INITIAL RESULTS ON KNOWLEDGE DISCOVERY AND DECISION

SUPPORT FOR INTRACRANIAL ANEURYSMS

Christoph M. Friedrich, Martin Hofmann-Apitius

Fraunhofer Institute for Algorithms and Scientiﬁc Computing (SCAI), Department of Bioinformatics

Schloss Birlinghoven 53754 Sankt Augustin Germany

Robert Dunlop

Infermed Ltd, 25 Bedford Square, London, UK

Ioannis Chronakis

Department of Engineering, Oxford University, UK, Department of Academic Oncology, UCL, UK

Miriam C. J. M. Sturkenboom, Roelof Risselada

Erasmus MC, Medical Informatics, 3000 CA Rotterdam, Netherlands

Baldo Oliva, Ferran Sanz

Research Unit on Biomedical Informatics (GRIB) IMIM/UPF, C/Dr. Aiguader, 88, 08003 - Barcelona, Spain

Keywords:

Knowledge Discovery, Decision Support, Intracranial Aneurysm.

Abstract:

Intracranial Aneurysms are bulbous expansions of the intracranial vessels, that may rupture and lead to sub-

arachnoid haemorrhage, which can result in severe disability or death of the affected person. The prediction of

the individual rupture risk of a patient based on information from images, haemodynamic simulations, clinical

parameters and genetic markers is one of the aims of the European Integrated Project @neurIST. The pre-

dicted rupture risk is meant to support decision making on clinical treatment. We will present initial results on

Knowledge Discovery through a combination of text-mining, data integration from public bioinformatics data

sources, and database mining. Additionally, we provide ﬁrst results for decision support through knowledge

based clinical guidelines and Bayesian networks.

1 INTRODUCTION

The advent of improved medical imaging facilities

and their routine use in clinical practice increases

the number of accidentally detected asymptomatic In-

tracranial Aneurysms (IA). Intracranial Aneurysms

are bulbous expansions of the intracranial vessels, that

may rupture and lead to intracranial bleeding (sub-

arachnoid haemorrhage), which can result in severe

disability or death of the affected person. In (Rinkel

et al., 1998) the prevalence of the disease for adults

without risk factors for subarachnoid haemorrhage is

reported with approximately 2 % and the annual risk

rate for a rupture with 0.7 %. This relatively high

prevalence with low incidence of the dangerous event

leads to controversial discussions on treatment deci-

sions. In general there are three treatment options:

1. do not treat the asymptomatic aneurysm with low

risk

2. conduct neurosurgical clipping

3. deploy a platinum coil via endovascular interven-

tion

One of the targets of the European Integrated

Project @neurIST

is to support decision making on

IA treatment options by building a distributed envi-

ronment for healthcare. This environment will al-

low access to patient related information from images,

http://www.aneurist.org

265

M. Friedrich C., Hofmann-Apitius M., Dunlop R., Chronakis I., C. J. M. Sturkenboom M., Risselada R., Oliva B. and Sanz F. (2008).

INITIAL RESULTS ON KNOWLEDGE DISCOVERY AND DECISION SUPPORT FOR INTRACRANIAL ANEURYSMS.

In Proceedings of the First International Conference on Health Informatics, pages 265-272

 SciTePress

haemodynamic simulations, clinical parameters, ge-

netic markers and epidemiological data.

Additionally, a set of application suites will be de-

veloped, that are based on this infrastructure and di-

rectly support the goal of improving clinical decision

making. A draft architecture of the distributed sys-

tem is described in (Arbona et al., 2007). Considering

the target of this paper, two application suites are of

interest, @neuRisk, a decision support system based

on clinical guidelines and @neuLink, a research ori-

ented application targeted at linking genetics to dis-

ease. The @neuLink suite supports Knowledge Dis-

covery for the detection of genetic risk factors.

2 INITIAL RESULTS

In the following we will give examples and prelimi-

nary results of Decision Support and Knowledge Dis-

covery that have been developed during the ﬁrst year

of the project.

2.1 Decision Support based on the

Proforma Language and the

REACT Application

In this section we will describe the work carried out

in the development of the ﬁrst @neuRisk prototype.

