i n t e r n a t i o n a l j o u r n a l o f m e d i c a l i n f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657

journa l homepage: www. int l .e lsev ierhea l th .com/ journa ls / i jmi

Viability study of a personalized and adaptiveknowledge-generation telehealthcare system fornephrology (NEFROTEL)

Manuel Pradoa,∗, Laura M. Roaa, Javier Reina-Tosinab

a Biomedical Engineering Group, University of Seville, Seville, Spainb Signal Theory and Communications Group, University of Seville, Seville, Spain

a r t i c l e i n f o

Article history:

Received 30 September 2005

Received in revised form 27 March

2006

Accepted 31 March 2006

Keywords:

Telehealthcare

Personalized knowledge generation

Nephrology

Adaptive falling detection

Physiological models

Multi-scale knowledge

a b s t r a c t

Objectives: Several important problems in the majority of countries are challenging the cen-

tralized and overburdened current model of healthcare. Telehealthcare is presented as a

new paradigm that offers high expectations to solve this picture. In this paper we present

the major outcomes of the viability study of a novel personalized telehealthcare system for

nephrology (NEFROTEL).

Methods: The study evaluates the accuracy and quality of the knowledge generated by two

key processing layers, namely, sensor layer and patient physiological image (PPI) layer, in an

independent way, thanks to its modular design. The first one was defined by a personalized

falling detection monitor, on account of the consequences of falls in chronic renal patients.

The second one was analyzed by means of a PPI’s prototype based on a urea compartmental

pharmacokinetic model. The experimental study of the falling detector monitor has been

more extensive than the other because the latter has already been addressed in other works.

Results: The outcomes show, firstly, the capability of the PPIs to provide integrated and cor-

related physiological knowledge adapted to each patient, and secondly, demonstrate the

reliability of the impact detection function of the adaptive human movement monitor com-

pliant with the NEFROTEL paradigm.

Conclusions: The study confirms that NEFROTEL is able to provide knowledge concerning a

patient in a manner that cannot be accomplished by the ordinary healthcare model at the

current model of healthcare [1–5].

present time.

1. Introduction

The growth of chronic pathologies such as diabetes mellitus,end stage renal disease (ESRD), hypertension, and cardiovas-

cular diseases, the aging of population and the associatedexpansion of degenerative disorders such Alzheimer orParkinson diseases, the change of social models and structure

∗ Corresponding author at: Grupo de Ingenierıa Biomedica, Escuela Sucubrimientos, s/n, 41092 Sevilla, Spain. Tel.: +34 954487399/637124960;

E-mail address: mpradovelasco@ieee.org (M. Prado).

1386-5056/$ – see front matter © 2006 Elsevier Ireland Ltd. All rights resdoi:10.1016/j.ijmedinf.2006.03.005

© 2006 Elsevier Ireland Ltd. All rights reserved.

of family, and the new threats coming from the acceleratedworldwide spreading of infectious diseases due to the impactof migration and population movements in a globalizedworld, are challenging the centralized and overburdened

perior de Ingenierıa, Universidad de Sevilla, Camino de los Des-fax: +34 954487340.

Telehealthcare is presented as a new paradigm that offershigh expectations to provide effective solutions to this picture.Telehealthcare systems pursue the improvement and decen-

erved.

a l i n

tspsf

ttv(ticbauesmabpb

rhdiwotpa

ithoabtblpecsstisbae

sfsgt

i n t e r n a t i o n a l j o u r n a l o f m e d i c

ralization of healthcare services, allowing the geographicaleparation between patient and physician. These goals haveushed the research in novel, non-invasive, and ubiquitousensors, multimedia services, and communications networks,rom a multidisciplinary perspective [6,7].

Telemedical information systems (TIS’s) are showingheir important role for an adequate implementation ofelemedicine applications, extending the telemedicine ser-ices for providing access to the health information systemHIS) and the electronic patient record (EPR) [8]. This is pushinghe emergency of a knowledge-based telehealthcare. However,n spite of the important role that medical knowledge dis-overy has acquired, as shows its inclusion into 1 of the 20uilding blocks identified by the Euromed project [9], as wells its consideration into several of the major HIS research linesp to now [10], insufficient efforts have been devoted to gen-rate useful medical knowledge in a real-time fashion fromignals and data. Advances in knowledge discovery have beenainly oriented to the detection of data patterns that could

ssist in medical diagnosis, but unfortunately, this line haseen aimed at the off-line processing of data. As an exam-le, we could cite the knowledge-based approach developedy Bousquet et al. for pharmacovigilance [11].

A recent review regarding the potential advantages andisks associated with health information technologies (HIT’s)as shown the huge investments performed in this areaespite the hardly advances in their adoption and in the

mprovement of patient outcomes [12]. It concludes that HITill be widespread adopted because of the inexorable growthf the health expenditure and the need to provide solutionso the healthcare challenges, but the question “how best toromote the adoption of HIT to transform healthcare” awaitsresponse [12].

In agreement with [9,10], we think that a plausible responses to promote an evolution in the central paradigm of HITowards a personalized and adaptive knowledge-based tele-ealthcare. Accordingly, we have developed a novel method-logical approach for generating real-time personalized anddaptive biomedical knowledge in telehealthcare [13,14]. Theiomedical knowledge is provided by means of a compu-ational image of the patient state focused on the desirediomedical domain (e.g. renal). It is built by a smart sensor

ayer and a patient physiological image (PPI) layer, being com-uted as distributed subsystems. Each layer generates knowl-dge from the input data, adapting to each user and his/herontext. This way the information generated in the smart sen-or layer is used by the PPI layer, which provides an advancedupervision of the patient. Our previous studies suggest thathis methodology is able to build an integrated biomedicalmage of the patient state and could help to predict events andystem malfunctions [13,14]. It also has the ability to integrateiomedical information belonging to different living scales, ingreement with current trends in system biology [15,16] andven in HIS [10].

