Recommender systems in the healthcare domain: state-of-the-art and research issues

There are three main aspects that need to be considered in recommender systems: usage context, users, and items (Sánchez-Bocanegra et al. 2015). Usage context describes the environment where all elements (e.g., items, users, and their relationship) interact with each other. Users are the end-users of recommender systems, and items are the elements that users are looking for. In the healthcare domain, additional aspects concerning the mentioned elements should be considered to generate more precise recommendations.

The information of the mentioned elements can be the input of algorithms that generate personalized recommendations to patients.

Due to the extensive growth of food variety and busy lifestyles, people have been facing the issue of making healthy food decisions to reduce the risk of chronic diseases (Ge et al. 2015; Robertson et al. 2004). In this context, food recommender systems can motivate users to change their eating behaviors or suggest healthier food choices (Tran et al. 2018; Trattner and Elsweiler 2017; Yang et al. 2017). In the following, we summarize scenarios where food recommender systems support users in optimizing their nutrition intake. Studies on food recommendation were presented in our earlier survey (Tran et al. 2018). However, different from (Tran et al. 2018) where these studies were grouped based on recommendation techniques, in this article, they are grouped according to dietary needs. Besides, we include additional studies on “food-substitutes suggestions” to increase the coverage of the article. For the studies already presented in our earlier survey, we shortly mention the general idea of recommendation algorithms, and for further details, we refer to (Tran et al. 2018).

In the past decades, predicting risks concerning specific diseases has become an intensive research topic (Davis et al. 2009), where the number of related studies for chronic diseases has been increasing significantly. The reason lies in the rapid growth of these diseases worldwide (Hussein et al. 2012). Long-term diseases prevent patients from physical activities and trigger burdens concerning time and money of the treatment process (Nasiri et al. 2016). To help patients avoid these diseases, HRS have been developed to detect disease symptoms as early as possible. Moreover, they can assist healthcare professionals in making proper treatment plans for patients. Davis et al. (Davis et al. 2009) and Nasiri et al. (Nasiri et al. 2016) proposed recommender systems to predict risk factors (e.g., potential complications or further diseases) that a target patient with a chronic disease would face in the future. These systems applied CF, which is based on the intuitive assumption “patients who share similar diseases and health status might face the same risk factors”. Predictions of disease risks can be generated based on a set of similar patients’ information. The traditional CF technique is modified to make it suitable for the healthcare domain. The reason for this modification lies in the rating values of items. The patients’ ratings are non-ordinal values; they are binary (1/0 - the patient is facing/not facing a risk factor j). For this approach, given an active patient a, a set of patients I, and a set of risk factors J, the risk factor prediction is generated in the following steps:

Besides recommendations of disease treatment plans, suggestions on physical activities have become another focus of HRS. Physical-activity recommendations help to decrease the probability of becoming frail of patients and prevent them from further health complications (Valdez et al. 2016). Moreover, they also encourage users to follow daily activities that meet their calorie-burn goals. Runner (Donciu et al. 2011) and Shade (Faiz et al. 2014) provide users with food and exercise recommendations to keep them stay healthily. These studies generate a recommendation based on the fact that “what and when you eat during and after exercise can be just as important” (Donciu et al. 2011). These recommendations are tailored based on users’ health status, goals, and preferences, which are usually collected from different sources, such as foods, physical activities, elderly/diabetes/runner domains, user-health state, and user preferences. Therefore, ontologies and semantics technologies (Orgun and Vu 2006) are utilized to address the heterogeneous issues of user data. The recommendation process can be done as follows: First, an initial set of exercises for the user is selected based on his/her physical health status and exercise goal. Thereafter, the usage history and prior feedback regarding difficulty and enjoyment levels are used to adapt the selected exercises before sending it to the user.

Dharia et al. (Dharia et al. 2016) proposed a system to suggest personalized workout session recommendations based on the contextual data of users, such as past activities, preferences, and physical state. The recommendation process is performed as follows. The user first enters his/her contextual data. Thereafter, the system collects all the contacts and calendars events from the user’s device and employs a hybrid approach to recommend fitness sessions to the user. This approach combines CB and CF recommendations, in which the CB considers the user’s preferences, and the CF considers the preferences of similar users. The system also offers available slots in the user’s calendar so that he/she can re-schedule sessions anytime.

