PhD Defense - Knowledge graphs based extension of patients’ files to predict hospitalization

Knowledge graphs based extension of patients’ ﬁles to predict
hospitalization
Raphaël Gazzotti
Supervisors: Catherine Faron Zucker & Fabien Gandon
PhD Thesis Defense - April 2020

CURRENT STATUS
2
Problem of hospitalization:
● Rate of 191 hospitalizations
per 1000 inhabitants (12.7 million patients).
● On average a full hospitalization lasts:
○ 6.4 days in medicine, surgery, obstetrics.
○ 36.4 days in follow-up care and
rehabilitation.
○ 44.5 days in home hospitalization.
○ 57.0 days in psychiatry.
90%
Perceived as a failure by the physician.
Increase in the number of patients in a state of comorbidity (multiplicity of chronic diseases).
Feeling of abandonment of physicians
General practitioners problem:
Difficulty to plan actions against comorbidity:
● Lack of recommendations.
● Multiplication of treatments and side
effects.
● Risk of applying recommendations for
isolated diseases.

[Lacroix-Hugues et al. 2012]
Balanced Dataset
PRIMEGE [SINCE 2012] - DATASET
Gender Consultation’s reason ICPC2 ... History Observations
H vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général -
auscult pulm libre; bdc rég sans
souffle - tympans ok-
3

TOWARDS A DECISION SUPPORT TOOL FOR PHYSICIANS
4
Preventive system that orders the risk factors involved in patient hospitalization:
1. Predict hospitalization => learn the risk factors.
2. Sort the risk factors found in patient’s ﬁle (personalised medicine).
3. Provide a simulation with a decision support tool called Health Predict.
General practitioner:
● Preserve and/or improve his patient’s health and autonomy.
● Predict as rapidly an easily as possible the probability of hospitalization of his patient.
● Avoid hospitalization to his patient as far as possible.
Patient:
● Preserve and/or improve one’s health and autonomy.
● Avoid one’s hospitalization as far as possible.
Computer scientist:
● Use of methods that do not jeopardize data conﬁdentiality.
● Make sense of all the variables contained in medical records.

THESIS OVERVIEW
5
I. Predicting hospitalization on a basic representation of
electronic medical records
II. Predicting hospitalization based on electronic medical
records representation enrichment
A. How to extract knowledge relevant for the prediction
of the occurrence of an event?
B. Can ontological augmentations of the features
improve the prediction of the occurrence of an event?
III. Decision support application
IV. Conclusion

THESIS OVERVIEW
6
IV. Conclusion

VECTOR REPRESENTATION OF TEXT DATA IN EMRs
We opted for a Bag-of-words model:
● Main information extracted without
requiring a large corpus.
● Attributes are not transformed.
● Integration of heterogeneous data
with concatenation.
7
Doc. 1 History of headaches and facial weakness.
Doc. 2 Severe structural defects of the right petrous temporal bone.
Doc. 3 Patient presented to the Emergency Room.
(a) Sample documents
Word 1 History Word 11 right
Word 2 of Word 12 petrous
Word 3 headaches Word 13 temporal
Word 4 and Word 14 bone
Word 5 facial Word 15 Patient
Word 6 weakness Word 16 presented
Word 7 Severe Word 17 to
Word 8 structural Word 18 Emergency
Word 9 defects Word 19 Room
Word 10 the
(b) Vocabulary used in the document collection
1 0 0
1 1 0
1 0 0
1 0 0
1 0 0
1 0 0
0 1 0
0 1 0
0 1 0
0 1 0
0 1 1
0 1 0
0 1 0
0 1 0
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
(c) Word-document occurrence matrix

A patient's medical record can be spread over a long period of time (several years):
● Use of non-sequential algorithms.
● Involves to aggregate all their consultations.
We have opted for the patients:
● “Hospitalized” => All consultations occurring before hospitalization / return from hospitalization.
● “Not Hospitalized” => All their consultations.
● Hospitalization patterns detected with a regex.
HANDLING TEMPORAL DIMENSION IN THE REPRESENTATION OF EMRs (1)
Hospitalization
8