This article presents the major outcomes of the viabilitytudy of a personalized and adaptive telehealthcare system

or nephrology (NEFROTEL), based on that methodology. Thetudy evaluates the accuracy and quality of the knowledgeenerated by both layers in an independent way, taking advan-age of its modular design. The first layer was defined by an

f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657 647

adaptive and personalized human monitor used for fallingdetection. This variable was selected on account of the seri-ous consequences that falls produce in patients with chronicrenal disease, due to their muscular loss, lack of D vitamin,and incidence of arthropathy by �2-microglobulin [1].

The second layer was built by means of a PPI’s prototypebased on a compartmental pharmacokinetic model. This isable to generate useful medical knowledge related to ESRDpatients submitted to periodic hemodialysis (HD) [17,18]. Thedescription and analysis of the smart falling detector is moreextensive than the PPI’s prototype, because some technicaldetails and study-cases regarding the PPI have already beenpresented [13,14].

2. Description of NEFROTEL

Fig. 1 shows a simplified block diagram of the computationalarchitecture of a NEFROTEL prototype (left), together with theprocessing stages that the biosignals follow after their acquisi-tion (right). The diagram emphasises the two processing layersinvolved in the real-time generation of personalized and adap-tive biomedical knowledge that characterizes the methodolog-ical approach of NEFROTEL.

This telehealthcare system is composed by three scenar-ios. The Remote Access Units (RAU’s) connect the assisted userto the provider center by means of a simple phone link tothe Public Switched Telephone Network (PSTN) [14]. This isthe minimum technical communication requirement of theRAU, which guarantees a nearly universal and inexpensiveaccess to the system [19]. The Professional Access Interfaces pro-vide to physicians and professional users different types ofaccesses to the system. The third scenario refers to the Tele-healthcare Provider Center. This subsystem was designed as amulti-tier architecture, in order to avoid the bottleneck of thedatabase management system (DBMS). Signals are sampledand processed by the smart sensors and sent through RAUsto the Provider Center, where they are processed taking intoaccount their priority. A Supervisory Control and Data Acqui-sition (SCADA)-based module performs a subsequent analysisand conditioning of this information, which is mainly storedin an object-relational database (PostgreSQL) and prepared tobe used by the PPI computational modules. The following sub-sections deepen the description of the key methodologicalfeatures of NEFROTEL and present the implementations jus-tified in Section 1, with the aim of testing the viability of thissystem.

2.1. Smart sensor layer

The sensor layer of NEFROTEL is complex, as it involves dif-ferent sensors, sample periods, and acquisition procedures,from real-time to manual inputs (e.g. pre- and post-dialysisblood samples). We present here a novel wearable humanmovement monitor (patented), which was developed accord-ing to the methodological approach of NEFROTEL, and is used

as a smart sensor. It measures body accelerations to com-pute postural and kinematic variables of the assisted user,as well as the energy expenditure caused by physical activ-ity. An interesting added value of this sensor is the detection

648 i n t e r n a t i o n a l j o u r n a l o f m e d i c a l i n f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657

Fig. 1 – Simplified block diagram of the NEFROTEL computational architecture (left) and the associated signal processingcks evaluated in this study have been shaded and connected by

diagram (right). The processing layers and the associated blo

oriented lines.

of falls, which represent a serious risk in chronic renal diseasepatients [1].

Its technical architecture is depicted in Fig. 2. The moni-tor is constituted by a wireless personal area network (WPAN)that integrates a personal server element (PSE), together withan intelligent accelerometer unit (IAU) and a personal router(PRT), as shown in the referred figure. This solution wasthought to solve many of the limitations existing in otherfalling detectors. The major technical details of the IAU designwere presented in [20], notwithstanding the following para-graphs give a comprehensive description.

The PSE is a small device worn on the wrist or as a pen-dant, which serves as a master of the WPAN, carries the userinterface, and processes the measurements sampled and pre-processed by the IAU in real-time. In turn, the latter acts asa slave device in the WPAN and was designed to be wornas a skin patch located at the user back, near the sacrum.The IAU performs the first analysis of the signals sampledat a frequency higher than that necessary to compute kine-matic and postural parameters. This processing is executedwith the aim of detecting signal properties that suggest theoccurrence of impact events and postural transitions. Plausi-

ble detected events and signal properties together with theacceleration signals, filtered and re-sampled at a lower fre-quency, are sent towards the PSE subsequently. This devicecompletes the real-time processing of acceleration signals.

Fig. 2 – Block diagram showing the major components ofthe human movement monitor (inside circle).

a l i n

Tfoccl

wiiitr

2

FtieclPaitp

idi

ioatiwpg

i n t e r n a t i o n a l j o u r n a l o f m e d i c

herefore, this monitor is able to generate useful knowledgerom the acceleration measurements that it performs. More-ver this knowledge discovery will be adapted to the user andontext, in agreement with the NEFROTEL methodology. Thisustomization and adaptive capacity will be clarified and ana-yzed in the viability study presented in this article.

Finally, the PSE manages the communication between theearable monitor and the RAU. More technical details regard-

ng the communication links are described in [19,21]. The PRTs another slave device that guarantees the full-time monitor-ng of the user, by connecting the monitor to a Provider Centerhrough a mobile telephone local link. More technical detailsegarding the IAU are presented in Appendix A.

.2. PPI layer

ig. 3 outlines a block diagram of a PPI. This is a computa-ional component composed of a mathematical model, annterface block that provides connection to data and otherlements, and an execution block that is responsible for theontrol of the simulation of the mathematical model. Theatter supports the real-time knowledge generation of thePI, using the input variables provided by the sensor layer,nd generating a dynamic representation of the physiolog-cal and biomechanical state of the patient associated withhe PPI, according to the needs of supervision defined by thehysicians.