This version of the prototype employs a mixed quan-

titative and qualitative approach to provide risk as-

sessment and decision support in the treatment of

cerebral aneurysms. The knowledge base for the

approach is derived from two trials: The Interna-

tional Study on Unruptured Intracranial Aneurysms

(ISUIA) (Wiebers et al., 2003) and International Sub-

arachnoid Aneurysm Trial (ISAT) (Molyneux et al.,

2005). For demonstration purposes, some additional

test data were also included to show how future

research results could be incorporated to the ﬁnal

@neuRisk suite.

Qualitative decision support in @neuRisk has

been implemented using the PROforma method and

tools (Sutton and Fox, 2003) while quantitative deci-

sion support was implemented by adapting an existing

treatment planning application called REACT (Risk,

Events, Actions and their Consequences over Time)

(Glasspool et al., 2006).

PROforma is a well established technology, ﬁrst

published in 1996 (Fox et al., 1996) and described in

detail in 2000 (Fox and Das, 2000; Sutton and Fox,

2003), is a well established clinical decision support

technology, that has been tested in a number of trials

with promising results (Fox et al., 2006; Hurt et al.,

2003; Patkar et al., 2006). There are two major imple-

mentations available, the Tallis implementation from

Cancer Research UK

and the Arezzo implementation

by InferMed Ltd in London

. REACT technology is

based on PROforma concepts and has been tested in

one trial in the area of genetic counselling with en-

couraging results (Glasspool et al., 2006).

In the qualitative part of the prototype, we used

the PROforma language (Sutton and Fox, 2003) to

model both the workﬂow involved in patient manage-

ment and a set of treatment decisions. The resulted

computer-executable guideline application is then en-

acted by the Tallis PROforma engine. It guides the

user through the workﬂow, provides a set of data cap-

ture services to collect data from the various disci-

plines (clinician, radiologist, geneticist, etc) and ﬁ-

nally, it offers support for the treatment decisions. It

does this by offering a set of logical arguments (rules)

to the clinicians which either support or oppose each

of the available treatment actions. The system sug-

gests the most appropriate action but the ﬁnal decision

is taken by the clinician (Fox et al., 2006).

In the quantitative part of the prototype, we used

an adapted version of the REACT tool (Glasspool

et al., 2006). This tool provides support for planning

the treatment of the patient based on the effect that

each treatment action has on the risk. The REACT

user interface is divided into 4 major parts:

1. The treatment plan,

2. The graph area,

3. The argumentation area and

4. The notiﬁcation area.

The treatment plan area provides the clinician with

a set of available treatment options and a timeline

where she/he can schedule them, similar to a Gantt

chart. As the user adds or removes events from the

treatment plan, the graph area plots the expected con-

sequences in real time (in the @neuRisk prototype

these are life expectancy and years gained or lost if an

aneurysm were left untreated). This allows the user

to explore the space of available options with imme-

diate feedback of the various interactions and conse-

quences. Information directly relevant to each option

Tallis is an implementation of the PROforma engine

written in Java which was developed at Cancer Research

UK by the Advanced Computation Laboratory (http://

acl.icnet.uk/) that was led by Prof John Fox. The en-

gine is supported by a suite of tools including an engine,

an authoring tool, a tester tool and a web based enacting

application (http://www.cossac.org/tallis.html).

Arezzo is an implementation of the PROforma engine

created by InferMed (http://www.infermed.com) and it

has been used in a number of commercial products.

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pert work, specialisation on one topic and possible se-

lection bias. As an alternative to this manual extrac-

tion, we consider text-mining (Jensen et al., 2006).

This helps to get an overview on genes possibly in-

volved in a disease and to ﬁnd potential new genes

from publications. We implemented a Find Candi-

date Genes module in the @neuLink application suite.

This part of the application suite is based on two text-

mining systems, ProMiner (Hanisch et al., 2005) and

OSIRIS (Bonis et al., 2006). ProMiner ﬁnds enti-

ties (Genes/Proteins, Drugnames, Chromosomal Lo-

cations . . . ) and links them to unique database identi-

ﬁers, e.g. EntrezGene (Maglott et al., 2005). OSIRIS

ﬁnds and disambiguates mentions of genetic varia-

tions in text to dbSNP (Smigielski et al., 2000) iden-

tiﬁers with a query-expansion approach.

To support the focussed view of the user on rel-

evant information in the disease context, we devel-

oped a ranking mechanism based on Relative Entropy

(Kullback and Leibler, 1951), also known as Kull-

back/Leibler divergence. In this ranking mechanism,

we use the complete MedLine as reference corpus and

contrast it with the speciﬁc corpus derived from full-

text search.