Imran Ali et al. (Ali et al. 2018) developed a hybrid framework that provides physical activity and diet recommendations using context-aware recommendation (Verbert et al. 2012) and knowledge-based recommendation (Burke 2000). The proposed framework consists of a multi-stage recommender system which supports the following modules:

In recent years, there has been a significant increase in the amount of available medical information, which results in some difficulties for patients when searching for suitable doctors. What concerns patients greatly is how to find medical professionals with the best expertise for resolving their health issues (Hoens et al. 2010; Narducci et al. 2015). Most existing healthcare providers do not provide patients with full infrastructure or service design implementations that assist them in fulfilling this task. This gap raises an open topic on patient-doctor matchmaking, in which patients can find the right doctors to build a trust relationship (Han et al. 2018). Han et al. (Han et al. 2018) proposed a hybrid recommender system, in which family-doctor recommendations are made based on the level of available information about users. The authors discussed three use cases of generating recommendations:

Zhang et al. (Zhang et al. 2016) proposed an iDoctor system to provide users with personalized doctor recommendations. This system explores the emotions and preferences of users about doctors through their ratings and reviews. Three modules are integrated into the system: sentiment analysis, topic modeling, and hybrid matrix factorization. The sentiment analysis module calculates the emotional offset from user reviews. The topic modeling module extracts user preferences and doctor features (e.g., specialty, fee range, and prescribing habits) from user reviews. The extracted information is used in the hybrid matrix factorization module to predict the rating for doctors.

Gujar et al. (Gujar et al. 2018) proposed a recommendation approach based on the “word of mouth” recommendation (e.g., asking friends or relatives), which is often used in reality to find doctors. The authors developed a recommender system to identify the location, contact, and other necessary information of medical specialists. They used the CoreNLP technique to generate doctor recommendations based on the review of previous users. The recommended doctors are filtered out based on some criteria, such as fewer fees, more experienced, nearest location, and feedback reviews of doctors. Different from the studies discussed earlier, this system allows patients to give their feedback on recommended doctors, which are then used to improve the quality of future recommendations. Based on a similar recommendation mechanism as mentioned in (Gujar et al. 2018), Narducci et al. (Narducci et al. 2015) presented a social network so-called HealthNet, where a recommendation component is integrated to suggest doctors and hospitals which best fit a specific patient profile. In HealthNet, a patient enters his/her health data, such as conditions, treatments (drugs, surgeries, or side effects), health indicators (blood pressure, body weight, laboratory analysis, etc.), consulted doctors, and hospitalizations. Based on the input data, the system search for similar patients stored in the database. The similarity between the active patient p and another patient p^′ in the database is estimated using Formula (8), where:

Given an active patient p_i, the relevant doctors and hospitals for this patient can be estimated using Formulae (11) and (12).

In HRS, besides user preferences that are typically used in recommender systems, further user information should be collected to obtain relevant, diverse, and precise recommendations. Thereof are demographic information, current health condition, diseases/allergies, treatments/surgeries/diagnoses experienced in the past, physical activities, nutrition needs, eating habits, feelings, and experiences. Although many sources exist to accumulate this information, recording such information could not avoid faults (Mika 2011). Hence, it is critical to require a standard concerning data formats, the authenticity of data sources, and automated update intervals (Schäfer et al. 2017) to ensure the quality of obtained information. Besides, the user profile parameters could be conflict with each other (e.g., user preferences vs. health conditions). To guarantee optimal suggestions in terms of balancing between user satisfaction and healthiness, parameters in the user profile need to be deemed wholly and appropriately. In some cases, the parameters regarding health conditions should be taken precedence over those concerning user preferences. For instance, a user diagnosed with a high risk of having diabetes should not be recommended any food containing trans-fats, even this recommendation goes against his preference.

Many reports show that patients suffering from chronic diseases or cancers are often not well perceived about their disease or treatment options until the disease situations fall in late stages (Schäfer et al. 2017). The late detection of such diseases causes a low probability of completely curing the disease, and in some cases, this could threaten patients’ lives (e.g., the late stage of cancers). In such a context, besides assisting patients in finding suitable treatment methods (Davis et al. 2009; Nasiri et al. 2016), HRS should offer users a health education functionality that helps to improve the perception of users concerning the diseases. Stettinger et al. (Stettinger et al. 2020) developed an e-learing application so-called KnowledgeCheckr¹², which provides intuitive learning contents and suggests learning units in a personalized fashion. With this application, helpful information concerning disease descriptions and related symptoms can be transferred to patients. Moreover, HRS should analyze the underlying health condition of patients and predict early diseases that users might face in the near future. Also, necessary diagnoses and information of corresponding healthcare professionals should be delivered to patients. For chronic and life-threatening diseases, such early disease detection can minimize disease complications and the treatment process’ burdens.