A detailed technological description of the PPI appearsn [14]. The modelling methodology used in the PPI is alsoescribed in [14], and it can be examined with more detail

n [22,23].Fig. 4 illustrates the different hierarchical levels of a phys-

ological model that can be used to describe the distributionf chemical compounds into the human body by means ofPPI. Fig. 4(a) shows the iconic diagram associated with a

hree-pool pharmacokinetic model representing the vascular,

nterstitial, and cellular human compartments. This modelas built connecting mathematical virtual components from aharmacokinetic library developed previously by our researchroup [24]. Variables Vv, Vi and Vc represent the volume of the

Fig. 3 – Simplified block diagram of a PPI.

f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657 649

vascular, interstitial, and cellular compartments, respectively,while Mv and Mc are the vascular and cellular membranes.

Pharmacokinetic models can be considered as simplifiedphysiological models [25]. This concept is used in Fig. 4(b),which shows how the pharmacokinetic model Fig. 4(a) isextended to consider several anatomical regions, as liver, kid-neys, and other ones (including the muscular system), asnecessary, aggregating and parameterizing the previous vir-tual modules with the modelling language used in NEFROTEL(EcosimPro language or EL) [14]. This kind of language allowsthe integration of knowledge pertaining to different livingscales. This is shown in Fig. 4(c), presenting a nephron that cansubstitute the compartments describing the renal system inthe upper hierarchy level. A detailed description of the appliedmethodology exceeds the scope of this work.

Fig. 4(d) and (e) continue going down in the living scaleby presenting an EL-based diagram that describes the over-all hydraulic permeability of the membrane of the kidneycollecting duct and a causal diagram showing its major reg-ulation mechanisms. The hydraulic permeability is governedby diffusion through channels (mainly intercellular) sharedwith low molecular weight compounds (Lp), and by diffusionthrough specific water-channels (L∗

p). The specific channels areassociated with aquaporins (AQP) proteins [26]. The plausiblerelationship between AQP2 channels in the kidney collectingduct cells and the evolution of renal disease in patients withdiabetes mellitus, which is the first cause of ESRD in industri-alized countries, justifies their consideration.

The parameter Lp depends directly on the patient, whereasthe patient dependency of L∗

p occurs through the cellular, pro-teomic, and genomic regulation mechanisms.

Fig. 4 also shows some input variables used by the PPI’smathematical model in NEFROTEL for adapting the model toeach particular patient. It is not a requirement that these vari-ables are sampled in real-time, although the sampling instantmust be known. The energy expenditure, EE, due to physicalactivity can be measured by the IAU in a real-time mode, inagreement with previous works [27]. EE is interesting for thePPI given its relation with glucose and nitrogen metabolism.This issue is particularly significant due to the high percentageof diabetic mellitus patients into the population with chronicrenal disease [28].

3. Materials and methods

3.1. Sensor layer

The study has been designed to validate the reliability of theIAU for detecting human body shocks (impacts), as well as theinfluence of the subject (personalization) and context (type offloor) on this detection. It is important to make a distinctionbetween falling detection and shock detection. The majority offalls occur with body shock, and the latter is the responsible formany of the serious health consequences, such as bone frac-tures. On the other hand, the kinematics and biomechanics

of a fall are very complex. For example, many types of humanactivities involving collision with ground have a bimodal pat-tern, which is characterized by an impact force peak and abraking force peak [29]. The first impact seems to be passive,

650 i n t e r n a t i o n a l j o u r n a l o f m e d i c a l i n f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657

Fig. 4 – Iconic diagrams of human physiology mathematical models used to describe the distribution of drugs and fluids.Diagrams show the evolution of a pharmacokinetic compartmental model (a) towards a more complete physiological model(b) which considers three types of anatomical regions, as well as the vascular compartment. Diagram (e) shows the majorregulation mechanisms of L∗

p at genomic (solid arrows), proteomic (dotted arrows) and cellular level (dashed-dot arrows).Nomenclature: vasopressin (VP), cyclic adenosine mono phosphate (cAMP), protein kinase (PKA), total AQP2 (TAQP2),hematocrit (HTO), plasma protein concentration (PP), blood urea nitrogen concentration (BUN), energy expenditure (EE).

a l i n

wtbi

idcattPw

eitn(ota

1

2

ift

l

weaEeaoAaagtitawpr

i n t e r n a t i o n a l j o u r n a l o f m e d i c

hereas the second impact might be attributed to the con-raction of the musculature to decelerate the motion of theody. The type of fall arrest has a strong influence on the force

mpact and the joint kinematics.The falling detection function of our movement monitor

s based on its distributed processing capability, which wasescribed in Section 2.1. When the IAU detects an impact, itommunicates this event to the PSE, which performs a deepernalysis of the acceleration signals with the aim of studyinghe kinematics and dynamics pattern of the subject aroundhe event. This approach optimizes the processing load of theSE, which has been designed to attend other smart sensorsithin the WPAN.

Accordingly, we have designed a laboratory study thatvaluates the goodness of the IAU on the detection of bodympacts. The study was carried out over eight healthy volun-aries, which performed five normal physical activities (trueegative impact) and three physical activities involving impact

true positive impact). The impacts were executed in two typesf floor, hard and soft (thin mat), with the aim of evaluatinghe influence of the ground (context). The physical activitiesre defined and classified as follows:

. True negative impacts. Slow walking (l1), normal walking (l2),fast walking (l3), going upstairs (l4), and going downstairs(l5).

. True positive impacts. Vertical jump, knee falling, and hori-zontal falling from a low bank (50 cm) on hard floor (l6, l7,l8), and the same activities on soft floor (l9, l10, l11).

The IAU laboratory prototype was worn at the user back,n the placement previously defined (Section 2.1), but it wasastened by means of a belt, because its size was higher thanhe industrial and final version.

