided an opportunity for us to study the perceived role of moderators in OHCs.ObjectiveWe examine the OHC members’ perception on OHC moderators by studying their reactions toward the departure of moderators in their communities. We also analyzed the relative posting activity on OHCs before and after the departure of moderators from the communities among all members and those who discussed moderators’ departures.MethodsWe applied a mixed-methods approach to study the posts of all 55 moderated WebMD communities by querying the terms relating to discussions surrounding moderators’ disappearance from the WebMD community. We performed open and axial coding and affinity diagramming to thematically analyze patients’ reactions to the disappeared moderators. The number of posts and poster groups (members and moderators) were analyzed over time to understand posting patterns around moderators’ departure.ResultsOf 821 posts retrieved under 95 threads, a total of 166 open codes were generated. The codes were then grouped into 2 main themes with 6 total subthemes. First, patients attempted to understand why moderators had left and what could be done to fill the void left by the missing moderators. During these discussions, the posts revealed that patients believed that moderators played critical roles in the communities by making the communities vibrant and healthy, finding solutions, and giving medical information. Some patients felt personally attached with moderators, expressing they would cease their community participation. On the other hand, patients also indicated that moderators were not useful or sometimes even harmful for peer interactions. The overall communities’ posting activity, which was already in decline, showed no significant difference before and after the moderators’ departure. In fact, the overall posting activities of the communities were declining well before the moderators’ departure. These declining posting activities might be the reason why WebMD removed the moderators.ConclusionCompassionate moderators who provide medical expertise, control destructive member posts, and help answer questions can provide important support for patient engagement in OHCs. Moderators are in general received positively by community members and do not appear to interfere with peer interactions. Members are well aware of the possibility of misinformation spreading in OHCs. Further investigation into the attitudes of less vocal community members should be conducted.

Knowledge sharing in online health communities: A social exchange theory perspective

Online health communities (OHC) are becoming valuable platforms for patients to communicate and find support. These communities are different from general online communities. The knowledge shared in an OHC can be categorized as either general (public) or specific (private), and each category is shared in vastly different ways. Using the social exchange theory, we propose a benefit vs. cost knowledge sharing model for OHCs. The benefits are mainly based on Maslow's hierarchy of needs, and the cost includes cognitive and executional costs. We use this benefit vs. cost model to examine how OHC members share general and specific knowledge. Data were collected from 323 users of two well-known OHCs in China and were analyzed using the structural equation model. The results demonstrate that three factors positively impact the sharing of both general and specific knowledge: a sense of self-worth, members’ perceived social support, and reputation enhancement. Another factor, face concern, has a negative influence on specific knowledge sharing and a positive influence on general knowledge sharing. Executional cost only negatively impacts general knowledge sharing, and cognitive cost only negatively impacts specific knowledge sharing. This study of OHCs reveals that personal benefits promote knowledge sharing and costs prohibit it. These impacts vary between general knowledge and specific knowledge sharing.

Patient value co-creation in online health communities

Purpose– Patient value co-creation represents a key research priority and an essential determinant of health care service outcomes. Yet few studies empirically examine the factors that motivate patients to participate actively in value co-creation. The purpose of this paper is to seek to identify the motivators of such activities in online health communities (OHC) and examine their specific and unique effects.Design/methodology/approach– A netnographic study helps identify the motivators that drive patients’ value co-creation activities in OHCs. The combination of these results with social identity theories suggest the hypotheses; mediation analyses test the hypothesized model with data collected from eight OHCs that address both life-threatening and non-life-threatening illnesses.Findings– The netnographic results show that social identity drives patients’ value co-creation activities. Interactions among OHC members and the cognitive resources of the OHC both contribute to the development of its social identity. Furthermore, benevolence trust, shared vision, and shared language determine how likely an OHC member is to identify with a particular OHC, which further influences his or her value co-creation activities in that OHC.Originality/value– Although value co-creation is critical to the health care sector, few studies examine antecedents of patient value co-creation empirically. This study represents an initial attempt to do so by combining innovative netnographic analyses with mediation analyses.

The influence of social capital on knowledge creation in online health communities

This study investigates how social capital influences knowledge creation in online health communities (OHCs), based on social capital theory. The study proposes that three social capital dimensions, namely trust, shared language, and network density, affect OHC members' externalization and combination of knowledge creation, and that these in turn lead to membership continuance intention (MCI). Trust also directly contributes to MCI in the model. Three hundred fifty-three members from eight existing US based OHCs participated in surveys. The results provide overall support for the proposed model. Among the three dimensions of social capital, network density plays a critical role in developing both externalization and combination of knowledge creation. Externalization of knowledge in turn affects members' MCI. Supplemental analysis shows that trust and shared language influence OHC members' knowledge creation indirectly via network density.

Building Trusting Relationships in Online Health Communities

This study investigates consumers' use of online health communities (OHCs) for healthcare from a relationship building perspective based on the commitment-trust theory of relationships. The study proposes that perspective taking, empathic concern, self-efficacy, and network density affect the development of both cognitive and affective trust, which together determine OHC members' membership continuance intention (MCI) and knowledge contribution. Data collected from eight existing OHCs (N=255) were utilized to test the hypothesized model. Results show that perspective taking and self-efficacy can increase cognitive trust and affective trust, respectively. Network density contributes to cognitive and affective trust. Both cognitive trust and affective trust influence MCI, while only affective trust impacts members' knowledge contribution behaviors.

Feel Blue so Go Online: An Empirical Study of Online Supports among Patients

This paper investigates the helpfulness of an online healthcare community to patients’ health conditions.We propose an inhomogeneous Partially Observed Markov Decision Process (POMDP) model to examine the latent health outcomes of online health community members. The transition between different health states is modeled as a probability function which incorporates different forms of social supports that patients receive via online communication, and other factors that would impact patients’ online behaviors. We find that patients gain benefits from learning from others, and the participation in the online community helps them to improve their health conditions and better engage in their disease self-management processes. Our results also reveal the effect of various forms of social supports on dynamic evolution of patients’ health conditions. Informational support on average contributes the most to helping patients move to a better health state. Peer recognition and self-identification are also found to have significant impact on the transitions between patients’ health states. Finally, we demonstrate that our POMDP model provides a very accurate method to recover missing or unavailable information on patient health conditions.

Feeling Blue? Go Online: An Empirical Study of Social Support Among Patients

In this paper, we investigate whether social support exchanged in an online healthcare community benefits patients’ mental health. We propose a nonhomogeneous Partially Observed Markov Decision Process (POMDP) model to examine the latent health outcomes for online health community members. The transition between different health states is modeled as a probability function that incorporates different forms of social support that patients exchange via discussion board posts. We find that patients benefit from learning from others and that their participation in the online community helps them to improve their health and to better engage in their disease self-management process. Our results also reveal differences in the influence of various forms of social support exchanged on the evolution of patients’ health conditions. We find evidence that informational support is the most prevalent type in the online healthcare community. Nevertheless, emotional support plays the most significant role in helping patients move to a healthier state. Overall, the influence of social support is found to vary depending on patients’ health conditions. Finally, we demonstrate that our proposed POMDP model can provide accurate predictions for patients’ health states and can be used to recover missing or unavailable information on patients’ health conditions.