Finding and disambiguating variation mentions in

text with the OSIRIS system, needs a high-quality

gene-mention machinery. We therefore combined our

text-mining tools and complemented them with a ma-

chine learning variation ﬁnding engine based on Con-

ditional Random Fields (CRF) (Lafferty et al., 2001).

The improved results of this approach have been de-

scribed in (Klinger et al., 2007).

One of the crucial questions of all Discovery

methods is their validation. For the ﬁnding and dis-

ambiguation of gene mentions in text, we have been

able to get an independent assessment of the perfor-

mance of our approach by participating in the BioCre-

aTive II assessment (Morgan and Hirschmann, 2007).

Our ProMiner system assessed as described in (Fluck

et al., 2007), has been ranked 3rd of 21 submissions.

In a second evaluation, we tested wether our sys-

tem, given the keyword search “intracranial AND

aneurysm*”, was able to detect the same related sus-

pectibility genes, that have been found by human

experts. The review on genetics (Krischek and In-

oue, 2006) mentions 18 associated genes in the con-

text of Intracranial Aneurysms. In our evaluation (as

of 2007-10-01) (Gattermayer, 2007), we ﬁnd 16,548

documents in PubMed related to the keyword and 596

documents, that mention 316 different genes/proteins.

We ﬁnd and could disambiguate all 18 genes in pub-

lications and rank them to the ﬁrst 238 hits with 7

hits among the top 16 candidates. See ﬁgure 3 for a

screenshot of the interface. Among the high-ranked

false positives we ﬁnd frequently used therapeutic

proteins like the plasminogen activator (PLAT), but

also new true positives like the JAG1 gene, that have

not been mentioned in the genetic reviews.

2.5 Generating Protein-Protein

Interaction Networks

We used “Protein ineractions and network analysis”

(PIANA) (Aragues et al., 2006) to combine data

from the Database of Interacting Proteins (DIP) (Sal-

winski et al., 2004), the MIPS database of interac-

tions (Pagel et al., 2005), the Molecular INTeractions

database (MINT) (Chatr-aryamontri et al., 2007), In-

tAct (Kerrien et al., 2007), the Biomolecular In-

teractions Database (BIND) (Alfarano et al., 2005),

the BioGrid (Stark et al., 2006) and Human Pro-

tein Reference Database (HPRD) (Peri et al., 2003)

and the human interactions from two recent high-

throughput experiments (Rual et al., 2005),(Stelzl

et al., 2005). We also provide the interactions

obtained from STRING (von Mering et al., 2005)

and methods of protein-protein interaction prediction

based on sequence/structure patterns (Espadaler et al.,

2005), (Cockell et al., 2007). The integration of many

different sources of interactions into a single repos-

itory allowed us to work with an extensive set of

363,571 interactions between 42,040 different protein

sequences.

PIANA represents protein interaction data as a

network where the nodes are proteins and the edges

interactions between them. In such a network, a set

of proteins linked to protein p

(i.e. physically inter-

acting with p

) is named “partners of p

”. PIANA

builds the network by retrieving partners for a initial

set of seed proteins (i.e. the relevant proteins, here re-

ferred as “seed proteins”) that were obtained from the

Find Candidate Genes module in section 2.4. A net-

work is generated for the set of proteins that contains

them and their partners. In this network, a protein

that is connected to more than one “seed” is referred

as a linker-N, with N being the number of seed pro-

teins to which it is connected. Finally, proteins only

connected to one seed protein are named leafs. This

allowed us to enlarge the interaction network and de-

tect new putatively relevant proteins for the biological

pathway.

3 CONCLUSIONS

We have presented initial results on Decision Support,

Database Mining and Knowledge Discovery for In-

tracranial Aneurysms. Due to the lack of patient data,

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biomedical data and compute services. IEEE Trans

Nanobioscience, 6(2):136–141.

Bonis, J., Furlong, L. I., and Sanz, F. (2006). OSIRIS: a

tool for retrieving literature about sequence variants.

Bioinformatics, pages 2567–2569.

Chatr-aryamontri, A., Ceol, A., Palazzi, L. M., Nardelli,

G., Schneider, M. V., Castagnoli, L., and Cesareni, G.

(2007). Mint: the molecular interaction database. Nu-

cleic Acids Res, 35(Database issue):D572–D574.

