=Paper=
{{Paper
|id=Vol-2317/article-05
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2317/article-05.pdf
|volume=Vol-2317
|dblpUrl=https://dblp.org/rec/conf/semweb/GyrardGTSS18
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==None==
https://ceur-ws.org/Vol-2317/article-05.pdf
Personalized Health Knowledge Graph?
Amelie Gyrard1? ? ? , Manas Gaur1 , Saeedeh Shekarpour2 , Krishnaprasad
Thirunarayan1 , Amit Sheth1
1
Knoesis, Wright State University, USA
2
University of Dayton, USA
{amelie,manas,tkprasad,amit,saeedeh}@knoesis.org
Abstract. Our current health applications do not adequately take into
account contextual and personalized knowledge about patients. In or-
der to design “Personalized Coach for Healthcare” applications to man-
age chronic diseases, there is a need to create a Personalized Health-
care Knowledge Graph (PHKG) that takes into consideration a patient’s
health condition (personalized knowledge) and enriches that with contex-
tualized knowledge from environmental sensors and Web of Data (e.g.,
symptoms and treatments for diseases). To develop PHKG, aggregat-
ing knowledge from various heterogeneous sources such as the Internet
of Things (IoT) devices, clinical notes, and Electronic Medical Records
(EMRs) is necessary. In this paper, we explain the challenges of col-
lecting, managing, analyzing, and integrating patients’ health data from
various sources in order to synthesize and deduce meaningful information
embodying the vision of the Data, Information, Knowledge, and Wisdom
(DIKW) pyramid. Furthermore, we sketch a solution that combines: 1)
IoT data analytics, and 2) explicit knowledge and illustrate it using three
chronic disease use cases – asthma, obesity, and Parkinson’s.
Keywords: Healthcare, Knowledge Graph (KG), Personalized Knowledge Graph,
Data Management, Reasoning and Integration, Contextualization, Ontology, Linked
Open Data (LOD).
1 Introduction
World Health Organization (WHO)3 estimates the number of people suffering
from asthma, obesity and Parkinson’s disease as 235 million, 650 million, and 4-6
million respectively [1]. Chronic diseases such as obesity and asthma are multi-
factorial diseases and have become epidemics of the current century according to
the WHO. Recent studies have shown the need for a variety of effective solutions
to maintain a healthy lifestyle (e.g., Hapifork4 is an electronic fork that helps
monitor and track eating) and are the basis for emerging applications such as
“Personalized Preventive Coach” and “Digital Health Advisor” that track and
interpret health and well-being data [2]. Other related areas contributing to
?
Position Paper, Copyright © 2018 for this paper by its authors. Copying permitted for private
and academic purposes.
???
Thanks to the kHealth team for fruitful discussions and feedback. This work is partially funded
by KHealth NIH 1 R01 HD087132-01 and Hazards SEES NSF Award EAR 1520870. The opinions
expressed are those of the authors and do not reflect those of the sponsors.
3
http://www.who.int
4
https://www.hapi.com/product/hapifork
2 ISWC 2018 Contextualized Knowledge Graph Workshop
these applications include Wireless Body Area Networks (WBANs) [3], medical
Internet of Things (mIoT), m-health, e-health, and Ambient Assisted Living5
(AAL). Specifically, these applications utilize: 1) inexpensive sensors to collect
raw data generated by the IoT devices (e.g., weight scale device), 2) a domain
model (e.g., asthma or obesity ontology6 ) to structure and abstract the data, and
3) a reasoning mechanism to deduce insights and recommendations (e.g., using
Body Mass Index (BMI) and environmental data with a rule-based inference
engine to provide relevant information). However, the current-state-of-the-art
lacks appropriate exploitation of: 1) contextual data, 2) personalized data,
and 3) their integration with background medical knowledge bases for
monitoring user health. In the following, we discuss these limitations.
Context-Awareness refers to the use of external data that can impact the
user’s situation. For instance, IoT devices can be used to monitor the surrounding
environment. Interpretation of IoT data using a background model for abstrac-
tion can provide contextual awareness to physicians. Clinical protocols should
take these into account to determine a patient’s condition. For example, each pa-
tient can react differently when exposed to different environmental factors (e.g.,
air pollution or pollen level).
