In this paper we present HealthNet (HN), a social network that helps patients to meet the best doctor for her health condition. The core component of HN is a recommender system that suggests to the user patients similar to her, and generates suggestions about doctors and hospitals that best match her patient pro le. Currently an alpha version of HN is available only for Italian users, but in the next future we want to extend the platform to other languages. We organized three focus groups with patients, practitioners, and health organizations in order to obtain comments and suggestions. All were very enthusiastic by using the prototype version of HN1.
The e-health, de ned as the healthcare practice supported by electronic process
and communication [
In this paper we present HealthNet (HN), a social network whose main goal
is to help a user in nding a solution for her health conditions. The main idea
behind HN is the same as PLM: sharing knowledge, nding similar patients,
comparing their experiences. However, the main di erence with respect to PLM
is the embedded recommender system that is able not only to discover
similarities between patients, but also to exploit the data coming from the patient
community for suggesting practitioners and hospitals that best t a patient
prole. In this way, HN deters the self-diagnosis since it just helps the patient to
nd a doctor or a health facility. Di erently from a classical recommender system
which generally builds a user pro les in order to suggest items potentially
interesting for a given user [
Other Health Related Recommender Systems (HRRS) are presented in the
literature [
The rest of this paper is organized as follows. Section 2 describes the platform and its general architecture, and Section 3 draws conclusions and future work.
HN is implemented as a standard social network where users are patients. The rst step of the interaction with the system is the user registration. After that, the patient can insert her medical data. More speci cally, the patient can insert information about: conditions, treatments (e.g., drugs, dosages, side e ects, surgeries), health indicators (e.g., blood pressure, body weight, laboratory analysis, etc.), consulted doctors, hospitalizations. In this way, HN centralizes individual's health data allowing a simple and organized access to them. Furthermore, the user can take advantage from sharing her data by obtaining suggestions in terms of doctors, health facilities, and other resources useful for her conditions. In order to receive recommendations the user should insert at least one condition she is a ected by. For each condition, the user can click on the "How can I cure it?" button and receives suggestions. It is worth noting that the HN user can decide to be anonymous, by indicating only a nickname during the registration step. Accordingly, the health data inserted in HN are not linkable to a real identity thus preserving the user privacy.
In Figure 1 a general architecture of the platform is depicted. There are three main components: the Pro le Manager, the Social Manager, the Recommender System. The interaction with the system occurs through a Web GUI.
This component manages all information related to the patient state of health. The Pro le Manager allows a user to decide which data she wants to share with the community and which data wants to maintain private. The component manages also information about consulted doctors, hospitalizations, success or failure of therapies/treatments, monitoring. The Pro le Manager stores these information in a patient pro le which is exploited by the Recommender System for generating suggestions. In the actual version of the system the patient pro le is composed of two distinct dimensions: conditions and treatments. These are the only information exploited by the system for computing patients similarities. 2.2
This component manages the activities related to the social network interactions. It allows one to establish relations between patients by means of friendship connections. Friends share their updates, information about drugs, most common conditions or symptoms they are used for, side e ects, dosages. The Social Manager also manages the Health Point of Interests (HPOIs). A HPOI is a point of interest which o ers services related to the health domain useful for the HN community. A HPOI can be an association which o ers home care, or an organization which supports patient relatives, for example. The association or organization which desires to have a page on HN for adrvetising its activities, can sign up on the platform and indicate the conditions for which o ers services or facilities. The HPOIs are then suggested by performing a simple matching between the relevant conditions de ned by the HPOI owner and the patient condition. 2.3
This is the core component of the system. The Recommender System exploits the pro le of the patients to suggest other similar patients as well as the best doctors or hospitals according to a given patient pro le. The similarity between patients is computed in terms of shared conditions and shared treatments. The component computes a semantic matching between the conditions by exploring the hierarchy of diseases 6. The idea is that a patient with prostate cancer and another with testicular cancer, for example, should have a high similarity score since both conditions a ects organs belonging to the male reproductive system (in the disease hierarchy). Hence, even though the two patients do not have the same condition can share useful experiences. Similarly, the matching between treatments takes into account not only their names but also the active ingredients (for drugs), and organ it a ects (for surgeries). The recommender system combines the similarity score deriving from the patient conditions and the score deriving from the patient treatments to compute a similarity score between two patients. Suggestions of doctors and hospitals are thus ranked by analyzing
doctors and hospitals consulted by the most similar patients. Furthermore, the ranking takes also into account o cial quality indicators generated by the Italian Ministry of Health7.
More formally, the similarity score between two patients is computed as follows: (1) (2) (3) s(p; p0) =
Pk
i=1 +(1 )
Pz i=1
Pn j=1 sc(pci ; p0cj ) + k + n Pr j=1 st(pti ; p0tj ) ; z + r where k (respectively n) is the number of conditions p (respectively p0) is a ected by, pc is a condition of the patient p, z (respectively r) is the number of treatments for p (respectively p0), pt is a treatment for the patient p, sc(pci ; p0cj ) is the condition similarity between ci, and cj , st(pti ; p0tj ) is the treatment similarity between ti, and tj , computed as follows: sc(pci ; p0cj ) = st(pti ; p0tj ) = (1 log ##PCci ; if ci = cj
1 sp(ci;cj) ;
otherwise (1; if ti = tj 0; otherwise If the two conditions are the same, the similarity score sc is equal to a weight computed as the ratio between the number of conditions in the database (#C) and the number of patients a ected by that condition (#P ci). The goal of this additional weight is to give higher similarity to patients which share rare diseases. If the two conditions are di erent the score sc is computed as the reciprocal of the length (number of edges) of the shortest path sp which connects the two conditions in the disease hierarchy. More simply, the treatment similarity is 1 when the treatments are the same or (for drugs) have the same active ingredient, 0 otherwise. Treatment similarity and condition similarity score can di erently contribute to the patient similarity score by changing the value. The patient similarity is thus used for ranking the list of suggested doctors and hospitals. Doctors and hospitals are ranked according to the scoreDoc and scoreH. The scoreDoc for the doctor dz and the patient pi is computed by multiplying for each patient pj in the database the similarity score with pi and the rating assigned by pj to the doctor dz. Similarly, the scoreH, takes into account the similarity patient, the user rating rj for a given hospital hm and a quality indicator produced by the Italian Health Ministry for each Italian hospital8. The community indicator and the ministry indicator can be di erently weighted by changing the value. 7 For each hospital the number of admissions and the number of deaths are reported for a given treatment. http://95.110.213.190/PNEed13/ 8 http://95.110.213.190/PNEed13/
scoreDoc(dz; pi) = p j=1
X s(pi; pj ) rj (dz) scoreH(hm; pi) = 0 p @X s(pi; pj ) rj (hm)A (1 j=1 1 )qi(hm) (4) (5) 3
In this paper we presented HealthNet, a health social network available in alpha version for Italian users. HN suggests doctors or health facilities for a given patient condition by using experiences shared from the patient community. The recommender system implements a semantic matching able to compute a similarity also between patients which do not share the exact same condition. We are building a dataset in order to test our algorithm trough an in-vitro experimental evaluation. Subsequently we intend to perform a case study with real users. In the future work we want to extend the system to other languages, evaluate di erent similarity measures and allow user to export and share (e.g, with her practitioners) all her health data.