The algorithm for impact detection is defined by the fol-owing equations:

h =4∑

i=1

(afiefi), afi = [|ai| > A](t, t + th),

efi = [EAC,i > E](t, t + th), (1)

here afi and efi are binary flags that are activated (i.e.qualled true) if the logical conditions given by the secondnd third equations of (1) are verified, that is, if |ai| > A and

AC,i > E, respectively, being |ai| the absolute value of the accel-ration signal, and EAC,i the energy of the acceleration signalfter removing the dc component, in the i-axis. The numberf axes and other technical details are explained in Appendix. The parameters A and E are adjustable thresholds. Themplitude and energy flags, afi and efi, respectively, are keptctivated during the time th after the inequality conditionso down to 0 (false), where th is a value much greater thanhe acceleration sampling period. This temporal extensions denoted by the parenthesis (t, t + th). The sum and mul-iplication operators of the first equation in (1) refer to OR

nd AND logical operations, respectively, since they operateith logical variables. The output variable, h, is the impactrediction of the algorithm, in such a way that h = 1 (true)efers to a detected positive impact (positive prediction), and

f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657 651

h = 0 (false) refers to a detected negative impact (negativeprediction).

The energy of the acceleration for the axis i, EAC,i, iscalculated for a �-width temporal window, by the followingequation:

EF,i(n) = EF,i(n − 1) + |aF,i(n)| − |aF,i(n − �)| (2)

where aF,i is the i-axis acceleration signal filtered by a dcsuppressor and n is the current sampling instant. We usethe absolute value of the signal instead of the squared valuebecause the first one reduces the computational load withoutaffecting the performance. The dc suppressor filter was imple-mented by a FIR filter defined by the following z-transform:

H(z) = 12

(1 − z−1). (3)

This FIR filter was selected because of the small require-ments in memory and processing load. The algorithm (1) isan evolution of that presented in [30], which reduces the pro-cessing load. The previous algorithm was implemented intothe IAU’s prototype and tested for verifying the reliability,low power consumption, and accuracy, using a sampling timeof 27.5 ms [30]. Under those conditions the IAU operated inlow power consumption (sleep mode) more than 65% of thetime. In the present study, the sampling time was increasedto 82.5 ms, and the energy variable was computed each 0.4 s,a value close to the width of the temporal windows, � = 0.66 s.This relaxation in the temporal specifications pursues veri-fying the reliability of the IAU under worse conditions thanprevious studies. The temporal extension, th, was set to 1.76 s,as in previous studies [30]. This value is similar to the temporalinterval that characterizes the kinematics events of a fallingevent.

The present study was organized in two stages. In the firstone, the IAU sampled the accelerations, calculated the asso-ciated energies during the execution of the physical activitiesby each subject, and transmitted them through a wireless linktowards a personal computer. The physical activities were car-ried out by the subjects without excessive limitations on theirway of walking or moving up/downstairs, and with no restric-tion regarding the clothes.

We define TP as the number of positives correctly detected,FP as the number of positives incorrectly detected, P as thenumber of true positives, and N as the number of true nega-tives. The goodness of the impact detector was evaluated cal-culating the true positive rate, tpr = TP/P, and the false positiverate, fpr = FP/N, of the algorithm given by Eq. (1) for differentamplitudes and energy thresholds, A, and E, respectively.

We formally transformed the two-dimension parametricspace (A, E) to the receiver operating characteristics (ROC)space, given by the 2-tuple (fpr, tpr) [31]. The major advan-tage of this method is due to the equality between the areaunder the ROC curve (AUC) and the probability that thealgorithm assigns distinguishable internal scores to negative(non-impact) and positive (impact) physical activities. Another

advantage is the clear representation of activities properlyclassified (tpr).

We have defined three different scenarios to evaluate thegoodness of the IAU impact detection function. In the first one,

652 i n t e r n a t i o n a l j o u r n a l o f m e d i c a l i n f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657

Fig. 5 – Set of ROC points (left diagram) and upper envelope curves (both diagrams) for the two first sceneries or operatingmodes. The right diagram shows the envelope curves related to the hard floor (dashed line), soft floor (dash-dot line) andtheir average (solid line). The equi-spaced isolines of the objective functions for each scenery (solid and labelled lines),

linesthe

including the isolines associated with the optimum (dottedoptimum operating points are marked by filled squares over

the algorithm operated in a non-personalized mode, apply-ing the same parameters (A, E) for all of the subjects andtypes of floor. Under this mode, each point in the ROC spacewas obtained calculating tpr and fpr for the 88 experimentalinstances (8 subjects × 11 activities). In the second mode thealgorithm distinguished the type of floor where the impactoccurred. This way, two different sets of ROC points wereobtained. Each one was associated with 64 instances (8 sub-jects × [5 + 3] activities). In the third scenario the algorithmoperated in a personalized mode, adapted to each subject andto the type of floor where the impact occurred. This modeproduced 16 (8 × 2) sets of ROC points, where each point wascalculated through eight instances (5 + 3 activities).

We present the ROC space diagram obtained for eachimpact detector operating mode, together with the AUC of theupper envelope curve of each set of ROC points. The AUC of theaverage upper envelope curve for the total sets of ROC pointsis also provided for the last two operating modes. This way, wecan assign an AUC value to each operating mode. However, thecomparison among the three different strategies of detection

is not reduced to these three numbers.

Finally, it is necessary to relate the set of ROC points tothe operating point selected as the optimum by the moni-tor, and to the way in which the associated thresholds (A,

Table 1 – Major anthropometric measurements of the subjectsmaximize the objective function F, and the AUC of the envelopthe IAU operates in a personalized mode

Subjects Gender W H Y

1 F 60 1.63 252 F 56 1.54 313 F 53 1.63 264 M 86 1.77 275 M 72 1.83 266 M 83 1.79 237 M 60 1.67 238 F 49 1.57 26

Variables W, H and Y are in kg, m, and years, respectively.

near the upper left corners), are also presented. TheROC envelope curves.