HANDLING TEMPORAL DIMENSION IN THE REPRESENTATION OF EMRs (2)
Sequential representation deﬁned as:
● ti
= (xi
,yi
)
It handles both (xi
):
● Permanent Data
○ Family History
○ Personal History
○ Patient Information
● Time-series Data
○ consultation speciﬁc
9

TP: True Positives
FP: False Positives
FN: False Negatives
Validation of results by nested cross-validation:
● K at 10 for the outer loop.
● L at 2 for the inner loop, hyperparameters by
random search (7 iterations).
Metric to evaluate vector representations:
● The F-measure Ftp,fp
(Forman and Scholz (2010))
suited to cross-validation context.
PREDICTING HOSPITALIZATION FROM EMRs: EXPERIMENTAL PROTOCOL
10

11
PREDICTING HOSPITALIZATION FROM EMRs: RESULTS
SVC RF Log CRFs
0.819 0.831 0.850 0.834
Machine learning algorithms and hyperparameters optimized:
● SVC, C-Support vector classiﬁer, libSVM [Chang and Lin 2011]:
○ The penalty parameter C, the kernel used and the kernel coefficient.
● RF, Random forest [Breiman 2001]:
○ Number of trees in the forest, the maximum depth in the tree, the
minimum number of samples required to split an internal node, the
minimum number of samples required to be at a leaf node and the
maximum number of lead nodes.
● LR, Logistic regression [Mcullagh and Nelder 1989]:
○ The regularization coefficient C, the penalty used by the algorithm.
● CRFs, Conditional random ﬁelds [Sutton et al. 2012]:
○ The regularization coefficients c1 and c2 used by the solver L-BFGS.
No added value in terms of performance with a sequential representation.

THESIS OVERVIEW
12
IV. Conclusion

THESIS OVERVIEW
13
IV. Conclusion

TEXT REPRESENTATION & INJECTION OF DOMAIN KNOWLEDGE
BOW Concept Vector
Resulting Vector
14
Bag-of-words (BOW) representation:
● Main information extracted without
requiring a large corpus.
● Attributes are not transformed.
● Integration of heterogeneous data with
concatenation.
Injection of domain knowledge by
concatenation:
● BOW obtained from text ﬁelds.
● Concept vector from ontologies.
1 2
2: Cardiovascular disease
1: Organ failure
...
Data
Cardiovascular
disease
Organ failure ...
Concept
Vector

Open knowledge bases used:
● The French chapter of DBpedia with the property dcterms:subject.
● Wikidata with knowledge about drugs:
○ Properties: ‘subject has role’ (wdt:P2868), ‘medical condition treated’ (wdt:P2175)
and ‘significant drug interaction’ (wdt:P769).
○ Couple property-concept (e.g., meprobamate treats (wdt:P2175) headache).
Vocabularies specific to the health sector, OWL-SKOS representations of :
● ATC (Anatomical, Therapeutic and Chemical):
○ e. g., ‘meprednisone’ (code H02AB15) has ‘Glucocorticoids, Systemic’ (code H02AB) as super class…
○ Considered as super classes concepts linked by properties rdfs:subClassOf and atc:member_of.
● ICPC2 (International Classification of Primary Care):
○ e. g., ‘Symptom and complaints’ (H05) has ‘Ear’ (H) as super class (rdfs:subClassOf) .
● NDF-RT (National Drug File - Reference Terminology):
○ Properties: ‘may_prevent’, ‘CI_with’, ‘may_treat’.
ONTOLOGIES USED TO ENRICH THE REPRESENTATION OF EMRs
15

NAMED ENTITY RECOGNITION IN EMRs AND LINKING WITH ONTOLOGIES
16
Use of free text and structured data.
Not all drug-related properties necessarily
exist in Wikidata:
● RxNorm code
● cui code
● ATC code

NAMED ENTITY LINKING WITH DBPEDIA
Enrichment workflow used with DBpedia (limited set of concepts):
17
Speciality Concept
Oncology Neoplasm stubs, Oncology, Radiation therapy
Cardiovascular Cardiovascular disease, Cardiac arrhythmia
Neuropathy Neurovascular disease
Immunopathy Malignant hemopathy, Autoimmune disease
Endocrinopathy Medical condition related to obesity
Genopathy Genetic diseases and disorders
Intervention Surgical removal procedures, Organ failure
Emergencies Medical emergencies, Cardiac emergencies
(+s, +s*)
● +s: Consider the text fields about: the
patient's personal history, family history,
allergies, environmental factors, past health
problems, current health problems, reasons
for consultations, diagnoses, medications,
care procedures, reasons for prescribing
medications, physician observations,
symptoms and diagnosis.
● +s*: Consider the text fields about: patient's
personal history, allergies, environmental
factors, current health problems, reasons for
consultations, diagnoses, medications, care
procedure, reasons for prescribing
medications and physician observations.

