JMIR MEDICAL INFORMATICS

Ammar & Shaban-Nejad

Original Paper

Explainable Artificial Intelligence Recommendation System by
Leveraging the Semantics of Adverse Childhood Experiences:
Proof-of-Concept Prototype Development
Nariman Ammar, PhD; Arash Shaban-Nejad, MSc, MPH, PhD
University of Tennessee Health Science Center - Oak Ridge National Laboratory, Center for Biomedical Informatics, Department of Pediatrics, Collegeof Medicine, Memphis, TN, United States

Corresponding Author:
Arash Shaban-Nejad, MSc, MPH, PhD
University of Tennessee Health Science Center - Oak Ridge National Laboratory, Center for Biomedical Informatics
Department of Pediatrics, College of Medicine
Memphis, TN
United States
Phone: 1 901 287 583
Email: ashabann@uthsc.edu

Abstract
Background: The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although
the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build
a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence
techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility
issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting
credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with
explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence,
thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well
as semantic reasoning and causality models is a potential solution to this problem.
Objective: In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype fora knowledge-driven evidence-based recommendation system to improve mental health surveillance.
Methods: We used concepts from an ontology that we have developed to build and train a question-answering agent using theGoogle DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation
and recommendation components that leverage third-party graph technology.
Results: To showcase the framework functionalities, we here present a prototype design and demonstrate the main features
through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee.
Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporatinga privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of theimplementation.
Conclusions: This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability
to provide explanations for the decisions they make.
(JMIR Med Inform 2020;8(11):e18752) doi: 10.2196/18752
KEYWORDS
mental health surveillance; semantic web; knowledge-based recommendation; digital assistant; explainable artificial intelligence;
adverse childhood experiences

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Introduction
BackgroundThe concept of adverse childhood experiences (ACEs) has beenrecognized for quite some time but was first formally studied
in the CDC-Kaiser landmark study [1], which uncovered thestrong connection between ACEs and the development of risk
factors for different negative health outcomes that threaten thewell-being of populations throughout their life course. Social
determinants of health (SDoH) are measurable indicators ofsocial conditions in which a patient is embedded. Individuals
who experience a more negative burden of these factors within
their neighborhood are at higher risk of negative health outcomes[2-4]. There is an entire body of research focused on studying
the links between ACEs and SDoH and health outcomes, but
few intelligent tools are available to assist in the real-timescreening of patients and to assess the connection between ACEs
and SDoH, which could help to guide patients and families toavailable resources (eg, health care providers, government, andnongovernment agencies). Other recent works have focused on
developing question-answering (QA) systems for training
nursing practitioners on how to answer patient inquiries [5].
Recommendation systems and digital assistants often require
machine learning (ML), artificial intelligence (AI), and natural
language processing capabilities to effectively connect andharvest the vast amounts of generated data. They also need tostore, retrieve, and learn from past interactions and experienceswith users. Traditionally, recommendation systems have reliedon classic ML techniques that often cannot provide a fullscientific understanding of the inner workings of the underlying
models. For AI to mimic human intelligence, it needs toincorporate one of the most important classes of information
people use to make predictions and decisions: context. Althougha standard AI algorithm can learn useful rules from a training
set, it also tends to learn other unnecessary, nongeneralizable
rules, which may lead to a lack of consistency, transparency,
and generalizability. Explainable AI is an emerging approachfor promoting credibility, accountability, and trust in
mission-critical areas such as medicine by combining ML
techniques with explanatory techniques that explicitly show
why a recommendation is made. One way to achieve this is byhttps://medinform.jmir.org/2020/11/e18752/