Clarke, M. (2007). Cochrane literature review snapshot v2.0

- aneurysm analysis rupture risk review. Deliverable

of the @neurIST project D18, University of Oxford.

Cockell, S. J., Oliva, B., and Jackson, R. M. (2007).

Structure-based evaluation of in silico predictions of

protein-protein interactions using comparative dock-

ing. Bioinformatics, 23(5):573–581.

Druzdzel, M. J. (1999). GeNIe: A development environ-

ment for graphical decision-analytic models. In Pro-

ceedings of the 1999 Annual Symposium of the Amer-

ican Medical Informatics Association (AMIA-1999),

page 1206.

Efron, B. and Tibshirani, R. J. (1993). An Introduction to

the Bootstrap. Chapman & Hall/CRC.

Espadaler, J., Romero-Isart, O., Jackson, R. M., and Oliva,

B. (2005). Prediction of protein-protein interactions

using distant conservation of sequence patterns and

structure relationships. Bioinformatics, 21(16):3360–

3368.

Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996).

Knowledge Discovery and Data Mining: Towards a

Unifying Framework. In Knowledge Discovery and

Data Mining, pages 82–88.

Fluck, J., Mevissen, H. T., Dach, H., Oster, M., and

Hofmann-Apitius, M. (2007). ProMiner: recognition

of human gene and protein names using regularly up-

dated dictionaries. In (Hirschmann et al., 2007), pages

149–151.

Fox, J. and Das, S. (2000). Safe and sound: Artiﬁcial intel-

ligence in hazardous applications. In Proceedings of

AAAI 2000.

Fox, J., Johns, N., Rahmanzadeh, A., and Thomsen, R.

(1996). PROforma: A method and language for spec-

ifying clinical guidelines and protocols. In Brender,

J., Christensen, J., Scherrer, J.-R., and McNair, P., ed-

itors, Proceedings of the Medical Informatics Europe

1996, pages 516–520. IOS Press.

Fox, J., Patkar, V., and Thomson, R. (2006). Decision sup-

port for health care: the proforma evidence base. In-

form Prim Care, 14(1):49–54.

Gattermayer, T. (2007). SCAIView: annotation and visu-

alization system for knowledge discovery. Master’s

thesis, Life Science Informatics at Bonn-Aachen In-

ternational Center for Information Technology (B-IT);

Germany.

Ghinea, N. and van Gelder, J. M. (2004). A probabilis-

tic and interactive decision-analysis system for un-

ruptured intracranial aneurysms. Neurosurg Focus,

17(5):E9.

Glasspool, D. W., Fox, J., Oettinger, A., and Smith-Spark,

J. H. (2006). Argumentation in decision support for

medical care planning for patients and clinicians. In

Proceedings of the AAAI Spring Symposium Series

2006.

Han, B., Friedrich, C. M., Manthey, R., and Hofmann-

Apitius, M. (2006). Bayesian network modeling for

the prediction of treatment outcomes of intracranial

aneurysms. In Huson, D., Kohlbacher, O., Lupas, A.,

Nieselt, K., and Zell, A., editors, Short Papers and

Poster Abstracts of the German Conference on Bioin-

formatics (GCB2006), page 122.

Hanisch, D., Fundel, K., Mevissen, H.-T., Zimmer, R.,

and Fluck, J. (2005). ProMiner: rule-based protein

and gene entity recognition. BMC Bioinformatics, 6

(Suppl 1)(S14).

Hirschmann, L., Krallinger, M., and Valencia, A., editors

(2007). Proceedings of the Second BioCreative Chal-

lenge Evaluation Workshop. Centro Nacional de In-

vestigaciones Oncologicas, CNIO.

Hurt, C., Fox, J., Bury, J., and Saha, V. (2003). Comput-

erised advice on drug dosage decisions in childhood

leukaemia: a method and a safety strategy. In Dojat,

M., Keravnou, E., and Barahona, P., editors, Proceed-

ings of the 9th Conference on Artiﬁcial Intelligence

in Medicine in Europe (AIME’03), LNAI 2780, pages

158–163. Springer Verlag.

Jensen, L. J., Saric, J., and Bork, P. (2006). Literature min-

ing for the biologist: from information retrieval to bi-

ological discovery. Nature Reviews; Genetics, 7:119–

129.

Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I.,

Bridge, A., Derow, C., Dimmer, E., Feuermann, M.,

Friedrichsen, A., Huntley, R., Kohler, C., Khadake, J.,

Leroy, C., Liban, A., Lieftink, C., Montecchi-Palazzi,

L., Orchard, S., Risse, J., Robbe, K., Roechert, B.,

Thorneycroft, D., Zhang, Y., Apweiler, R., and Her-

mjakob, H. (2007). Intact–open source resource

for molecular interaction data. Nucleic Acids Res,

35(Database issue):D561–D565.

Klinger, R., Furlong, L. I., Friedrich, C. M., Mevissen,

H. T., Fluck, J., Sanz, F., and Hofmann-Apitius, M.

(2007). Identifying gene speciﬁc variants in biomedi-

cal text. Journal of Bioinformatics and Computational

Biology, 5(6):in print.

Krischek, B. and Inoue, I. (2006). The genetics of intracra-

nial aneurysms. J Hum Genet, 51(7):587–594.

Kullback, S. and Leibler, R. (1951). On information and

sufﬁciency. Ann. Math. Stat., 22:79–86.

Lafferty, J., McCallum, A., and Pereira, F. C. N. (2001).

Conditional Random Fields: Probabilistic Models for

Segmenting and Labeling Sequence Data. In Pro-

ceedings of the Eighteenth International Conference

on Machine Learning (ICML 2001), pages 282–289.

Morgan Kaufmann Publishers.

Maglott, D., Ostell, J., Pruitt, K. D., and Tatusova, T.

(2005). Entrez Gene: gene-centered information at

NCBI. Nucleic Acids Research, 0:D1–D6.

INITIAL RESULTS ON KNOWLEDGE DISCOVERY AND DECISION SUPPORT FOR INTRACRANIAL

ANEURYSMS

271

Molyneux, A. J., Kerr, R. S. C., Yu, L., Clarke, M., Sneade,

M., Yarnold, J. A., and Sandercock, P. (2005). Inter-

national subarachnoid aneurysm trial (ISAT) of neuro-

surgical clipping versus endovascular coiling in 2143

patients with ruptured intracranial aneurysms: a ran-

domised comparison of effects on survival, depen-

dency, seizures, rebleeding, subgroups, and aneurysm

occlusion. Lancet, 366:809–817.

Morgan, A. A. and Hirschmann, L. (2007). Overview of

BioCreative II gene normalization. In (Hirschmann

et al., 2007), pages 17–27.

Nahed, B. V., Bydon, M., Ozturk, A. K., Bilguvar, K.,

Bayrakli, F., and Gunel, M. (2007). Genetics of in-

tracranial aneurysms. Neurosurgery, 60(2):213–25;

discussion 225–6.

Pagel, P., Kovac, S., Oesterheld, M., Brauner, B., Dunger-

Kaltenbach, I., Frishman, G., Montrone, C., Mark, P.,

umpﬂen, V., Mewes, H.-W., Ruepp, A., and Frish-

man, D. (2005). The mips mammalian protein-protein

interaction database. Bioinformatics, 21(6):832–834.

Patkar, V., Hurt, C., Steele, R., Love, S., Purushotham,

A., Williams, M., Thomson, R., and Fox, J. (2006).

Evidence-based guidelines and decision support ser-

vices: A discussion and evaluation in triple assess-

ment of suspected breast cancer. Br J Cancer,

95(11):1490–1496.

Peri, S., Navarro, J. D., Amanchy, R., Kristiansen, T. Z.,

Jonnalagadda, C. K., Surendranath, V., Niranjan, V.,

Muthusamy, B., Gandhi, T. K. B., Gronborg, M., Ibar-

rola, N., Deshpande, N., Shanker, K., Shivashankar,

H. N., Rashmi, B. P., Ramya, M. A., Zhao, Z., Chan-

drika, K. N., Padma, N., Harsha, H. C., Yatish, A. J.,

Kavitha, M. P., Menezes, M., Choudhury, D. R.,

Suresh, S., Ghosh, N., Saravana, R., Chandran, S., Kr-

ishna, S., Joy, M., Anand, S. K., Madavan, V., Joseph,

A., Wong, G. W., Schiemann, W. P., Constantinescu,

S. N., Huang, L., Khosravi-Far, R., Steen, H., Tewari,

M., Ghaffari, S., Blobe, G. C., Dang, C. V., Garcia,

J. G. N., Pevsner, J., Jensen, O. N., Roepstorff, P.,

Deshpande, K. S., Chinnaiyan, A. M., Hamosh, A.,

Chakravarti, A., and Pandey, A. (2003). Develop-

ment of human protein reference database as an initial

platform for approaching systems biology in humans.