EXAMPLE OF EXTRACTED DBPEDIA CONCEPTS
18
Patient 1 Patient 2
French prédom à gche - insuf vnse ou insuf cardiaque -
pas signe de phlébite - - ne veut pas mettre de
bas de contention et ne veut pas aumenter le
lasilix... -
procédure FIV - - transfert embryon samedi
dernière - a fait hyperstimulation ovarienne;
rupture de kyste - - asthénie, - - dleur abdo,
doulleur à la palpation ++ - - voit gynéco la
semaine prochaine pr controle betahcg, echo
-
English
(Translation)
predom[inates] on the l[e]ft, venous or cardiac
insuf[ﬁciency], no evidence of phlebitis, does not
want to wear compression stockings and does not
want to increase the lasix
In vitro fertilization procedure, embryo
transfer last Saturday, did ovarian
hyperstimulation, cyst rupture, asthenia,
abdominal [pain], [pain] on palpation ++, will
see a gyneco[logist] next week [for] a beta
HCG, echo check-up
Concepts Cardiovascular disease,
Organ failure
Neoplasm stubs
PREFIX dbpedia-fr: <http://fr.dbpedia.org/resource/> PREFIX category-fr:<http://fr.dbpedia.org/resource/Catégorie:>
insuffisance cardiaque dbpedia-fr:Insuffisance_cardiaque category:fr:Défaillance_d'organe

AUTOMATIC EXTRACTION WITH DBPEDIA
19
Use of features extracted from text of the medical reports.
Generation of vector representations based on annotations
from feature selection.

PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX yago: <http://dbpedia.org/class/yago/>
PREFIX cat: <http://fr.dbpedia.org/resource/Catégorie:>
SELECT ?skos_subject WHERE {
SERVICE <http://fr.dbpedia.org/sparql> {
# Constraint on the medical domain
VALUES ?concept_constraint {
cat:Maladie # disease
cat:Santé # health
cat:Génétique_médicale # medical genetics
cat:Médecine # medicine
cat:Urgence # urgency
cat:Traitement # treatment
cat:Anatomie # anatomy
cat:Addiction # addiction
cat:Bactérie # bacteria
} 20
SPARQL QUERY FOR CONCEPT EXTRACTION IN DBPEDIA
<link_dbpedia_spotlight>
dbpedia-owl:wikiPageRedirects{0,1} ?page.
?page dcterms:subject ?page_subject.
?page_subject skos:broader{0,10} ?concept_constraint.
?page_subject skos:prefLabel ?skos_subject.
?page owl:sameAs ?page_en.
# Filter used to select the corresponding resource in the
English Chapter of DBpedia
FILTER(STRSTARTS(STR(?page_en),
"http://dbpedia.org/resource/")) }
SERVICE <http://dbpedia.org/sparql> {
?page_en a ?type
}
# Selection on speciﬁc types on the DBpedia EN chapter
FILTER(?type in (dbo:Disease, dbo:Bacteria,
yago:WikicatViruses,
yago:WikicatRetroviruses,
yago:WikicatSurgicalProcedures,
yago:WikicatSurgicalRemovalProcedures))
}

VALIDATION OF THE CONCEPTS SELECTED TO ANNOTATE EMRs
Annotation of the 285 concepts by 3 experts:
● 2 General practitioners
● 1 Biologist
Inter-rater agreement (relevance of concepts):
● 0.51 / 0.21 between the GPs.
● Without terminological conﬂicts 0.66 / 0.52
between the GPs (243 Concepts).
● Average of 198 concepts annotated as
useful in studying hospitalization risk.
21
Scores are not signiﬁcant.
Harder than it looks.