Personalization adjusts the treatment to each patient’s condition. Patient’s
data is obtained by harnessing multi-modal data7 from clinical documents (de-
mographic information, clinician’s observations, lab tests, data collected during
clinical visits), patient generated health data including sensor and social data
(see Figure 1 for an example involving more than 25 types of data for each
patient). It permits one to tailor judgments and treatments based on patient’s
vulnerabilities, triggers, and symptoms [4]. Personalization, in conjunction with
predictive analytics, enables actionable insights. IoT devices can be utilized for
personalization. For instance, the dosage of long-term allergy medication pre-
scribed for an asthmatic patient to control the symptoms is tailored to a person’s
asthma severity, potential for environmental triggers, and the past history.
Personalized Healthcare Knowledge Graph (PHKG)8 is a represen-
tation of all relevant medical knowledge and personal data for a patient. PHKG
can support development of innovative applications such as digitalized personal-
ized coach applications that can keep patients informed and help manage their
chronic condition, and empower the physicians to make effective decisions on
health-related issues or receive timely alerts as needed through continuous mon-
itoring. Typically, PHKG formalizes medical information in terms of relevant
relationships between entities. For instance, a knowledge graph (KG) for asthma
can describe causes, symptoms and treatments for asthma, and PHKG can be
the subgraph containing just those causes, symptoms, and treatments that are
applicable to a given patient.
5
https://goo.gl/rLLYCC
6
https://goo.gl/JWyze4
7
https://www.technologyreview.com/s/426968/the-patient-of-the-future/
8
http://wiki.knoesis.org/index.php/KnoesisKnowledgeGraph
ISWC 2018 Contextualized Knowledge Graph Workshop 3
State-of-the-art of Health Knowledge Graphs. Google Healthcare Knowl-
edge Graph is a manually curated health knowledge graph that integrates ICD-9
and UMLS9 along with probabilistic machine learning and physician support to
provide relevant information upon a user search [5]. In [6], a KG is applied
to the pneumonia use case [6], by performing a contextual pruning algorithm
on knowledge graphs. DepressionKG is a disease-specific KG [7] that can benefit
representation and reasoning about Major Depressive Disorder (MDD) requiring
overcoming challenges in: 1) heterogeneity of datasets, 2) highly contextual text
processing, 3) incompleteness and inconsistency in datasets, and 4) expression,
representation, and reasoning of medical knowledge.
PHKG is one solution to achieve the vision of the Data - Information -
Knowledge - Wisdom (DIKW) pyramid. DIKW describes a hierarchical re-
lationship between Data, Information, Knowledge, and Wisdom, an example of
which has been applied to the healthcare domain in the context of managing
blood pressure [8]. At each layer of the DIKW pyramid, the contextualization
becomes finer and becomes finest at the Wisdom stage. In our study, we incor-
porate relevant domain-specific medical knowledge bases for contextualizing the
information on health diseases. We aim to design the methodology to achieve
this DIKW vision and provide a set of easy-to-use tools [9].
In this paper, we explain the following Research Challenges (RC) to
achieve this DIKW vision and to design the PHKG: (RC1) How to model a
knowledge graph for healthcare and chronic disease management? (RC2) How
to model personalization and context-awareness to understand patient’s symp-
toms and derive actionable insights? (RC3) How to analyze datasets generated
by IoT devices to deduce meaningful information? (RC4) How to promote re-
produceable experiments from previous projects (e.g., datasets, data models,
and reasoning mechanisms)? (RC5) How to customize and instantiate relevant
knowledge from existing publicly available health knowledge bases to obtain in-
sights from health-related social media text? In the next section, we provide our
vision to address these challenges through the PHKG. Then, we conclude the
paper and provide directions for future work.