E) are obtained. Although a detailed explanation of this taskexceeds the scope of this work, we present here the isolinesassociated with the objective function that was used for opti-mizing the IAU in previous works [30], as well as the point(fpr, tpr) that maximizes the function. We also indicate brieflyother strategies. The cited objective function is given by Eq. (4),which rewards the successes and penalizes the failures. Thisfunction is maximum at (fpr, tpr) = (0, 1) and minimum at (fpr,tpr) = (1, 0), as expected

F = N(3tpr − 1) − Pfpr. (4)

3.2. PPI layer

We selected a PPI based on a three-pool variable volume ureakinetic model, representing the vascular, interstitial and cel-lular compartments of the patient, because of the importanceof urea kinetic in the adequacy of hemodialysis [17,18]. Thismodel is represented in the Fig. 4(a). The vascular compart-ment is connected to a dialyzer as indicated. This model is

called 3pUKM. We have not used a more complex physiologi-cal model such as that indicated in Fig. 4(b) because that taskexceeds the scope of this work, and the viability of the testedmethodology can be evaluated by means of the easiest model.

under study, together with the values (fpr*, tpr*) thate ROC curve, for each type of floor (1, hard; 2, soft), when

AUC1 (fpr*, tpr*)1 AUC2 (fpr*, tpr*)2

0.7000 (0.60, 1) 0.8000 (0.60, 1)1.0000 (0, 1) 0.9667 (0.20, 1)0.9000 (0.40, 1) 0.9000 (0.40, 1)1.0000 (0, 1) 0.9667 (0.20, 1)0.8000 (0.40, 1) 0.7000 (0.40, 1)0.9667 (0.20, 1) 1.0000 (0, 1)1.0000 (0, 1) 0.9333 (0.20, 1)1.0000 (0, 1) 0.9000 (0.40, 1)

a l i n

gstftbtds

a(c

Fphite

i n t e r n a t i o n a l j o u r n a l o f m e d i c

We evaluated the capability of the 3pUKM-based PPI forenerating useful biomedical knowledge during the HD ses-ion of four randomly selected ESRD stable patients submit-ed to thrice-weekly HD. We selected the Wednesday sessionor the measurements, which included blood urea concen-ration, plasma protein concentration, and hematocrit. Thelood samples were extracted at the beginning of the HD, athe end, and 30 min after the end, following standard proce-ures. The operating conditions were kept constant duringessions.

These measurements were used to personalize each PPI,s well as to adjust a reference two-pool urea kinetic model2pUKM) whose accuracy on the prediction of the blood ureaoncentration had been previously validated with success [32].

ig. 6 – Upper envelope curves of the ROC points associated withersonalized mode (third scenario) for the eight subjects of the sard floor (dashed line), soft floor (dash-dot line), and their avera

solines of the objective functions (solid and labelled lines) and the upper left corners) are also presented. The optimum operatinnvelope curves. See the text for a more detailed explanation.

f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657 653

The accuracy of the 3pUKM-based PPIs is quantified by meansof the difference between the urea kinetic indices, dKt/V, pre-dicted by the 3pUKM-based PPI and the 2pUKM. The indexdKt/V is defined as KT/V, being K the dialyzer effective ureaclearance, T the HD session duration, and V the urea distri-bution volume. More details regarding the 3pUKM and themethod used for adjusting both the PPI and the 2pUKM canbe seen anywhere [33,34].

4. Results

Fig. 5 shows the ROC spaces of the impact detection func-tion for the laboratory study carried out over the eight healthy

the impact detection function of the IAU operating in atudy. Diagrams present the envelope curves related to thege for all of the subjects (solid line). The equi-spaced

he isolines associated with the optimum (dotted lines nearg points are marked by filled squares over the ROC

i c

654 i n t e r n a t i o n a l j o u r n a l o f m e d

voluntaries, when the IAU operates without personalization.The left diagram refers to the first scenario (no adaptation),whereas the right one applies to the second scenario (flooradaptation). In the first case we have presented both theupper envelope curve and the set of ROC points, in orderto clarify the definition of envelope that has been applied.The area under the ROC curve (AUC) shown in the left dia-gram was 0.8760, whereas the AUC for the average ROCcurve presented in the right diagram was 0.8766 (hard floorAUC 0.8979, soft floor AUC 0.8552). The objective functionreaches an optimum value of 58.40 for the first case, and69.20, and 70.40, for hard and soft floor in the second case,respectively.

The optimum true positive rates (tpr*), according to theobjective function F defined in (4) were 1 in both scenarios,whereas the optimum false positive rates (fpr*) were 0.45, forthe first one, and 0.45, and 0.40 (hard and soft floor, respec-tively) for the second. The optimum points are marked by filledsquares in both diagrams. Both squares of the second diagramare overlapped.

These outcomes suggest a small improvement in the capa-bility of the impact detector when the type of floor is takeninto account. Certainly, the improvement is much more evi-dent in the case of hard floor. This issue will be discussedbelow.

Table 1 shows the AUC and optimum rates (fpr*, tpr*), foreach subject and type of floor of the study, when the IAU oper-ates in a personalized mode (third scenario). The AUC of theaverage envelope ROC curve for this case was 0.9083, whichclearly improves the average AUC’s obtained in the two previ-ous scenarios.

The ROC diagrams of the personalized operating mode ofthe IAU are shown in Fig. 6. These diagrams present simi-lar information to be presented in Fig. 5, excepting the aver-age envelope ROC curve, which now refers to the average ofthe envelope ROC curves of all subjects and type of floors.The average fpr* for this mode of operation is 0.23, whichclearly improves the minimum fpr* obtained in the two pre-vious scenarios (0.40), keeping tpr* = 1 in all cases. In agree-ment with the results of the second scenario, the average hardfloor AUC (0.9208) was greater than the average soft floor AUC(0.8958).

These outcomes show the influence of the subject andenvironment in the impact detection, supporting the person-alized and adaptive knowledge-generation methodology ofNEFROTEL. Several relevant issues concerning the study defi-nition are discussed in the following section.