ON THE CORRELATION BETWEEN HUMAN AND MACHINE ANNOTATORS (1)
Correlation between annotated sets of 285
concepts from both human and machine
annotators.
The correlation metric ranges from 0 to 2:
● 0: Perfect correlation
● 1: No correlation
● 2: Perfect negative correlation
22
ū is the mean of elements of u.
Cosine distance uses the same formula
without the mean.
Maximum variation:
● Maximum of 0.681 between humans
● Maximum of 0.485 between machines
Machines’ speciﬁcities:
● U1
similar to M5
score of 0.12

ON THE CORRELATION BETWEEN HUMAN AND MACHINE ANNOTATORS (2)
23
Maximum variation:
● Maximum of 0.681 between humans (A1
/A2
)
● Maximum of 0.485 between machines (M10
/U1
)
Machines’ speciﬁcities:
● U1
similar to M5
score of 0.12
Closer annotation with the machines.
Caption:
: > 0.5
: 0.25<x<0.5
: < 0.25

THESIS OVERVIEW
24
IV. Conclusion

TP: True Positives
FP: False Positives
FN: False Negatives
Validation of results by nested cross-validation:
● Re-shuffle of the folds.
● K at 10 for the outer loop.
● L at 3 for the inner loop, hyperparameters by
random search (150 iterations).
Metric to evaluate vector representations:
● The F-measure Ftp,fp
(Forman and Scholz (2010))
suited to cross-validation context.
PREDICTING HOSPITALIZATION FROM EMRs: EXPERIMENTAL PROTOCOL
25

● baseline: BOW generated from medical records.
● +t: ICPC2 concepts.
● +s: DBpedia on all text ﬁelds.
● +s*: DBpedia focussed on patient’s own record.
● +c: ATC on different hierarchical depth levels.
● +wa: Wikidata, property ‘subject has role’.
● +wi: Wikidata, property ‘signiﬁcant drug interaction’.
● +wm: Wikidata, property ‘medical condition treated’.
● +d: enrichment with NFD-RT:
○ prevent
=> ‘may_prevent’ property
○ treat
=> ‘may_treat’ property
○ CI
=> ‘CI_with’ property.
INDIVIDUAL FEATURES RESULTS - Ftp,fp
metric
Features set SVC RF Log
baseline 0.8270 0.8533 0.8491
+t 0.8239 0.8522 0.8545
+s 0.8221 0.8522 0.8485
+s* 0.8339 0.8449 0.8514
+c1
0.8235 0.8433 0.8453
+c1
-2
0.8254 0.8480 0.8510
+c2
0.8348 0.8522 0.8505
+wa 0.8223 0.8468 0.8545
+wi 0.8149 0.8484 0.8501
+wm 0.8221 0.8453 0.8458
+dprevent
0.8254 0.8506 0.8479
+dtreat
0.8338 0.8472 0.8481
+dCI
0.8281 0.8498 0.8460 26

Favg
OF INDIVIDUAL FEATURES UNDER Log
27

COMBINED FEATURES RESULTS & CONVERGENCE CURVE
28
+t+s+c2
+wa+wi 0.8258 0.8486 0.8547
+t+s*+c2
+wa+wi 0.8239 0.8494 0.8543
+t+c2
+wa+wi 0.8140 0.8531 0.8571
Better result with combined features.
Ftp,fp
Ftp,fp
Favg
on Log

union
intersection
Introducing automatic selection in conceptual vector representation
29
. . .
K-Folds with feature selection or
annotated concepts
Possible alternative concept vector representation of EMRs by union or intersection of
selected concepts from each fold:

30
ANNOTATIONS RESULTS ON DBPEDIA - Ftp,fp
metric
Generalisation of vector concepts on +sm:
● Intersection: 0.8662 (14 concepts)
● Union: 0.8714 (51 concepts)
baseline 0.8270 0.8533 0.8491
+s 0.8221 0.8522 0.8485
+s* 0.8339 0.8449 0.8514
+s*T 0.8214 0.8492 0.8388
+s*∩ 0.8262 0.8521 0.8432
+s*∪ 0.8270 0.8467 0.8445
+s*m 0.8363 0.8547 0.8642
+sm 0.8384 0.8541 0.8689
● baseline: BOW generated from medical records.
● +s: On all text fields (selection of 14 concepts).
● +s*: Focussed on patient’s own record (selection of 14 concepts).
● +s*T: all concepts automatically extracted, focus on patient’s own
record.
● +s*∩: intersection of concepts estimated relevant by experts,
focus on patient’s own record.
● +s*∪: concepts unanimously estimated relevant by experts, focus
on patient’s own record.
● +s*m: Feature selection algorithm (Lasso) step on concepts, focus
on patient’s own record.
● +sm: Feature selection algorithm (Lasso) step on concepts, focus
on all text fields.
Best results achieved with machine annotations on all text fields.

Favg
OF ANNOTATED FEATURES FROM DBPEDIA UNDER Log
31

Correction of dependent Student’s t test (Nadeau and Bengio (2003)):
● Comparison of the different statistical tests (Demšar (2006))
● Features sets compared: baseline vs. other on LR.
● Alpha at 0.05.
● Critical values: 1.83 (F1) / 1.86 (AUC).
● Couple t-value/p-value.
STATISTICAL COMPARISON OF THE RESULTS - T TEST
32
Features set
t-value/
p-value
(on F1)
t-value/
p-value
(on AUC)
+wa -1.06/0.32 0.11/0.92
+t+s+c2
+wa+wi -0.47/0.65 0.02/0.98
+t+s*+c2
+wa+wi -0.52/0.62 0.10/0.92
+t+c2
+wa+wi -0.69/0.51 -0.16/0.87
+sm -1.57/0.151 -0.77/0.46
+sm∩ -1.62/0.139 -0.58/0.58
+sm∪ -2.23/0.05 -0.81/0.44
Given two sets A and B of length n:
● xj
= Aj
- Bj
● n2 is the number of testing folds, here 1.
● n1 is the number of training folds, here 9.
● σ² is the sample standard deviation on x.
T test on +sm∪ rejects the null hypothesis.
To validate it on the other features sets we should relaunch X times the experiments.

33
OVERALL RESULTS & DISCUSSION
Concepts involved in the hospitalization prediction (among the 51 selected concepts of +sm):
Source Concept Concept (translated)
Generic knowledge Terme médical Medical terminology
Patient’s mental state
Antidépresseur, Dépression
(psychiatrie), Psychopathologie,
Sémiologie psychiatrique, Trouble de
l’humeur
Antidepressant, Major depressive disorder,
Psychopathology, Psychiatric assessment,
Mood disorder
Infectious disease
Infection ORL, Infection urinaire,
Infection virale, Virologie médicale
ENT infection, Urinary tract infection, Viral
Infection, Clinical virology
Cardiovascular system
Dépistage et diagnostic du système
cardio-vasculaire, Maladie
cardio-vasculaire, Physiologie du
système cardio-vasculaire, Signe
clinique du système-cardiovasculaire,
Trouble du rythme cardiaque
Screening and diagnosis of the
cardiovascular system, Cardiovascular
disease, Physiology of the cardiovascular
system, Clinical sign of the cardiovascular
system, Cardiac arrhythmia
Family history Diabète, Terme médical Diabetes, Medical terminology
Use of technical terminology in a situation involving a complex medical case.
Semi-supervised step allows to retrieve the most relevant concepts.

THESIS OVERVIEW
34
IV. Conclusion

35
THE PHYSICIAN’S NEEDS
General practitioner:
● Preserve and/or improve her patient’s health and autonomy.
● Predict as rapidly an easily as possible the probability of
hospitalization of her patient.
● Avoid hospitalization to her patient as far as possible.
Example scenario:
Dr. Nathalie predicting the hospitalization of her
patient Patrick (57 y.o.).