2 Designing PHKG
We explain the methodology to build the PHKG in terms of: 1) its architec-
ture, 2) the use cases considered, 3) the medical datasets obtained from the
LOD cloud, 4) the reasoning mechanism to deduce high-level information from
IoT datasets, and 5) an online ontology catalog tool to reuse and share the do-
main knowledge. The architecture designed to build our PHKG is introduced
in Figure 1. PHKG uses heterogeneous sources of knowledge: 1) IoT data pro-
vided by sensors, 2) medical datasets from Alchemy API that provides access
to SNOMED-CT10 , UMLS, and ICD-1011 , 3) ontology catalogs to reuse models
(e.g., asthma ontology), and 4) a set of unified rules to interpret data.
9
https://www.nlm.nih.gov/research/umls/
10
http://bioportal.bioontology.org/ontologies/SNOMEDCT
11
https://bioportal.bioontology.org/ontologies/ICD10
4 ISWC 2018 Contextualized Knowledge Graph Workshop
kHealth project12 developed at Kno.e.sis Research Center, is a framework
for continuous monitoring of the patient’s personal data and for generating no-
tifications as needed to assist the clinicians [10]. kHealth integrates data from
three different sources: 1) Electronic Medical Records, 2) Environment using IoT
devices (e.g., Foobot) and querying Web Services (for weather data), and 3)
Personal health signals using IoT devices (e.g., Fitbit) to provide data on sleep,
activity, and heart rate, etc. The knoesis Asthma Ontology (kAO)13 integrates:
1) W3C SOSA ontology to semantically annotate sensor observations (e.g., peak
flow meter is a subclass of the sosa:Sensor class), 2) the Asthma Ontology
(AO) from BioPortal to reuse relevant concepts, 3) FOAF ontology to describe
people, and 4) weather ontology to deduce meaningful information from weather
datasets. The asthma dataset consists of data generated by IoT devices such
as peak flow meter, Foobot, Fitbit, AirNow, and from Web Services obtaining
air quality parameters14 , pollen index and type15 , outside humidity, and tem-
perature. The obesity dataset consists of data generated by IoT devices such
as weighing scale, pill and water bottle, and Fitbit to obtain parameters such
as weight, medication consumption, heart rate and sleep activity. The Parkin-
son dataset16 from Kaggle consists of mobile sensor data from accelerometer,
compass, and microphone etc. on smartphone to synthesize patient symptom
information such as unsteady walk, lacks balance, has a fall, and has slurred
speech. This information can be used to both diagnose and monitor progression
of Parkinson’s disease.
Kno.e.sis Alchemy
API17 addresses RC2 and
RC5, it identifies healthcare-
related entities, entity types,
and relations from so-
cial media text (e.g., Red-
dit) to define the con-
text. Figure 2 demon-
strates the utilization of
medical datasets such as
SNOMED-CT, ICD-10, and
Clinical Trials to achieve
entity extraction (e.g.,
cough concept is a tax-
Fig. 1. PHKG Architecture
onomy itself within SNOMED-
CT). Furthermore, SIDER
(a drug and side-effect
12
https://goo.gl/quEfjH
13
http://wiki.knoesis.org/index.php/KHealthAsthmaOntology
14
https://airnow.gov/index.cfm?action=aqibasics.aqi
15
https://www.pollen.com
16
https://goo.gl/8mBoHF
17
http://wiki.knoesis.org/index.php/Knoesis_Alchemy_of_Healthcare
ISWC 2018 Contextualized Knowledge Graph Workshop 5
knowledge base) is utilized to identify treatment, disorder, side-effects, drugs,
drug-dosage form, drug-dosage level, and adverse drug reactions using the enti-
ties and their type defined in the context.
Fig. 2. Sample annotation of entities and relations identified from Reddit Post on
r/Health subreddit. Implicit relations are discovered after 1 hop and Explicit relations
are present in 1 hop.
kHealth reasoner is a rule-based reasoning engine (an extension of the
reasoner explained in [9]18 ) which deduces meaningful insights from heteroge-
neous data provided by clinicians and patient questionnaire responses, and ob-
tained from IoT devices (as depicted in Figure 3). The reasoner addresses RC2
and RC3.