Regarding the capability of the 3pUKM-based PPI forgenerating accurate biomedical knowledge, the differencebetween dKt/V values predicted by the 3pUKM-based PPIand the 2pUKM, for the HD sessions delivered to the fourESRD patients of the study was (mean ± S.D.) −1.11 ± 1.88%.We tested that the difference between extracellular ureaconcentrations predicted by 3pUKM and 2pUKM was neg-ligible. PPIs allowed observing the behaviour of the inter-stice as a buffer during the HD sessions. This compartment

helps to moderate the loss of vascular volume and there-fore the risk of hypotension events during HD [35,36]. Moredetails regarding the functionality of the PPI were studied in[13,14].

a l i n f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657

5. Discussion and conclusions

The objective of this study has been the evaluation of thefeasibility and advantages of a new methodology for tele-healthcare. This can be achieved by means of experimen-tal studies carried out over prototypes designed accordingto that methodology. We have proceeded modularly, takingadvantage of the degree of autonomy of sensor and PPI lay-ers. This is not a study of evaluation of information tech-nology in healthcare, and thus we have not had the type ofproblems claimed by many authors regarding the complex-ity of the evaluation project or the motivation for evaluation[37].

The necessity of a methodology for telehealthcare that pro-vides an advanced supervision of chronic renal patients wasjustified in a previous work [13]. The personalized and adap-tive knowledge-based methodology conceived as a responseto the state of healthcare in nephrology provides also interest-ing functions for other chronic pathologies such as diabetes, orpopulation groups such as the elderly. A detailed explanationof the functions of a technological solution for this type of tele-healthcare system appears in [14]. The mathematical modelsconstitute the kernel of this methodology, because they allowbuilding real-time integrative and dynamic knowledge regard-ing physiological and biomechanical aspects of patients, andalso of physical processes of artificial therapy machines suchas dialyzers.

Our methodology shares some concepts with the OrgAheadcomputational modelling program [38], which is devoted toimprove patient care unit safety and the quality of outcomes,providing “what-if” functions into a kind of virtual scenario.However, NEFROTEL presents differences with OrgAhead inthe objective (organizational in OrgAhead versus health-stateof patients in NEFROTEL), and in the nature, methodology,and technology of the mathematical models used to provideknowledge. Also, NEFROTEL shares some of the objectivesof physiology simulation projects based on knowledge net-working such as the Physiome project [15], allowing multi-scale knowledge integration. This particular feature has beendescribed in Section 2.

The study regarding the feasibility of the PPI layer has a pre-liminary scope in this paper, given its limitation to only fourESRD patients. However, relevant technological and method-ological issues concerning the PPI layer have been validatedsuccessfully in preliminary works [13,14]. A detailed techni-cal description of the PPI and the 3pUKM model has beenavoided because of the excessive length, but it can be foundin [13,14,34]. The study has also been limited to the easiestmodel of Fig. 4(a) by similar reasons, despite the capability ofNEFROTEL for integrating multiscale knowledge is an impor-tant methodological feature.

Anyway, the outcomes presented here suggest the capa-bility of the PPI layer to be personalized and to providereal-time, integrated, and correlated knowledge regarding thecompartmental urea kinetic of renal patients. This knowl-

edge can be extended according to the analysis of Section 2(see Fig. 4).

The 3pUKM-based PPI also shows the evolution of thepatient compartmental volumes [34], confirming the impor-

l i n

ttraetcmcowmbicvfifi[

ddtgttcttS

ihafft

tvjtoe(i3ihsmc2

h(rdeu

r

i n t e r n a t i o n a l j o u r n a l o f m e d i c a

ance of the interstice to moderate the blood volume reduc-ion due to ultrafiltration during HD, and thus reducing theisk of hypotension. This is a frequent complication thatppears in 30% of all HD treatments, and although the gen-sis of this problem is multifactorial, hypovolemia seemshe major responsible mechanism [35]. Although there areurrently different procedures that predict this event byeans of the on-line measurement of HTO [39,40], the PPI

ould provide a more powerful solution thanks to the usef the real-time HTO measurement as an input variable,hich allows the adaptation and personalization of the PPI’sathematical model. A personalized PPI can present the

lood volume correlated with other physiological variables,n such a way that it can assist the physician regarding theauses that originate a more abrupt depletion of the plasmaolume, for example, or allowing a better control of the ultra-ltration velocity in order to avoid the occurrence of back-ltration due to an excessive low transmembrane pressure

41].The experimental study regarding the sensor layer has

emonstrated in first place the reliability of the IAU’s impactetection function of our novel human movement moni-or. The AUC of the upper envelope ROC curve was close orreater than 0.9 in all scenarios. It is noteworthy the facthat the optimum operating point, associated with the objec-ive function F, detected all impact events (tpr* = 1) in allases. This is an important mission of the IAU, according tohe distributed processing architecture of the monitor (Sec-ion 2), and the methodology of falling detection exposed inection 3.1.

Secondly, the outcomes have proved the possibility ofmproving the quality of the detection function if the sensoras the capability to personalize its operation to the subjectnd the environment. The false impact rate (fpr) was reducedrom 45% to 23% (average) from scenario 1 (equal thresholdsor all subjects and types of floor) to scenario 3 (personalizedhresholds).

The study was designed so as to let the smart sensor proto-ype work under conditions worse than those of the industrialersion. This way, the type of attachment of the IAU to the sub-ect body and the stiffness of the accelerometers assembly intohe IAU prototype introduced a level of noise higher than thene expected in the industrial device. Sampling times of accel-rations and intervals for energy computation were greaterworse) than other ones that we have already tested to ver-fy power consumption and reliability of the monitor (Section.1) [30]. The detection of impacts on soft floor by compar-ng with normal activities on hard floor supposed also a veryard condition in this study, because the difference amongignal energies of positive and negative impact activities isuch more reduced. This difficulty explains the better out-

omes of the IAU on hard floor than in soft floor (scenariosand 3).

The personalized mode of operation is related to the wayow the optimum is defined and reached. The optimum values

fpr*, tpr*) for each subject and ground that appear in Table 1

efer to the objective function F, whose isolines have beenrawn in Fig. 6. The optimum F-isolines are tangent to thenvelope ROC curve, as Fig. 6 shows (dotted lines near thepper left corners). Notwithstanding, it is possible to define

f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657 655

the optimum by means of different paths or adaptive pro-cesses. Fig. 6 shows a possible adaptive process that beginsfrom the more “liberal” (lowest thresholds) point in the ROCspace, marked by a filled circle in the upper right corner, andadvances towards the optimal by a learning process. A detaileddescription of this process exceeds the scope of this article,although it can be easily understood as a procedure aimed atincreasing the threshold values provided the true positive rateis equal to 1. This procedure can be implemented thanks to thecomputational architecture of the monitor (Section 2.1).