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Ammar & Shaban-Nejad
considering formal ontologies as an integral part of the learning
process, providing the necessary contextual knowledge abouta phenomenon. AI and ML become more trustworthy when
underpinned by contextual information provided by ontologicalplatforms. Ontologies can be represented through a graphstructure. When computations are performed overgraph-structured data, they can make generalizations related tostructure rather than data since they support relational reasoning
and combinatorial generalization [6]. Moreover, when AI apps
are based on contextually aware and dynamic backends, they
become easier to train with minimal maintenance. A knowledge
graph [7] can serve as a dynamic backend that stores data in acertain domain as entities and relationships using a graph model,
which abides by an ontology. Several studies have incorporated
graph technologies into ML models applied to biomedical
informatics problems [8-11].
We here propose the Semantic Platform for Adverse Childhood
Experiences Surveillance (SPACES), an intelligent
recommendation system that employs ML techniques to help
in screening patients and allocating or discovering relevant
resources. The novelty in the approach lies in its ability to use
the contextual knowledge collected about the user, and infer
new knowledge to support subsequent QA and resource
allocations during intake assessment sessions in (near) real time.
Moreover, our proposed system intends to build rapport withpatients by generating personalized questions during interviews
while minimizing the amount of information that needs to becollected directly from the patient.
In our previous work [12-14], we developed the Adverse
Childhood Experiences Ontology (ACESO) that captures
knowledge on ACEs, SDoH, health outcomes, and interventions.
Both expressive and light versions of the ontology can be freely
downloaded via BioPortal [15]. The ontology defines concept
and property hierarchies, and encodes causal epidemiologicalknowledge as axioms. We also developed a repository termed
Urban Population Health Observatory (UPHO), which provides
metrics based on several socioeconomic and environmental dataat the neighborhood level that can be linked to health outcomes.
Figure 1 demonstrates the multiple layers, from data to the app,that are required for building a knowledge-based explainable
and interpretable model.

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Figure 1. Multilayer representation of a knowledge-based explainable/interpretable model.

ACESO
There are two types of knowledge captured in the ontology: (1)domain concepts, in which the ontology encodes concepts aboutrisk factors, including ACEs (eg, abuse), SDoH (eg, housing
condition), health outcomes such as chronic diseases (eg,asthma) and stress, and interventions; and (2) semantic
inference, in which some of the knowledge in the ontology is
encoded in the form of axioms. The axioms express knowledge
related to (1) inclusions that define how two concepts are
related; (2) equivalence relationships; and (3) causal knowledge,
which are statements in the form of assertions of links between
risk factors and health outcomes (eg, “obesity is a risk factor of
diabetes” and “stress and exposure to toxins are risk factors forasthma”).

Knowledge-Based UPHOThe UPHO is a knowledge-based repository that can be used
to represent and infer neighborhood-level indicators (eg, blight
prevalence, poverty, education, proximity to clinics, proximity
to public transportation) that may lead to negative healthoutcomes (eg, asthma, diabetes, stress, obesity). Moreover, the
UPHO provides an analytics layer for calculating several metrics(eg, affinity scores in chronic conditions as a measure forcomorbidity [16]) from analyzing neighborhood data.

Objectives

mental health surveillance. We first present our methodology
along with the general architecture of the proposed
recommendation system. We then demonstrate the feasibility
and usability of our approach through multiple use casescenarios and offer recommendations for further development.

Methods
Platform Design
The idea behind the SPACES platform is to monitor the causesof ACEs and SDoH, and their impacts on health. This platform
is based on the ACESO to provide the contextual knowledge
needed to facilitate intelligent exploratory and explanatory
analysis. Through this framework, decision makers can (1)identify risk factors, (2) integrate and validate ACEs and SDoH
exposure at individual and population levels, (3) and detect
high-risk groups. The idea for implementing therecommendation system for surveillance of ACEs was motivatedby a study conducted under the Family Resilience Initiative(FRI) at Le Bonheur Children’s Hospital (Memphis, Tennessee)that serves families with children during regular child visits inthe clinic. Our use case scenarios to demonstrate the main
components and features that constitute the SPACES framework
were inspired by client examples and typical issues (Textbox1), and the follow-up activities (Textbox 2) reported in the FRI
reports.

We aimed to develop a knowledge-driven evidence-based
recommendation system and a digital assistant to facilitatehttps://medinform.jmir.org/2020/11/e18752/

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Textbox 1. Typical adverse childhood experiences (ACEs)- and social determinants of health (SDoH)-related risk factors that arise in Family Resilience
Initiative reports.
ACEs•

Child behavioral issues•

Child developmental health

SDoH•

Housing•

Food insecurity•

Transportation•

Education•

Legal/benefits

Textbox 2. Typical follow-up activities in Family Resilience Initiative reports.
Well-being check-in
Following up on a referral
Renewal inquiry
Client assistanceContact resources on behalf of a client
Sharing information about future training
Confirming appointment (psychological, clinical, education, legal)
Arranging transportation
Scheduling appointment (psychological, clinical, education, legal)

System Architecture
The main components of the SPACES framework are illustrated
in Figure 2. A detailed explanation of each component isprovided below.
Figure 2. Architecture of the Semantic Platform for Adverse Childhood Experiences (SPACEs) that reflects the main components of the system. The
blue components are the services that we implement for the mental health domain, and the green components reflect general components that can begeneralized to any other domain. QA: question-answering; ACEs: adverse childhood experiences; SDoH: social determinants of health.