A kHealth IoT dataset is semantically annotated using an appropriate on-
tology (e.g., the asthma dataset is annotated according to the kAO ontology)
to make its meaning explicit and later deduce abstractions. The rules to sup-
port reasoning reflect domain knowledge and are mainly extracted from scientific
publications, or from web services explicitly describing the domain expertise, or
manually curated as required to interpret the data. The formalism is inspired
by the Jena inference grammar that we enrich to be compliant with a dictio-
nary of IoT devices (e.g., thermometer) and IoT observations types (e.g., outside
temperature) classified within the kAO ontology.
The execution of the rule provides
meaningful abstractions from IoT ob-
servations (e.g., high temperature)
and links the IoT data to specific do-
main ontologies (e.g., weather) from
ontology catalogs or datasets from the
LOD cloud.
Ontology Catalogs (e.g., LOV4IoT-
Fig. 3. kHealth Reasoner Framework
Health19 [9], BioPortal20 , Linked
18
http://linkedopenreasoning.appspot.com/
19
http://lov4iot.appspot.com/?p=ontologies
20
https://bioportal.bioontology.org/
6 ISWC 2018 Contextualized Knowledge Graph Workshop
Open Vocabularies (LOV)21 ) address
RC1, RC2 and RC4 because catalogs provide domain-specific knowledge already
structured to enrich the PHKG. LOV4IoT covers IoT domains (e.g., healthcare
and weather) to deduce abstractions from sensor data.
However, reusing existing ontologies is challenging. For instance, the AO
ontology22 : 1) is a taxonomy rather than an ontology, 2) has concept URIs that
are opaque for humans to decipher (e.g., AO:MOCHA-Asthma_000073), and 3)
has common pitfalls detected and explained by the OOPS ontology validation
tool (e.g., merging different concepts in the same class).
Conclusion and Future Work. Designing the PHKG is critical to achieve
Digitalized Personalized Health coaches (e.g., chatbots) to assist doctors and
patients, especially given that generic knowledge should be tailored for each pa-
tient. Designing PHKG is challenging because it requires semantic integration of
heterogeneous data from: healthcare providers, IoT devices, and the Web, taking
into account context and personal history for reasoning and deducing high-level
abstractions and effective actionable insights. PHKG can be serve as a founda-
tion for assisting physicians in understanding the symptoms, hypothesizing and
explaining disease progression, and then inferring a potential management and
treatment plan.
References
1. Anantharam, P., Thirunarayan, K., Taslimi, V., Sheth, A.P.: Predicting parkin-
son’s disease progression with smartphone data. (2013)
2. Dimitrov, D.V.: Medical internet of things and big data in healthcare. Healthcare
informatics research (2016)
3. Negra, R., Jemili, I., Belghith, A.: Wireless body area networks: Applications and
technologies. Procedia Computer Science (2016)
4. Sheth, A., Jaimini, U., Thirunarayan, K., Banerjee, T.: Augmented personalized
health: How smart data with iots and ai is about to change healthcare. In: IEEE
RTSI. (2017)
5. Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health
knowledge graph from electronic medical records. Scientific reports (2017)
6. Shi, L., Li, S., Yang, X., Qi, J., Pan, G., Zhou, B.: Semantic health knowl-
edge graph: Semantic integration of heterogeneous medical knowledge and services.
BioMed Research International (2017)
7. Huang, Z., Yang, J., van Harmelen, F., Hu, Q.: Constructing knowledge graphs of
depression. In: ICHIS. (2017)
8. Sheth, A., Anantharam, P., Henson, C.: Physical-cyber-social computing: An early
21st century approach. IEEE Intelligent Systems (2013)
9. Gyrard, A.: Designing Cross-Domain Semantic Web of Things Applications. PhD
thesis, Telecom ParisTech, Eurecom (2015)
10. Sheth, A., Anantharam, P., Thirunarayan, K.: khealth: Proactive personalized
actionable information for better healthcare. In: PDA@ IOT at VLDB. (2014)
21
http://lov.okfn.org/dataset/lov/
22
https://goo.gl/z82oXB