Much research has been performed during the last years fordeveloping reliable falling detectors. Some interesting papersrelated to this issue can be cited [27,42–46]. However, to thebest of our knowledge, none of the published detector moni-tors use the methodology evaluated in this work.

In summary, our outcomes suggest the viability of NEFRO-TEL as a personalized knowledge-generation telehealthcarefor nephrology.

Acknowledgments

This work was partly supported by the Spanish NationalBoard of Biomedical Research (Instituto de Salud Carlos III,Fondo de Investigacion Sanitaria), under Grants 01/0072-01and PI040687, and by the Direccion General de Investigacion,Tecnologıa y Empresa under Grant TIC 314. We are grateful toDr. A. Palma and Dr. J.A. Milan for their clinical support anduseful comments.

Appendix A. Appendix

This section describes very briefly several technical aspectsof the IAU prototype used in this work. It is based on amicrocontroller PIC16LC66 from MicrochipTM, operating at 3 V,with 4 MHz of main frequency clock and an active consump-tion current IDD = 0.6 mA (XT clock mode). The device includestwo biaxial capacitive accelerometers ADXL202E from AnalogDevicesTM, with a range equal to ±2g. The measurements aredecoded from their pulse width modulated (PWM) digital out-puts. The accelerometers are mounted in such a way that threeacceleration axes, x1 (vertical), x2 (bisector) and x3 (horizon-tal) pertain to the sagittal plane, and the fourth one, x4, is asecond horizontal axis, perpendicular to the sagittal plane.

e f e r e n c e s

[1] R.J.A. Sims, M.J.D. Cassidy, T. Masud, The increasingnumber of older patients with renal disease, BMJ 327(7413) (2003) 463–464.

[2] M.S. Parmar, Chronic renal disease, BMJ 325 (7355) (2002)85–90.

[3] C.G. Swift, Care of older people: falls in late life and their

consequences-implementing effective services, BMJ 322(7290) (2001) 855–857.

[4] L. Hufnagel, D. Brockmann, T. Geisel, Forecast and controlof epidemics in a globalized world, PNAS 101 (42) (2004)15124–15129.

a l

656 i n t e r n a t i o n a l j o u r n a l o f m e d i c

[5] The world health report 2003—shaping the future, ReportNo.: 2003/15424, Sadag, 20000, World Health Organization,France, 2003.

[6] R. Wootton, Recent advances: telemedicine, BMJ 323 (7312)(2001) 557–560.

[7] A.B. Suleiman, The untapped potential of telehealth, Int. J.Med. Informatics 61 (2–3) (2001) 103–112.

[8] A. Horsch, T. Balbach, Telemedical Information Systems,IEEE Trans. Inf. Technol. Biomed. 3 (3) (1999) 166–175.

[9] A. Marsh, The Creation of a global telemedical informationsociety, Int. J. Med. Informatics 49 (2) (1998) 173–193.

[10] R. Haux, Health Information Systems—past, present,future, Int. J. Med. Informatics 75 (2006) 268–281.

[11] C. Bousquet, C. Henegar, A.L.-L. Louet, P. Degoulet, M.-C.Jaulent, Implementation of automated signal generation inpharmacovigilance using a knowledge-based approach,Int. J. Med. Informatics 74 (7–8) (2005) 563–571.

[12] P.G. Goldschmidt, HIT and MIS: implications of healthinformation technology and Medical Information Systems,Commun. ACM 48 (10) (2005) 68–74.

[13] M. Prado, L. Roa, J. Reina-Tosina, A. Palma, J.A. Milan,Renal telehealthcare system based on a patientphysiological image: a novel hybrid approach intelemedicine, Telemed. J. e-Health 9 (2) (2003) 149–165.

[14] M. Prado, L. Roa, J. Reina-Tosina, A. Palma, J.A. Milan,Virtual center for renal support: technological approach topatient physiological image, IEEE Trans. Biomed. Eng. 49 (12)(2002) 1420–1430.

[15] J.B. Bassingthwaighte, Strategies for the physiome project,Ann. Biomed. Eng. 28 (2000) 1043–1058.

[16] H. Kitano, Systems biology: a brief overview, Science 295(2002) 1662–1664.

[17] F.A. Gotch, J.A. Sargent, M.L. Keen, Whither goest Kt/V?Kidney Int. 58 (Suppl. 76) (2000) S3–S18.

[18] L.M. Roa, M. Prado, The role of urea kinetic modeling inassessing the adequacy of dialysis, Crit. Rev. Biomed. Eng.32 (5–6) (2004) 461–539.

[19] J. Reina-Tosina, G. Sanchez, L. Roa, M. Prado, Cost-effectivedeployment of communication resources for a HomeHealth Telecare System for the elderly, in: Proceedings ofthe 25th Annual International Conference of the IEEE,Cancun, Mexico, 17–21 September, 2003, pp. 1372–1375.

[20] M. Prado, J. Reina-Tosina, L. Roa, Distributed intelligentarchitecture for falling detection and physical activityanalysis in the elderly, in: Proceedings of the 24th AnnualInternational Conference of the IEEE-EMBS and AnnualFall Meeting of the BMES, Houston, TX, USA, October23–26, 2002, pp. 1910–1911.

[21] G. Sanchez, J. Reina-Tosina, L.M. Roa, M. Prado, A. Dıaz,Design and evaluation of communications protocols for aHome Health Telecare System, in: X Mediterr. Conf. onMed. and Biol. Eng. and Comp (Medicon 2004) and 2ndHealth Telematic Conference; 2004; Ischia, Naples, Italy,2004.

[22] M. Tiller, Introduction to Physical Modeling with Modelica,Kluwer Academic Publishers, Boston, 2001.