One focus of HRS is to track users’ daily activities and motivate them to adjust their routines or habits positively. However, it seems to be a big challenge to change habits that have become so deeply entrenched over the years (Tran et al. 2018). Therefore, researchers have recently paid attention to develop persuasive systems, in which various strategies and persuasion principles are discovered to encourage users to adopt and maintain beneficial behaviors and attitudes. For instance, Thomas et al. (Thomas et al. 2017) investigated argument-based approaches in which motivating arguments are created to change the eating habit of users healthily. These studies indicate that it is necessary to produce persuasive arguments based on user attributes such as age, gender, or personality. Although these studies show positive effects on the behavior changing of users, it does not guarantee full acceptance of changes. The argumentation-based approaches have been proved to be sufficiently effective for patients in the late stages of the disease, whereas they show a lower effect for the patients in the early stages of the disease (Nguyen and Masthoff 2008). This raises an open issue of developing arguments that are strong, relevant, and convincing enough to bring actual changes for those in the early phases of health risks. Besides, while many efforts have been conducted to estimate the arguments’ perceived persuasiveness, measuring the actual persuasiveness of arguments is still an open issue. In fact, what people perceive to be persuasive is not necessarily what will persuade them to act. In the healthcare domain, this can be interpreted that users might be unwilling to change their behavior, even though they are aware of the risks triggered by unhealthy habits (Nguyen and Masthoff 2008). For instance, although some people may perceive the harmful effects of smoking, they are not ready to give it up. On the other hand, changing users’ behavior or attitude is a long-term process with plenty of steps. In this context, the question is “how to generate persuasive arguments that motivate users as much as possible”. In the healthy food domain, the answer to this question could be to develop food recommender systems, where theories from health psychology are integrated to stimulate users to comply with healthy eating behaviors (Schäfer and Willemsen 2019). One approach is to apply a simple change at a specific time until the user behavior becomes habitual. Another approach is to compare nutrients consumed by the user to the ones acquired from reliable sources (e.g., USDA, DACH¹³) (Snooks 2009).

Typically, the evaluation of recommender systems emphasizes the accuracy metrics (Powers 2011) (see also Section 5). However, in the healthcare domain, recommender systems’ quality needs to be measured based on aspects beyond the accuracy objectives (Valdez et al. 2016).

Trust is one of the most important criteria that should be considered when evaluating recommender systems (O’Donovan and Smyth 2005). This is even more critical for HRS to convince patients to follow health-related recommendations. This aspect can be enhanced by providing explanations for recommendations (Tran et al. 2019). Similar to other domains, explanations in the healthcare domain should show how a suggestion has been created for the user (Elahi et al. 2014), e.g., “According to the tests you did last week, we have detected that the level of uric acid in your blood is still really high. Therefore, r_i seems to be the most appropriate recipe for you since it has no ingredients containing purines”. Besides, effective visualizations should be included in HRS to further explain recommended items (Valdez et al. 2016). For instance, in food recommender systems, a table showing the description of the nutrition value of food items should be provided to the users to emphasize the healthiness of a recommended recipe (Tran et al. 2018).

Causability helps users understand why specific recommendations have been made. This criterion is useful in many domains and especially crucial in the medical domain to enhance trust in the results and enable domain experts to retrace, understand, and explain why a particular recommendation was given. This does not necessarily mean that everything must be explained automatically, but that a domain expert has a chance to understand it on demand. To measure the understandability of recommendations, the concept of causability can be helpful. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations (Holzinger et al. 2019).

Robustness is related to the trustworthiness of a recommender system. In HRS, sometimes, end-users could not be differentiated from potential attackers, which causes a degradation of trust in the objectivity and accuracy of the system (Valdez et al. 2016). To ensure secure HRS for users, future studies should model potential attacks and investigate the impacts of such attacks on recommendation algorithms (Mobasher et al. 2007).

Privacy is referred to as the ability of HRS to preserve patients’ preferences and medical information. The leak of such information raises the doubts of patients and consequently decreases the willingness to share their sensitive medical data with HRS (Valdez et al. 2016). The most common approach to address the privacy concern is data encryption that provides data confidentiality while utilizing the user data to generate precise recommendations (Hoens et al. 2010). However, this method requires highly overhead computation- and communication-wise, which significantly decreases the performance of HRS (Verhaert et al. 2018). Although there exist some studies to improve the data encryption approach, some of them still face the issue concerning the low efficiency of the system (Hoens et al. 2010; Verhaert et al. 2018). Up to now, developing HRS ensuring trade-offs between the privacy and the high performance of recommendation algorithms is still an open issue.

