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Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices

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Abstract

In many health care situations, powerful mobile tools may help to make decisions and provide support for continuous education and training. They can be useful in emergency conditions and for the supervised application of protocols and procedures. To this end, content models and formats with semantic and intelligence have more flexibility to provide medical personnel (both in off-line and on-line conditions) with more powerful tools than those currently on the market. In this paper, we are presenting Mobile Medicine solution, which exploits a collection of semantic computing technologies together with intelligent content model and tools to provide innovative services for medical personnel. Most of the activities of semantic computing are performed on the back office on a cloud computing architecture for: clustering, recommendations, intelligent content production and adaptation. The mobile devices have been endowed with a content organizer to collect local data, provide local suggestions, while supporting taxonomical searches and local queries on PDA and iPhone. The proposed solution is under usage at the main hospital in Florence. The smart content has been produced by medical personnel, with the adoption of the new ADF-Design authoring tool, which produces content in MPEG-21 format. The mobile content distribution service is integrated with a collaborative networking portal, for discussion on procedures and content, thus suggestions are provided on both PC and Mobiles (PDA and iPhone).

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Notes

  1. AXMEDIS (automated production of cross media content for multichannel distribution) (http://www.axmedis.org) is a content media framework developed by an European Commission IST IP Research project [6, 7], with the support of more than 40 partners, among them: University of Florence, HP, EUTELSAT, BBC, TISCALI, TEO, ELION, Telecom Italia, RAI, SIAE, SDAE, FHGIGD, AFI, University Pompeo Fabra, University of Leeds, EPFL, University of Reading, etc..

  2. http://dublincore.org/

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Acknowledgments

The authors would like to thank all the colleagues of the Medical area and in particular Prof. G. Gensini, Prof. L. Corbetta, and Dr. M. Dal Canto. A sincere thank to all the people involved, including Vodafone mobile operator for their support and collaboration.

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Correspondence to Paolo Nesi.

Appendices

Appendix A: main static metrics

1.1 Static metric on user’s languages

The users may have one or more languages and they may be very useful for estimating the similarities among users in multilingual portals and communities. To this end, the following metric has been defined. In a multi-language and multicultural portal the matching on languages is very important to create friendship and communications.

As a first step, a matrix m[i][j] is created where each element represents the similarity between the two languages, i,j. The matrix holds properties such as: m[i][j]=m[j][i] and m[i][j]=1 if i=j. To generate matrix m[i][j] a model has been used according to the above rationales and the following conditions:

  • all languages are classified into families (Latin, Anglo-Saxon, Slovenian, Asian, etc.), groups and subgroups;

  • to each leaf, one language or a set of similar languages is assigned;

  • to each branch a numeric weight, w(fi) treeLevel , is assigned where i is the number of families.

Thus for the hierarchy, the following property holds:

$$ 0 \leqslant \sum\limits_{{j = 1}}^{\rm{BL}} {w{{(fi)}_j} < 1}; \,{\hbox{and}}\,w{(fi)_1} < w{(fi)_2} < ... < w{(fi)_{{BL}}}. $$

Where: j is the tree level for language family fi; BL is the height to reach the leaf in the tree.

The numeric value of element m[i][j] representing the similarity between two languages is calculated adding the weights of all the common branches. Therefore, the similarity Sdl() is calculated taking into account all the languages chosen by the users involved and takes the maximum of similarity. For example, if User1 selects (L1, L4) and User2 selects (L2, L3, L5):

$$ {\hbox{ Sdl(User1, User2) = max }}\left\{ {{{\hbox{m}}_{{12}}};{{\hbox{m}}_{{13}}}{; }{{\hbox{m}}_{{15}}};{{\hbox{m}}_{{42}}};{{\hbox{m}}_{{43}}}{; }{{\hbox{m}}_{{45}}}} \right\}{ } $$