[23] L. Roa, M. Prado, Simulation Languages. in: M. Akay (Ed.),Wiley Encyclopedia of Biomedical Engineering, John Wileyand Sons, Inc., p. 4152, ISBN: 0-471-24967-X.

[24] M. Prado, L.M. Roa, Virtual modeling of compartmentalpharmacokinetic systems, in: Proceedings of the 27thAnnual International Conference of the IEEE Engineeringin Medicine and Biology Society, Shanghai, China,

September 1–4, 2005.

[25] J.D. Berrozpe, J.M. Lanao, J.M.P. Delfina (Eds.), Biofarmacia yFarmacocinetica (Biopharmacy and Pharmacokinetic), 1sted., Editorial Sintesis, Madrid (Spain), 1997.

i n f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657

[26] S. Sasaki, K. Ishibashi, F. Marumo, Aquaporin-2 and -3:representatives of two subgroups of the aquaporin familycolocalized in the kidney collecting duct, Annu. Rev.Physiol. 60 (1998) 199–220.

[27] C.V.C. Bouten, K.T.M. Koekkoek, M. Verduin, R. Kodde, J.D.Janssen, A triaxial accelerometer and portable dataprocessing unit for the assessment of daily physicalactivity, IEEE Trans. Biomed. Eng. 44 (3) (1997) 136–147.

[28] U.S. Renal Data System, USRDS, Annual Data Report: Atlasof End-Stage Renal Disease in the United States, NationalInstitute of Health, National Institute of Diabetes andDigestive and Kidney Diseases, Bethesda, MD, 2003.

[29] K.-J. Kim, J.A. Ashton-Miller, Biomechanics of fall arrestusing the upper extremity: age differences, Clin. Biomech.18 (4) (2003) 311–318.

[30] M. Prado, L.M. Roa, J. Reina-Tosina, M. Rovayo, Wearablehuman movement monitoring device aimed at providingpersonalized support in healthcare, in: Microtechnologiesfor the New Millenium, Smart Sensors, Actuators, andMEMS II, 2005 9-11 May, Sevilla, Spain, 2005.

[31] T. Fawcett, ROC graphs: notes and practical considerationsfor researchers, Report No.: Technical report, HPLaboratories, Page Mill Road, Palo Alto, CA 94304, USA,2004.

[32] B. Canaud, J.Y. Bosc, L. Cabrol, H. Leray Moragues, C.Navino, G. Verzetti, et al., Urea as a marker of adequacy inhemodialysis: lesson from in vivo urea dynamicsmonitoring, Kidney Int. 58 (Suppl. 76) (2000) S28–S40.

[33] M. Prado, L. Roa, A. Palma, J.A. Milan, A novelmathematical method based on urea kinetic modelling forcomputing the dialysis dose, Comput. Meth. Prog. Biomed.74 (2) (2004) 109–128.

[34] M. Prado, Aportaciones a la teleasistencia en pacientesrenales (shares to the telehealthcare for renal patients),PhD, University of Seville, Sevilla, 2003.

[35] C. Johner, P.W. Chamney, D. Schneditz, M. Kramer,Evaluation of an ultrasonic blood volume monitor,Nephrol. Dialysis Transpl. 13 (8) (1998) 2098–2103.

[36] M. Fenech, M. Maasrani, M.Y. Jaffrin, Fluid volumesdetermination by impedance spectroscopy and hematocritmonitoring: application to pediatric hemodialysis, Artif.Organs 25 (2) (2001) 89–98.

[37] E. Ammenwerth, S. Graber, G. Herrmann, T. Burkle, J.Konig, Evaluation of health informationsystems—problems and challenges, Int. J. Med. Informatics71 (2–3) (2003) 125–135.

[38] J.A. Effken, B.B. Brewer, A. Patil, G.S. Lamb, J.A. Verran, K.Carley, Using OrgAhead, a computational modelingprogram, to improve patient care unit safety and qualityoutcomes, Int. J. Med. Informatics 74 (7–8) (2005)605–613.

[39] R.R. Steuer, J.K. Leypoldt, A.K. Cheung, D.H. Harris, J.M.Conis, Hematocrit as an indicator of blood volume and apredictor of intradialytic morbid events, Asaio J. 40 (3)(1994) M691–M696.

[40] J.P. de Vries, P.M. Kouw, N.J. van der Meer, C.G. Olthof, L.P.Oe, A.J. Donker, et al., Non-invasive monitoring of bloodvolume during hemodialysis: its relation with post-dialyticdry weight, Kidney Int. 44 (4) (1993) 851–854.

[41] N.J. Ofsthun, J.K. Leypoldt, Ultrafiltration and backfiltrationduring hemodialysis, Artif. Organs 19 (11) (1995) 1143–1161.

[42] K.Y. Chen, D.R.J. Bassett, The technology ofaccelerometry-based activity monitors: current and future,Med. Sci. Sport Exerc. 37 (11) (2005) S490–S500.

[43] M.E. Hahn, L.-S. Chou, A model for detecting balance

impairment and estimating falls risk in the elderly, Ann.Biomed. Eng. 33 (6) (2005) 811–820.

[44] M.J. Mathie, A.C.F. Coster, N.H. Lovell, B.G. Celler,Accelerometry: providing an integrated, practical method

l i n

i n t e r n a t i o n a l j o u r n a l o f m e d i c a

for long-term, ambulatory monitoring of humanmovement, Physiol. Meas. 25 (2) (2004) R1–R20.

[45] B.A.K. Najafi, A. Paraschiv-Ionescu, F. Loew, C.J. Bula, P.Robert, Ambulatory system for human motion analysisusing a kinematic sensor: monitoring of daily physical

f o r m a t i c s 7 5 ( 2 0 0 6 ) 646–657 657

activity in the elderly, IEEE Trans. Biomed. Eng. 50 (6)(2003) 711–723.

[46] V. Rialle, F. Duchene, N. Noury, L. Bajolle, J. Demongeot,Health “Smart” home: information technology for patientsat home, Telemed. J. e-Health 8 (4) (2002) 395–409.