Genome Res, 13(10):2363–2371.

Rinkel, G. J., Djibuti, M., Algra, A., and van Gijn, J.

(1998). Prevalence and risk of rupture of intracranial

aneurysms. Stroke, 29:251–256.

Risselada, R., van der Lugt, A., Niessen, W. J., and Sturken-

boom, M. C. J. M. (2007). Hormonal contraceptives

and risk of aneurysmatic subarachnoid haemorrhage.

Basic & Clinical Pharmacology & Toxicology, Spe-

cial Issue on 8th Congress of the European Associa-

tion for Clinical Pharmacology and Therapeutics, 101

(Suppl.):40.

Rual, J.-F., Venkatesan, K., Hao, T., Hirozane-Kishikawa,

T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D.,

Dreze, M., Ayivi-Guedehoussou, N., Klitgord, N., Si-

mon, C., Boxem, M., Milstein, S., Rosenberg, J.,

Goldberg, D. S., Zhang, L. V., Wong, S. L., Franklin,

G., Li, S., Albala, J. S., Lim, J., Fraughton, C., Llam-

osas, E., Cevik, S., Bex, C., Lamesch, P., Sikorski,

R. S., Vandenhaute, J., Zoghbi, H. Y., Smolyar, A.,

Bosak, S., Sequerra, R., Doucette-Stamm, L., Cusick,

M. E., Hill, D. E., Roth, F. P., and Vidal, M. (2005).

Towards a proteome-scale map of the human protein-

protein interaction network. Nature, 437(7062):1173–

1178.

Russel, S. and Norvig, P. (2003). Artiﬁcial Intelligence a

modern Approach. Prentince Hall, 2nd edition.

Salwinski, L., Miller, C. S., Smith, A. J., Pettit, F. K.,

Bowie, J. U., and Eisenberg, D. (2004). The database

of interacting proteins: 2004 update. Nucleic Acids

Res, 32(Database issue):D449–D451.

Smigielski, E. M., Sirotkin, K., Ward, M., and Sherry, S. T.

(2000). dbSNP: a database of single nucleotide poly-

morphisms. Nucleic Acids Research, 28(1):352–355.

Stark, C., Breitkreutz, B.-J., Reguly, T., Boucher, L., Bre-

itkreutz, A., and Tyers, M. (2006). Biogrid: a general

repository for interaction datasets. Nucleic Acids Res,

34(Database issue):D535–D539.

Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brem-

beck, F. H., Goehler, H., Stroedicke, M., Zenkner, M.,

Schoenherr, A., Koeppen, S., Timm, J., Mintzlaff, S.,

Abraham, C., Bock, N., Kietzmann, S., Goedde, A.,

Toks

oz, E., Droege, A., Krobitsch, S., Korn, B., Birch-

meier, W., Lehrach, H., and Wanker, E. E. (2005). A

human protein-protein interaction network: a resource

for annotating the proteome. Cell, 122(6):957–968.

Sutton, D. R. and Fox, J. (2003). The syntax and semantics

of the proforma guideline modeling language. J Am

Med Inform Assoc, 10(5):433–443.

van der Lei, J., Duisterhout, J. S., Westerhof, H. P., van der

Does, E., Cromme, P. V., Boon, W. M., and van Bem-

mel, J. H. (1993). The introduction of computer-based

patient records in the netherlands. Ann Intern Med,

119(10):1036–1041.

von Mering, C., Jensen, L. J., Snel, B., Hooper, S. D.,

Krupp, M., Foglierini, M., Jouffre, N., Huynen,

M. A., and Bork, P. (2005). String: known and

predicted protein-protein associations, integrated and

transferred across organisms. Nucleic Acids Res,

33(Database issue):D433–D437.

Wiebers, D., Whisnant, J., Huston, J., Meissner, I.,

Brown RD, J., Piepgras, G., Thielen, K., Nichols,

D., O’Fallon, W., Peacock, J., Jaeger, L., Kassell, N.,

Kongable-Beckman, G., and Torner, J. (2003). Unrup-

tured intracranial aneurysms: natural history, clinical

outcome, and risks of surgical and endovascular treat-

ment. Lancet, 362:103–110.

HEALTHINF 2008 - International Conference on Health Informatics

272