Ethics has been raised in recommender systems to help users pick up morally appropriate items during the post-recommendation process (Tang and Winoto 2016). In HRS, ethics should be considered more strictly to prevent recommendations from directions that could harm the healthiness of patients (Valdez et al. 2016). The principle of “first do no harm” should be kept in mind when developing HRS to minimize potential risks and maximize benefits for users. The healthiness of patients is the most crucial criterion when creating recommendations, even this might be against patients’ preferences (Tang and Winoto 2016).

User satisfaction with recommendations can be different depending on user diversity. Some studies have been performed recently to have a deep look at modeling user satisfaction for the purpose of predicting satisfaction models (Chen et al. 2019; Nguyen et al. 2017). In HRS, it would make sense to investigate the relationship between health-related recommendations and users’ satisfaction from different user groups, e.g., patients, doctors, nurses, physicians, and medical researchers (Valdez et al. 2016). The differences in expertise, overview knowledge, and recommendation tasks of these users could influence their satisfaction with recommended items.

Uncertainty in HRS links to potential risks, such as imprecise predictions since user preferences are not always captured well, or the inability to find a perfect pattern because of incomplete data. The risks could result in a reduced quality of the patient’s life. Therefore, when developing HRS, system designers should find ways to visualize uncertainty in a set of recommendations, allowing users to evaluate the option adequately before making a final decision (Valdez et al. 2016).

Effectiveness is referred to as the ability of HRS to help patients meet their desired changes in health. To measure this aspect, we need to consider which health parameters to be assessed or which medical tests to be employed to ensure medical effectiveness. For instance, in HRS that support the lose-weight targets of users, the effectiveness should be assessed based on both short-term and long-term recommendations. The reason is that, in some cases, short-term recommendations could burden or conflict with long-term ones. For instance, crash diets could help a patient lose weight quickly since it cuts the calories too low and makes drastic changes regarding food types to be consumed. However, this reduces the metabolism of the patient’s body and consequently burdens the long-term weight loss (Valdez et al. 2016).

In-Situ Evaluation indicates real-life non-laboratory settings that have to be evaluated to prove the worthiness of HRS. This evaluation paradigm should be able to precisely evaluate the ability of HRS to improve the quality of care (concerning accuracy, relevance, and early diagnosis) and reduce the cost of care. Besides, it should be capable of evaluating the robustness to false information and the ability to consider potential health risks based on various dimensions (e.g., age, culture, ethnicity, etc.). Moreover, long-term behavioral effects must also be investigated in-situ evaluation to address the complexity of health and health behaviors (Schäfer et al. 2017).

In the healthy food domain, users might require recommendations of a complete meal with the combination of many recipes or a food schedule for more than one day (e.g., foods for next week). This issue is known as bundle recommendation, which is a new research branch of recommender systems. The idea here is to recommend a sequence of items instead of separated ones. Recommending a complete meal is quite complicated since the system has to consider not only the preferences of users but also other aspects, such as the meal variety, weather and season, the healthiness of recipes, health problems, or nutrition needs. Thus, approaches to generate bundle recommendations in the healthy food domain have remained an open issue.

For some scenarios (i.e., diet recommendation), recommended items could involve groups of users rather than individual users (e.g., recommend a menu for a Christmas party). The current literature shows a limited number of studies on food recommender systems for groups. Therefore, it is still an open topic that needs to be analyzed in future research. Group recommender systems usually attach the requirements/preferences of different users into group recommendation. This is the crucial idea discussed in many related studies (Berkovsky and Freyne 2010; Felfernig et al. 2018; O’Connor et al. 2001). Recommending a joint meal for a group of users is a complicated task since different goals and dietary constraints of group members should be taken into account. While we have a solution for merging the constraints (Atas et al. 2019), a solution for merging goals is still an open issue. Besides, recommendations generated for groups should assure fairness among group members, which means negotiation and argumentation mechanisms have to be developed to support group members in expressing acceptable trade-offs (Felfernig et al. 2014). For instance, in a meal plan for a week, users’ preferences ignored in previous meals should have a stronger influence on the upcoming meals. On the other hand, although different aggregation approaches have been applied to generate group recommendations, they do not ensure that recommended items reflect a high agreement level among group members (Castro et al. 2015). In this context, a consensus making process is needed to bring individual preferences closer to each other before delivering group recommendations. Further issues need to be considered to accelerate such a process. One promising solution is to enrich user interfaces that allow group members to share their preferences (Nguyen and Ricci 2017). Besides, psychological aspects (e.g., personality and emotions) beyond group members’ preferences are also crucial to be taken into account in group decision making. This draws an open topic regarding the influence of group members’ personality and emotions on group recommendation strategies (Quijano-Sanchez et al. 2013).