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JMIR MEDICAL INFORMATICSQA Conversation Agent
To implement the conversation agent, we used the Google
DialogFlow framework [17] and defined the following
constructs.

Intents
An intent represents the purpose of a user’s input. We start bydefining a set of intents and supplying those with trainingphrases. This trains the conversation agent on detecting an intent
based on values (eg, mold) that represent entity types (eg,@housing_circumstance) tagged in the text. For our task, wedefine a generic FRI_Assessment intent that is detected when
the user enters a text similar to a training phrase. This intent
has the following child intents corresponding to different risk
factors (eg, housing, food, transportation) and follows up
associated activities.
The SDoH_surveillance intent is detected whenever the text
has phrases related to entity types that match SDoH-related
concepts in the ontology. For instance, the entity typehousing_circumstances is detected whenever the entity values

Ammar & Shaban-Nejad“mold,” “lead-based paint,” “inadequate heating,” and others
are tagged in the text. Since all of these concepts are
SDoH-related risk factors, the SDoH_Surveillance follow-up
intent is also detected.
The ACEs_surveillance intent is detected whenever the text
includes phrases related to entity types that match ACEs-related
concepts in the ontology.
The FRI_followup_activity intent is triggered whenever anaction is detected in the text (eg, schedule an appointment).

Entity Types
Entity types are ontological concepts that dictate how data are
extracted from the user’s raw text. For instance, the entity typehousing_circumstance is detected whenever the entity values“mold,” “lead-based paint,” “inadequate heating,” and others
are tagged in the text. We load entity types from the ontology
into the agent, and then enhance the agent with a minimal setof training phrases to enable it to tag entity types that appear in
the phrases. Figure 3 shows a sample training phrase taggedwith entity types.

Figure 3. A sample training phrase and detected contextual parameters with entity types and values.

Contextual Parameters
Parameters are structured data extracted from raw text and havetypes that correspond to the entity types defined in the ontology.
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parameters from entity type values (eg, 38103=zip code; night
terror=symptom) that appear in the user expression (Figure 3).
The agent then uses those parameters to perform logic andgenerate responses, or they can be exchanged between different
contexts to control the conversation flow.
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Contexts
To keep track of the conversation flow, the agent maintains all
active contexts (see connection arrows in Figure 4) on a stack
to make sure they remain active throughout the conversation.
At each point in the conversation, either a new intent or afollow-up child intent is detected due to a user’s input or an
event and fulfillment configurations within those intents. There
are different types of contexts: (a) detected intent contexts,

Ammar & Shaban-Nejad
which are triggered by training phrases; (b) follow-up intent
contexts, which are triggered by their parent intent or whenever
the parent intent is triggered for fulfillment; (c) slot-filling
context, which becomes active when the user does not provide
values for preconfigured mandatory parameters; and (d)input/output contexts, in which intents can be configured with
input and output contexts. A parent intent is by default an input
context for its child intent and a follow-up intent is an output
context for its parent.

Figure 4. Sample conversation flow to demonstrate the different constructs used to define the question-answering (QA) agent, including intents,
contexts, events, fulfillments, and webhooks. ACEs: adverse childhood experiences; UPHO: Urban Population Health Observatory; FRI: Family
Resilience Initiative; SDoH: social determinants of health.

Fulfillments and Webhooks
Fulfillments and webhooks enable the agent to invoke external
service endpoints and send dynamic responses based on user
expressions as opposed to hard-coding those responses.
Fulfillment for an intent can be enabled by setting up a webhook,which is a service endpoint that we create and host. The agent
sends a webhook request message that contains information
about the matched intent, action, parameters, and response
defined for the intent to one of our webhook services. The
webhook service performs actions as needed (eg, query theknowledge graph or invoke external application programming

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interfaces [APIs]). The service then sends a webhook response
message to the agent, which sends it to the end user.