Example:

figure g
$$ { }\left\{ \begin{gathered} {\hbox{Sd}}{{\hbox{l}}_1}{\hbox{(U1, U2) = max }}\left\{ {{{\hbox{m}}_{{13}}};{{\hbox{m}}_{{14}}}{; }{{\hbox{m}}_{{23}}};{{\hbox{m}}_{{24}}}} \right\} \hfill \\where:{{\hbox{m}}_{{13}}} = {{\hbox{W}}_{{{\rm{f1\_ 1 }}}}},{{\hbox{m}}_{{14}}} = {{\hbox{m}}_{{24}}} = 0,{{\hbox{m}}_{{23}}} = {\hbox{W(f1}}{{)}_{{1}}} + {\hbox{W(f1}}{{)}_{\rm{2b}}} + {\hbox{W(f1}}{{)}_{{3}}} \hfill \\\end{gathered} \right. $$

1.2 Static metric on user’s continent and nationalities

The users may have one or more Nationalities/Passports, city and location. This information may be very useful for estimating the similarity among users in multicultural portals and communities. To this end, the following metric has been defined. As a first step, matrix n[i][j] is created where each element represents a similarity between two nations. The matrix has properties such as: n[i][j]=n[j][i] and n[i][j]=1 if i=j. Thus, the similarity Sdn() is calculated by taking into account the Nationalities and cities chosen by the users:

$$ \begin{array}{*{20}{c}} {{\hbox{Sdn(}}U{1,}U{2)} = (n{\hbox{[Nation(}}U1{\hbox{)][Nation(}}U2{)]} + \mathop{\lambda }\nolimits_1 (U1,U2) + \mathop{\lambda }\nolimits_2 (U1,U2))/3} \hfill \\{where\,\mathop{\lambda }\nolimits_1 (U1,U2) = \left\{ {\begin{array}{*{20}{c}} {1,} \hfill & {{\hbox{if Region(U1)}} = {\rm Re} gion({\hbox{U2)}}} \hfill \\{0,} \hfill & {\hbox{otherwise}} \hfill \\\end{array}, } \right.\,and\,:{\lambda_2}(U1,U2) = \left\{ {\begin{array}{*{20}{c}} {1,} \hfill & {{\hbox{if}}\,{\hbox{City}}({\hbox{U}}1) = {\hbox{City}}({\hbox{U}}2)} \hfill \\{0,} \hfill & {\hbox{otherwise}} \hfill \\\end{array} } \right.} \hfill \\\end{array} $$

To generate the values of matrix n[i][j] a decision tree is used, according to the following concepts. The world is divided into 5 continents, each of them contains several Nations (nearby nations are grouped under the same leaf). Therefore, to each branch a numeric weight, w(ci) treeLevel , is assigned \( 0 \leqslant \sum\limits_{{j = 1}}^{{2}} {w{{(ci)}_j} < 1} \); j is the tree level (the tree height is 2), and w(c2)1 < w(c2)2. The same model has been extended to regions and ethnical groups. The numeric value of element n[i][j] representing the similarity is calculated adding the weights of all the common branches. Therefore, the similarity Sdn() is calculated taking into account all the nationalities and regions chosen by the users and takes the maximum of similarity.

Example:

figure h
$$ \left\{ {\begin{array}{*{20}{c}} {{\hbox{S}}{{\hbox{d}}_2}({\hbox{U}}1,{\hbox{U}}2) = \left( {\frac{{{{\text{n}}_{{24}}} + {\lambda_1} + {\lambda_2}}}{3}} \right) = \left( {\frac{{{\text{W}}{{({\text{c}}1)}_1} + 0 + 0}}{3}} \right)} \hfill \\{{\hbox{S}}{{\hbox{d}}_2}({\hbox{U}}2,{\hbox{U}}3) = \left( {\frac{{({\text{W}}{{({\text{c}}1)}_1} + {\text{W}}{{({\text{c}}1)}_{{2{\rm{b}}}}}) + 1 + 0}}{3}} \right)} \hfill \\\end{array} } \right. $$

1.3 Static metric on user’s medical/technical specializations

In the medical area, the users may have collected during their studies one or more specializations, jobs and/or roles. Thus, they may be very useful for estimating the similarity among users in thematic portals and best practice networks. They can be presented as a predefined set of possibilities during the registration. To this end, the following metric for assessing similarity among users’ has been defined.