36
USE OF HEALTH PREDICT BY THE PHYSICIAN (1)
(Once connected to Health Predict
through a plugin directly integrated
into one’s consultation software)
To decide if patient Patrick needs to
be hospitalized, Dr. Nathalie:
1. checks the 5 Patrick’s risk
factors that she can act upon in
order to reduce the
hospitalization probability.
2.
3. selects the two ﬁrst risk
factors (those with the most
impact) for a treatment. In A,
she observes the decrease in
the risk of hospitalization and in
B she sees that the decrease in
risk is 17%.
1
2
1
2
A
A
B
B

37
USE OF HEALTH PREDICT BY THE PHYSICIAN (2)
To decide if patient Patrick needs to
be hospitalized, Dr. Nathalie:
1. checks the other Patrick’s risk
factors in order to avoid side
effects or contraindicated
treatments.
2.
3. identifies that some
antidepressant can be
contraindicated with the
patient’s condition (atrial
fibrillation -risk factor identified
in -, pacemaker, paroxysmal
tachycardia).
3
3
4
4
1

38
THE PATIENT’S NEEDS
Patient:
● Preserve and/or improve one’s health and autonomy.
● Avoid one’s hospitalization as far as possible.
Example scenario:
Patient Patrick (57 y.o.) negotiating the hospitalization
preventing treatment with Dr. Nathalie.

39
USE OF HEALTH PREDICT BY THE PATIENT
(The physician Dr. Nathalie shows up
her monitor with the Health Predict
plugin to her patient Patrick. She
wants her patient to stop smoking,
drinking. She also plans to deal with
her patient’s depression)
● Patrick doesn't feel ready to
quit smoking and drinking at
the same time.
● However, he may make an
effort to stop smoking and can
be attended by a professional
(depression and drinking).
● He notices that these actions
would make him -28% less
likely of being hospitalized.
A
A
B
B

THESIS OVERVIEW
40
IV. Conclusion

CONCLUSION
41
● We have shown that the injection of domain knowledge succeeds in
improving the prediction of hospitalization. The +sm∪ approach for which
the t-test rejects the null hypothesis.
● We proposed a semi-supervised process for selecting relevant concepts
from knowledge graphs for a given task.
● Deﬁnition of physicians’ needs and construction of an interface adapted to
some of their needs that exploits the fruit of our research in AI. The
usability tests of our interface still needs to be carried out.

LIMITS & FUTURE WORK
Vector representation:
● Coupling of semantic relationship and text data.
Additional medical domain knowledge:
● Integration of other annotators on Wikidata and on
domain speciﬁc knowledge graphs.
Address the weaknesses of the approach using DBpedia (free text):
● Better management of abbreviations / spelling mistakes.
● Use of different level of hierarchy.
● Detect the negation and the context of a medical concept.
● Identify complex medical expressions.
Selection of concepts on combined features. 42

AREA FOR FUTURE RESEARCH
Data related:
● Inclusion of biological values.
Personalized treatment:
● Suggestions of the most suitable treatment for the patient.
Consideration of other health issues:
● Study the risks associated with rehospitalization,
cardiovascular diseases, mental illness...
● Hospitalization risk related to Covid-19.
43

Thank you for your attention.
● Gazzotti, R., Faron-Zucker, C., Gandon, F., Lacroix-Hugues, V. and Darmon, D., 2019, January. Évaluation des améliorations de
prédiction d'hospitalisation par l'ajout de connaissances métier aux dossiers médicaux. In EGC 2019-Conférence Extraction et
Gestion des connaissances 2019.
● Gazzotti, R., Faron-Zucker, C., Gandon, F., Lacroix-Hugues, V. and Darmon, D., 2019, June. Injecting Domain Knowledge in
Electronic Medical Records to Improve Hospitalization Prediction. In European Semantic Web Conference (pp. 116-130). Springer,
Cham.
● Gazzotti, R., Noual, E., Faron-Zucker, C., Gandon, F., Giboin, A., Lacroix-Hugues, V. and Darmon, D., 2019, July. Interface d'Aide à la
Décision pour Prédire l'Hospitalisation de Patients et planiﬁer les Actions Préventives pour Prévenir cet Événement.
● Gazzotti, R., Faron-Zucker, C., Gandon, F., Lacroix-Hugues, V. and Darmon, D., 2020, March. Injection of automatically selected
DBpedia subjects in electronic medical records to boost hospitalization prediction. In Proceedings of the 35th Annual ACM
Symposium on Applied Computing (SAC ’20).

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