EventsAn intent could be detected either by a phrase in a user’s text
or by being configured for an event. Fulfillments can be used
to invoke external APIs. When the agent receives a webhook
response (from a backend API) that includes an event, it
immediately triggers the intent in which that event is defined.
Figure 5 illustrates a sample conversation flow that shows how
intent detection and context activation occur, and how thedifferent entities within the QA agent communicate

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Figure 5. Two scenarios for conversation flow. (a) Scenario 1: client does not provide much detail and is prompted with mandatory parameters asked
in a certain order. (b) Scenario 2: The client provides details for mandatory parameters. Since the user’s text contains lead-based paint, the agent makesa hypothesis that a household member might need an early diagnosis for asthma based on the fact that lead-based paint is a toxicant and that exposure
to toxicants may lead to asthma. The parameter values get substituted at run time and more intents get detected while active contexts remain and newcontexts get added. To keep track of where the user is in the conversation, the agent keeps track of all active contexts on a stack.

The user types or says an expression, which might be either
detailed (eg, “I am an African American female and I havehousing issues”) or vague (eg, “I have several issues”). The
presence or lack of details triggers different contexts. In either
case, the agent matches the user expression to the generic
FRI_Assessment intent, which is configured with a fulfillmentthat enables the agent to send a webhook request to one of thewebhook services (ie, ACEs surveillance, SDoH surveillance,
recommendation service). In the case of vague user input, theagent detects a slot-filling context by prompting the user withextra questions until they have provided values for all required
parameters. In the case of a detailed user input, the agent directly
moves to a follow-up intent, which might as well be configured
with a fulfillment. After filling values for contextual parameters,
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the agent sends a webhook request to the recommendation
service. The service responds with a webhook response that
includes either a resource or a follow-up question based on theknowledge graph. A phrase provided by a user may containparameters (eg, zip code), actions (eg, schedule an appointment),or priority words (eg, “I am interested in furthering myeducation, but would prefer a job first”). If a user’s input
includes more than one issue, then both follow-up intents aredetected, but the agent handles them one at a time either basedon the order they were mentioned or by using priority words.
Since the FRI_Assessment is configured with a fulfillment, theagent proceeds as though the end user initiated the match for
the FRI_followup_activity intent. Thus, instead of responding
to the user for the FRI_Assessment intent match, the agent
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triggers the FRI_followup_activity intent, which is configured
for the event “schedule an appointment.” Finally, the
FRI_followup_activity intent handles the required parameters(date, duration, time, and appointment type) and fulfillment (eg,Google calendar API) as dictated by the configuration of
FRI_followup_activity intent.

SDoH Surveillance Service
The SDoH surveillance service is triggered whenever an
SDoH-related entity type is introduced in the user’s conversation
with the QA agent. To achieve this, the surveillance uses the
ACESO to infer whether a detected entity type (eg,being_exposed_to_lead-based_paint) is a subtype of the moregeneric SDoH type as well as the causal knowledge (eg,lead-based_paint causes asthma). This service also invokes the
UPHO service endpoint to obtain metrics (eg, blight prevalence,
walkability score) based on the current user’s neighborhood.

ACEs Surveillance Service
This service keeps track of all possible questions that can be
asked during the ACEs assessment process [18]. It is triggeredeach time an ACE-related entity type is introduced in the user’s
conversation with the agent. It gets invoked by therecommendation service to retrieve only the questions relating
to the ACEs concepts provided by the agent. The QA agent

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keeps track of how many ACEs questions were reported as
positive, and provides those to the ACEs surveillance service,
which uses the knowledge stored in the ACESO about ACEsscore classification and rules for calculating them. It then sends
the resulting score to the recommendation service, which seeks
insights from external knowledge (eg, research publications[18]) to formulate a diagnosis based on the question: “given an
ACEs score of X and symptoms S1,…Sn, what are the likely
negative outcomes to screen for?”

Recommendation Service
To make real-time recommendations, this service instantly
captures new resource interests, ACEs, and SDoH-associatedrisk factors detected in the user’s current conversation and uses
them to incrementally refine a personalized knowledge graph.
At each stage in the conversation, the QA agent passes detected
entity types and contextual parameter values to therecommendation service. Entity types help the service determine
entry points on the knowledge graph, and contextual parameters
help refine the queries further to obtain a more personalizedversion of the graph (Figure 6). Once the personalized graph is
generated, the service supplies the QA agent with two types ofrecommendations: the next question to ask and a resource tosuggest.