As a first step, matrix S[i][j] with the similarities among specializations has been created. Each Element S[i][j] of the matrix represents the similarity between two specializations i, j; where: 0≤S[i][j] < 1 if i≠j; while S[i][j] = 1 if they are identical, i = j. In order to build matrix S[i][j], a decomposition in medical areas, subareas and specializations have been performed. Thus, a hierarchy has been created in which leafs correspond to the specialization classes. A set of weights w i (n) have been assigned to the branches of the tree, where i is the tree level in area ni according to:

$$ {w_1}(n) \geqslant 0,{w_1}(n) \leqslant {w_2}(n),0 \leqslant \sum\limits_{{i - 1}}^2 {w{}_i(n)} < 1. $$

The similarity between two specializations is the sum of the weights on the branches in common up to the root.

For example, if user A has set of specializations \( {P_A} = \left\{ {{p_{{A1}}},{p_{{A2}}}, \ldots, {p_{{AN}}}} \right\} \), where \( AN = Card({P_A}) \), and user B has\( {P_B} = \left\{ {{p_{{B1}}},{p_{{B2}}}, \ldots, {p_{{BN}}}} \right\} \), then, each couple of specializations taken from P A , P B determines the couple i,j and thus a value in matrix S[i][j]. Thus, the value of similarity Sds() for users A,B is estimated by using matrix S[i][j], by means of the following model:

$$ Sds(A,B) = \frac{{\sum\limits_{{i \in {P_A}}} {{{\max }_{{j \in {P_B}}}}(S[i][j])} }}{{AN}}. $$

Therefore, each specialization of user A is compared with all the specializations of user B. The maximum value among them, for each specialization of A, is used to estimate an averaged value of similarity. Because each value of S[i][j] is limited to 1, also the averaged final value of Sds() is bounded from 0 to 1, and the metric is not symmetric: Thus, Sds[A][B] may be different with respect to Sds[B][A]. This is due to the fact that the sets of the specializations for the two Users may have different cardinality, while the metric is normalized with respect to the size of the reference User. A way to create a symmetrical metric could be to perform the estimation only on the basis of the parts in common between the two sets. On one hand, a lower precision is obtained, while a non symmetric metric is not creating any problems to semantic computing goals.

1.4 Static metric on user’s groups

In collaborative portal and social networks, the users can create groups of discussion or thematic groups. These groups share commons goals and thus can be used to characterize the user profile. Typically, the users join and leave groups sporadically, thus this feature has been considered static even if it is dynamic. On the other hand, it is very probable that an user join a group during his/her life in the community, and not immediately at the registration time.

Given user A, its groups are G A , and for user B, G B . Thus, the similarity in this case can be directly estimated by using:

$$ Sdg(A,B) = \frac{{card({G_A} \cap {G_B})}}{{card({G_A})}}. $$

For example: if user A is registered to 8 groups, user B to 4, and only 3 groups are in common, thus: Sdg()=3/8. The maximum value for Sdg() is obtained when both users are subscribed to the same groups, independently of their number. Sdg() is bounded from 0 to 1 and it is not symmetric.

1.5 Static metric on user’s interested taxonomy topics

In Mobile Medicine CNP, each element, and thus also Users and Content items, may be classified with a set of terms taken from the chosen medical taxonomy. In the case of Users, the taxonomical classification is performed during registration and represents the areas of interest and/or of competences of the User. In this sense, the taxonomical classification is very important to estimate the similarity between users, and also for estimating suggestions such as: CU, GU, etc.

The taxonomy is a structure in which descriptive terms are present along the structure. Each term may have one or more children and one or more parents; children are typically specializations of their parents. The taxonomy can be represented as graph in which the braches can be weighted on the basis of their distance from the root. A maximum distance has been fixed to D, and a weight of D/4 has been assigned to the first level children of the root. The next branches have been weighed dividing the weight of the first level children for the distance of the root.

The similarity distance between a couple of taxonomy terms is reported in matrix t[i][j], of TxT. The values of the matrix are generated on the basis of weights associated with branches along the minimal path (in term of the number of branches) between the two terms i,j:

$$ t[i][j] = \frac{{D - \sum\limits_{{k \in \min path\{ i,..,j\} }} {{W_k}} }}{D} $$

The estimation of this matrix accelerates the general computing of similarities since it avoids performing the single estimations for each comparison of Elements having the taxonomical classification: Users, Content, Groups.

