About 30% of persons over 65 are subject to at least one fall during a year. A number of published studies identify statistical relations between risk factors and the probability of fall in terms of odds ratios. In this paper we present a tool based on the representation of risk factors as odds ratios. Such representation is exploited to automatically build a computational logic probabilistic program, that in turn computes the fall risk of the subject given the presence/absence of risk factors in his/her status.
Epidemiological studies have shown in the past that almost 30% of people aged
65 or more are subject to an unintentional fall each year [
Usually, fall risk assessment tools focus on the presence or absence of fall
risk factors in the subject under evaluation. A huge literature is available on
risk factors, de ned as \aspects of personal behaviour or lifestyle, environmental
exposure, or inborn or inherited characteristic, which, on the basis of
epidemiological evidence, are known to be associated with falls". For an example of a
systematic review, see [
Within the European project Farseeing3 we are investigating new models
for assessing the fall risk of a subject. The approach we introduce in this work
aims to exploit the existing literature, and in particular the statistical results,
to directly compute the probability of a fall within a year as a consequence of
a patient exhibiting one or more risk factors. To this end, we introduced a light
classi cation of risk factors (a minimal ontology), and for each factor we take into
consideration the Odds Ratios (OR) published in the literature. Starting from
the ORs, we generate a Logic Program with Annotated Disjunction (LPAD,
[
The overall architecture of our tool is shown in Figure 1. The information about the risk factors is stored as an ontology expressed in the OWL language: from such knowledge base, a rst component computes probabilities (from odds ratios) and generates three LPAD rule sets containing higher and lower bounds (from the con dence intervals) and average odds ratios. Then, these LPADs and a subject pro le are fed to a Prolog engine extended to support also LPAD clauses: the subject pro le speci es which are the risk factors the subject is exposed to. The Prolog engine computes the higher, lower and average estimates of risk probability, that are returned to the user.
The Ontology. Risk factors are represented through a simple ontology, containing a list of all relevant risk factors and an odds ratio for each of them. The majority of risk factors cited in the scienti c literature can be classi ed into three di erent types, depending on how they contribute to the fall risk: a Dichotomic risk factors are the most common: either the risk factor is present or not; its contribution to the fall risk is xed, and depends only on the presence/absence of the risk factor. Typical examples of dichotomic risk factors are Parkinson or diabetes. b Scalar risk factors too are either present or not in the subject's pro le.
However, these risk factors are observed with a certain degree: usually with more than two possible \levels", starting from zero (absence of the factor) to 3 http://farseeingresearch.eu/ n (maximum magnitude of the risk factor). These risk factors contribute to the fall risk depending on their \level": a typical case is a linear contribution based on such level. Examples of this kind of factor are the age of the subject or the number of medications. c Synergy risk factors exist when more than one of risk factor of other type are present contemporaneously. Synergy risk factors capture the well known nature of sets of risk factors to be synergistic and produce an increment to the risk that is bigger than the one produced by the factors when considered independently.
The ontology stores also data about the con dence intervals of the odds ratio (typically at 95%). Having con dence intervals data permits to run three risk assessing algorithms: one using the lower extremes, one using the upper extremes and another with the average values; this may give the user a feeling of the uncertainty in quantitative knowledge about risk factors.
Moreover, the ontology contains also the estimators, i.e. the concepts that provide indication of a subject being exposed to a risk factor. Indeed, certain risk factors can be directly characterised: e.g., either a subject su ers the Parkinson disease or not, depending on a medical diagnosis. However, there is a number of risk factors that are determined on the basis of many di erent assessments and criteria: e.g. , the visual impairment is a known risk factor, whose presence in a subject is determined on di erent medical exams (often alternative exams), such as the visual acuity on a three meter distance, the visual stereognosis, or the contrast sensitivity of the subject.
Generated LPAD The generated LPADs are sets of rules that, depending on the risk factor type, are di erently de ned. Given the list of estimators L for a speci c subject, the rules are Prolog clauses of the form: where Pi is the probability associated to risk factor Ri. Estimators can be easily taken into account by introducing further conditions in the bodies of the rules. For example, in the case of the visual impairment, we model the di erent estimators with the following rule: fall(L) : 0.06 :- visionImpairment(L). visionImpairment(L) :- member(visualAcuity3M(X),L), X =< 5. visionImpairment(L) :- member(visualStereognosis(X),L), X =< 3. visionImpairment(L) :- member(contrastSensitivity(X),L), X =< 16. Subject pro le The subject pro le is a simple Prolog list of the estimators that characterise the subject. It has the form: [age(71), 'Parkinson', 'diabetes'], indicating that for example the subject has an age of 71, and she/he su ers diabetes and Parkinson diseases.
Moreover, the current version of our tool supports three di erent alternatives: { if the subject su ers a risk factor, such risk factor is listed in the pro le and it is labelled with a term true, and the risk probability associated to the factor directly contributes to the overall fall risk; { if the subject does not su er the risk factor, then the factor is not present in the list { if the subject is unsure about su ering a risk factor, then the factor is present in the list with a label \unknown": in that case speci c LPAD rules are used, and the distribution probability of the speci c risk factor over the population is used to compute the overall fall risk. 3
In this paper we have introduced a tool for assessing the fall risk depending
on a speci c subject pro le. Our architecture is still in a prototypical stage,
and many aspects still need to be researched and developed. At the moment of
writing, we are evaluating the quality of our tool by using a prototype based
on the statistical ndings in [
The current implementation is based on the ndings in the scienti c
literature. We plan as future work to apply learning algorithms, like
ExpectationMaximization, for parameters and structures of LPADs [
Acknowledgments This work is supported by the FARSEEING project, cofunded by the European Commission, Seventh Framework Programme, CooperationICT, Grant Agreement no. 288940.