Table 2.
Provision of health recommendations.
| Author (year) | Description of how the system provides health recommendations | Feedback used | Type |
|---|---|---|---|
| Abbas (2016)29 | The proposed personalised healthcare service on social media (Twitter) offers risk of disease assessment and wellness management. Two frameworks are involved: 1. Risk of disease assessment using an approach based on collaborative filtering. Based on the attributes specified in the users’ query, the enquiring users’ profiles are compared with existing users who have the same disease, and the enquiring users are given a risk assessment score for that disease; 2. Recommends health experts to end-users on social media, by processing the users’ tweets and identifying and ranking relevant health experts, using the hubs and authority approach, i.e. the expert users are segregated from the tweet repositories based on their use of disease-specific keywords and then ranked. | NR | Othera |
| Ali (2016)30 | Multimodal hybrid reasoning methodology (HRM) integrates rule-based reasoning (RBR) (knowledge rules from physical activity guidelines), case-based reasoning (CBR) (knowledge from experts’ past experiences) and preference-based reasoning (PBR) approaches (users’ preference) to enable recommendations for healthy physical activities; the RBR generates goal, weight status and plan recommendations, the CBR part suggests the top three relevant physical activities for executing the recommended plan, and the PBR part filters out irrelevant recommendations from those suggested using user preferences. | NR | Messages |
| Almazro (2010)25 | This proposed hybrid collaborative recommender system combines user-based and item-based filtering approaches. First, items and users are categorised based on personal attributes. Then, suitable items from both a content-based algorithm and a user-based algorithm are extracted. These items are ranked from the most appropriate to the least appropriate for the target user. Both item sets are merged, and the top number of items are recommended. | NR | NR |
| Capella (2015)26 | This hybrid recommender system generates content similarity from comparing the items’ content profiles. Some hybrid recommender systems may also combine content similarity and users’ rating similarity for the recommendations. In addition, the recommendations can be enhanced by incorporating extra information on users, such as demographic data, or other domain-specific individual attributes relevant to user similarity. | NR | Messages |
| Chen (2016)31 | Patients can send requests through their cell phones to the system server to obtain a clinic recommendation with maximum utility to the patient. Once the patient sends this information, the system server first estimates the patient’s location and speed according to the detection results of a global positioning system. It then applies a fuzzy integer nonlinear programming-ordered weighted average approach to assess four criteria and finally recommends a clinic with maximum utility to the patient. | NR | Medical provision (clinics) |
| Chomutare (2011)32 | This hybrid recommender system takes the user profile into consideration and feeds it to the recommender engine; the presentation of recommendations regarding potentially relevant content and predictions about potentially interesting peers or communities is based on aggregated ratings by the community of users. | Metadata and explicit feedback | Otherb |
| Sanchez Bocanegra (2017)33 | This recommender system extracts texts from the metadata (video title, descriptions and subtitles) of selected YouTube videos and classifies and recommends items based on terms that encompass properties that are grouped into ontologies. | Metadata | Otherc |
| Colantonio (2015)34 | The proposed multi-sensory device, the Wize Mirror, translates the semeiotic code of the face (combination of physical face signs that reveal something about health) into cardiometabolic risk-related computational descriptors. | NR | Messages |
| Espín (2016)35 | This semantic recommender system provides recommendations via knowledge-based and content-based techniques. For knowledge-based techniques, rule-based reasoning is used to put together a diet that fits the nutritional requirements of the user profile; the content-based recommendations are carried out by means of semantic similarity calculations between nutritional features of food and the user’s previous selections or ‘food rates’. | Metadata | Messages |
| Esteban (2014)36 | This hybrid recommender system generates exercise recommendations based on information from the multimedia database of exercises for recommendations according to patient pathology and patient profile databases that include internal representation of their diagnostics and their personal evaluations of the recommended exercise. | Metadata and explicit feedback | Messages |
| Ge (2015)37 | Information is collected on the user’s preferences. The user can also rate whether a recipe used fits his or her tastes. User preferences are collected to recommend future recipes. | Metadata | Otherd |
| Guo (2016)38 | Collects information on doctors, such as: papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify key opinion leaders. With this information a profile is created for the doctor. The users can than receive recommendations on which doctors to go to for each specific disease. | NR | Medical provision (doctors) |