Therefore, if element A has a taxonomical classification \( {T_A} = \left\{ {{r_{{A1}}},{r_{{A2}}}, \ldots, {r_{{AN}}}} \right\} \), where AN is the number of terms, and element B has \( {T_B} = \left\{ {{r_{{B1}}},{r_{{B2}}}, \ldots, {r_{{BN}}}} \right\} \), thus, each couple of taxonomical terms taken from T A , T B determines the couple i,j , and thus a value in matrix t[i][j]. Then, the value of similarity for elements A, B in terms of taxonomy is estimated by means of the following model:

$$ Sdt(A,B) = \frac{{\sum\limits_{{i \in {T_A}}} {{{\max }_{{j \in {T_B}}}}(t[i][j])} }}{{AN}}. $$

Each taxonomical term of element A is compared with respect to all the terms of element B. The maximum value among these similarities for each term of A is used to estimate an averaged value of similarity. Since each value of t[i][j] is limited to 1, also the averaged final value of Sdt() is bounded from 0 to 1, and it is not symmetric.

When content is uploaded and when a group is created a taxonomical classification is assigned. Thus, this static measure of similarity can be applied to estimate the similarity between Content and/or Group elements on the basis of their taxonomical classification.

Appendix B: main dynamic metrics

2.1 Dynamic metric on user’s interested taxonomy topics

In most cases, the taxonomical classification of positive actions is much more relevant than the static expression of interest in terms of taxonomy provided by the users, since they are in the 85% of cases not provided with the needed level of attention.

The taxonomical classification of Users, Contents, Groups acted by each specific User is a dynamic additional information representing its interested/preferred topics (played, marked, positively commented, recommend, etc.). Thus, a dynamic measure of similarity can be estimated keeping trace of taxonomy of all the elements touched during dynamic activities. To this end, in Mobile Medicine, a dynamic profile for the taxonomical classification is associated with each User (and Group) on the basis of positive actions dynamically performed on Content, and several counters for the different action types mentioned above. The distinct actions could be weighted in different manner or could create separate dynamic profiles without reducing the validity of the general model presented.

The dynamic taxonomical classification profile of an User (or Group) consists of vector dt[i] modeling each term i of the taxonomy with a function F(): dt[i] = F(taxonomy, i)/NOA, where NOA is the Number Of Actions performed on an element (e.g., Content), and T=Card(dt). Each element (e.g., Content) presents a static set of taxonomical terms identified by their corresponding indexes: te = {α, β,...}, they have been determined during its creation.

When an action is performed by a User (or Group) on an element (e.g., Content) the corresponding counter NOA is increased, and the vector of taxonomical terms dt[i] is updated according to the terms of taxonomy of the latter element te{} by using:

$$ d{t_{{(n + 1)}}}[i] = \frac{{(d{t_n}[i] \cdot NOA) + 1}}{{(NOA + 1)}},{\hbox{if}}\,i \in te\,otherwise\,by\,using:d{t_{{(n + 1)}}}[i] = \frac{{(d{t_n}[i] \cdot NOA)}}{{(NOA + 1)}} $$

So that the new version of vector dt[i] is produced as: \( d{t_{{(n + 1)}}}[i] \), where \( 0 \leqslant dt[i] \leqslant 1 \). dt[i] is the percentage of times the user has chosen an object associated with that taxonomy node.

Therefore, it is possible to estimate a similarity value between two elements (Users, Contents, Groups) on the basis of the above described dynamic taxonomical classification profile and the similarity distance matrix defined for Sdt().

If element A has a taxonomical classification dt A [i], and B has dt B [j]; thus, each couple of taxonomical terms determines the couple i,j (when dt A [i]≠0 and dt B [j]≠0), and thus a reference into matrix t[i][j], of TxT. The value of similarity for elements A, B in terms of taxonomy is estimated by means of the following model:

$$ Ddt(A,B) = \frac{{\sum\limits_{{i = 1}}^T {{{\max }_{{j = 1...T}}}\left( {t[i][j] \cdot (1 - |d{t_A}[i] - d{t_B}[j]|) \cdot d{t_A}[i] \cdot d{t_B}[j]} \right)} }}{{NDTANN}}. $$

Where NDTANN is the number of non-null items of dt A . Each taxonomical term of element A is compared with respect to all the terms of element B. The maximum value among these similarities for each term of A, weighted for the distance in the profile vectors, is used to estimate an averaged value of similarity. The value of Ddt() is bounded from 0 to 1 and it is not symmetric.

This model can be used to estimate the similarity distance between a static taxonomy profile and a dynamic taxonomic profile as needed in comparing terms of taxonomy of Content and Users, or of Groups and Content. Thus, in these hybrid (static dynamic) cases, the above reported distance takes the form as:

$$ Ddt\prime (C,U) = \frac{{\sum\limits_{{j = 1}}^T {{{\max }_{{i = 1...T}}}\left( {t[i][j] \cdot d{t_U}[j]} \right)} }}{{NDTUNN}} $$

Where NDTUNN is the number of non-null items of dt U .

2.2 Dynamic metric on user’s interested formats

In Mobile Medicine, or in others portals with communities, several symbolic descriptors may exist such as the content Format that may assume enumerate values. For example for the format: image, document, images, video, audio, etc. To know the preferences of the users on these formats may be very important to present ads or to estimate similarities and thus suggest such as U→U, C→U, and C→G.

Also in this case, a dynamic vector of preferences can be created, df[i], associated with the counting of the actions related to content having those formats. This allows understanding which are the preferred Formats for a certain User or Group. The vector df[i] is kept updated at each action with similar equations adopted for vector dt[i]. So that the new version of vector df[i] is produced as: df (n+1)[i], where \( 0 \leqslant df[i] \leqslant 1 \), and F=Card(df).

In order to estimate the similarity distance been formats a similarity model has been defined among the different values of Format. In this case, the model proposed in [4] and presented in Fig. 7 has been used. Thus, a similarity matrix f[i][j] of FxF has been produced reporting all distances among Formats values.

Therefore, similarly to the dynamic similarity between two elements, A,B, based on terms of taxonomy we have for the dynamic similarity between two elements (e.g., U→U) based on formats:

$$ Ddf(A,B) = \frac{{\sum\limits_{{i = 1}}^F {{{\max }_{{j = 1...F}}}\left( {f[i][j] \cdot (1 - |d{f_A}[i] - d{f_B}[j]|) \cdot d{f_A}[i] \cdot d{f_B}[j]} \right)} }}{{NDFANN}} $$

Where NDFANN is the number of non-null items of df A . This model can be used to estimate the similarity distance between a static format and a dynamic format profile as occur in comparing in terms of formats for Content and Users, or Groups and Content. Thus, the above reported distance is transformed as:

$$ Ddf\prime (C,U) = \frac{{\sum\limits_{{i = 1}}^F {\left( {f[i][Format(C)] \cdot d{f_U}[i]} \right)} }}{{NDFUNN}} $$

Where NDFUNN is the number of non-null items of df U .

2.3 Dynamic metric on user’s preferred content items and colleagues

The Users may typically marks as preferred some elements (Content, Users). In the case of Users they are referring to friends or colleagues. A similarity metric can be defined in order to weight the proximity of two Users/elements on the basis of their preferred elements. Thus, metrics similar to Sdg() (defined in section “Static metric on user’s groups”) have been adopted for the preferred content, Ddp(), and preferred/linked colleagues, Ddc(), by using the ratio between the number of elements they have in common and those of the reference User. Also in this case, the metric is bounded from 0 to 1 and it is not symmetric. Please note that metric Ddp() can be also applied to similarities between Users and Groups, and to Content and Groups.

  • Ddp(U,G): the ratio between the number of elements a User has in common with the Group and those of the reference User (the elements in commons are typically Contents).

  • Ddp(C,G): the ratio between the number of elements a Content has in common with the Group and those of the reference Content (the elements in commons are typically Users).

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Bellini, P., Bruno, I., Cenni, D. et al. Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices. Multimed Tools Appl 58, 41–79 (2012). https://doi.org/10.1007/s11042-010-0684-y

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