| Giabbanelli (2015)38 | This recommender system identifies factors relevant to weight management of the user and is matched with evidence-based advice and relevant activities which are then recommended. | NR | Othere |
| Hales (2016)40 | This recommender system includes diet, physical activity, and weight tracking features and a calorie database with commonly consumed food and beverages. Within-app notifications at specific times throughout the day are sent to participants to remind them to self-monitor meals, and minutes of physical activity completed and total body weight are recorded each day. A notes feature allows participants to enter notes relating to their weight loss. | NR | Messages |
| Hidalgo (2016)41 | This recommender system uses case-based reasoning, which provides automatic recommendations to the patient, based on the recorded data and physician preferences, to improve their habits and knowledge about their disease. First, physicians create initial rules with medical information of former patients with the same disease. Then, when patients upload their data to the database, the recommender system compares information with initial rules and creates recommendations. | NR | Messages |
| Honka (2011)42 | This hybrid recommender system involves 1) content-based recommendations that use contextual information, obtained directly from the user or by sensing the environment, to search certain items and present the best matching items as results and 2) collaborative filtering recommendations via context preference elicitation and estimation based on assumption that users with similar interests/profiles are likely to find the same resources interesting. | NR | NR |
| Hors-Fraile (2016)27 | This hybrid recommender system involves content-based, utility-based and demographic filtering to tailor health recommendation messages and optimise the type of messages the user likes. Demographic filtering employs users’ attributes such as gender, employment status, age and quitting date; a utility-based algorithm uses both explicit and implicit utility rates; a content-based algorithm is based on an interest list defined by each user. | Metadata, implicit and explicit feedback | Messages |
| Narducci (2017)43 | This hybrid recommender system can find similar patients based on description of their symptoms, conditions and treatments to suggest health facilities or doctors consulted for patients with similar health status. The recommendation algorithm computes similarities among patients then generates a ranked list of doctors and hospitals for a given patient profile by exploiting health data shared by the community. | Metadata and explicit feedback | Medical provision (doctors, hospitals) |
| Marlin (2013)28 | A system that can learn to tailor future message selections to individual patients based on explicit feedback about past message selections. | Explicit feedback | Messages |
| Lin (2014)44 | This proposed system adopts the episode mining approach to monitor/predict anomalous conditions of patients and can send alarms when needed, and then offers related recommended care guidelines to caregivers so they can offer preventive care in a timely manner. | NR | Messages |
| Sadasivam (2016)45 | This hybrid recommender system provides tailored recommendations via machine-learning algorithms based on data derived from behaviour of users as they interact with the system (implicit and explicit feedback ratings) in addition to user profiles. | Metadata, implicit and explicit feedback | Messages |
| Wang (2016)46 | This system stores the user’s demographic information and service preference. When the user revisits the system to search for health information, the collaborative recommender applies a rough set-based prediction and average categorised-rating calculation to predict user’s target information. Then, the system records the user’s browser log and uses the information to predict target information for other users. | Implicit feedback | Messages |
| Wendel (2013)47 | Recommendations are made on the basis of consumer’s personal information. | NR | Messages |
| Wiesner (2014)48 | This recommender system component is implemented as an extension of an existing patient health record system. For health professionals as end-users, related clinical guidelines or scientific research articles are computed automatically; For patients as end-users, the system computes laymen-friendly content according to the person’s long-term individual medical history, and only highest ranking documents or media content related to health are recommended. | NR | Messages |
| Zhang (2016)49 | The integrated neighbourhood-based method and the restricted Boltzmann machine-based method were integrated to make predictions. The former combines several basic neighbourhood-based models, which adopt different thresholds for finding neighbours. The latter learns the probability distribution governing drug–side effect associations. The authors proposed combining the two methods to develop ensemble-learning models to make robust predictions. Two components are involved: base predictors and ensemble strategy. First, n recommender methods are used to build n recommender models and used as base predictors. Next, an average of all the predictions in the ensemble is adopted as the final prediction. | NR | Otherf |
aRisk of disease assessment score and doctor.
bPeople or communities.
cReputable health educational websites.
dRecipes.
eAlternative strategies for coping with factors influencing obesity.
fDrug side effect.