Figure 6. A personalized graph is generated based on contextual parameters provided by Alice (see Scenario 4 in Textbox 3) after populating ontology
concepts with real-time contextual parameters supplied by the agent, and after enriching the graph with external common sense knowledge. The figure
illustrates how a concrete path on the graph leads to a recommendation and the metapath that can be derived from that path.

We used Neo4j graph technology [19] for generating andenriching the graph. Before populating the graph with real-time
data, we loaded ACEs, SDoH, and health outcome concepts
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from the ontology into a graph and persist them. We used the
NeoSemantics framework [19] to import the Resource
Description Framework (RDF)-based data model schemas of
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the ontology as a metagraph into a Neo4j graph. The resulting
property graph inherits the modeling limitations of the RDF,
including the lack of support for attributes on relationships.
Therefore, we enriched and fixed the raw graph after loading it
in Neo4j. To populate the graph, the service starts with a dummy
node that represents the current user (Figure 6) and thenincrementally adds the following node types.
Domain concept nodes correspond to entity types detected by
the QA agent (eg, race, age, zip code, symptom). Entity typenodes could be either (a) risk factors such as“housing_circumstance,” “food_issue,” and “household_issue,”or (b) common sense knowledge such as school age and domain
concepts in the given context (eg, age is a school age andlead-based paint is a toxicant).
Value nodes are populated with real-time values either (a) fromcontextual parameters obtained from the QA agent (eg, “38103,”“night terror”), (b) calculated based on the ontological axioms(eg, ACEs score of 4), or (c) retrieved from the UPHO (eg, 80%
blight prevalence).
Recommendation nodes are question nodes and resource nodes.
Question nodes represent follow-up questions suggested for the
QA agent. The answers to these questions coming from the QA
agent can further refine the graph. The suggested question nodes
are solicited from a pool of questions kept by the ACEs
surveillance service. Resource nodes (eg, referrals, housing
options, school information, doctors, clinics) are resourcessuggested for the QA agent to provide to the user. These
resources are suggested based on the user’s demographic profile(eg, the food banks within a small radius from their zip code).
Resource nodes are pulled from a pool of existing resources orby invoking external backend APIs. In addition to the nodes
pulled from contextual parameters, the graph shows nodes thatare added based on knowledge inferred through axioms (dashedarrows in Figure 6) and concept hierarchies (solid arrows in
Figure 6).

Population Health and Policymaking Analytics
The analytics component utilizes the conversation history
recorded by the DialogFlow logging API, including (1)timestamps, number of interactions (within a conversation) for
all user sessions, and percentage of mismatches if any; (2) avisual summary of the conversation flow with percentages forhttps://medinform.jmir.org/2020/11/e18752/

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Ammar & Shaban-Nejad
each detected intent as well as the conversational paths thatusers have taken when interacting with an agent; (3) popularity
per intent by showing the number of sessions in which the intent
was matched as well as the number of times the intent was used
(total from all sessions); (4) percentage of sessions in which auser exited the conversation in the specified intent comparedto the total number of sessions in which this same intent wasmatched; and (5) average response time to user requests.
The aggregated results from all user conversations provide
policymakers with insights about population health. Analyzing
such data can help in designing interventions and preventive
measures based on the most prevalent risk factors in certain
regions. It can also assist the framework users by providing
recommendations on how to direct future conversations. For
example, it can look into smaller communities within geographic
areas or perform collaborative filtering based on similarities inuser behavior, or detect similar communities on the conversationgraph. For example, if a user that belongs to a certain age or
ethnicity group shows a certain pattern/route during the caseassessment conversation procedure, then the QA agent may
suggest the same route for the next user with similar criteria.

Results
We describe the main features provided by SPACES througha proof-of-concept prototype that will render the informationcollected by the QA agent and the recommendation service on
a user-friendly interface. The prototype is intended for several
types of users, including caregivers (eg, child-parents) or healthcare professionals (eg, nurses, physicians, social workers). The
main features of the view that would be available to a healthcare professional are illustrated in Figure 7, including
recommendations for digital assistance (A), studying theassociation between ACEs and SDoH (B.2 and B.5), knowledge
graph querying (B.3), geocoded resource recommendation (B.6),
and explainability by displaying inference sources (B.5 andB.7). The QA agent view that would be available to a caregiveris illustrated in Figure 5 and in panel A of Figure 7.
We present multiple use-case scenarios in Textbox 3. For
simplicity, we use Scenario 4 to demonstrate how the QA agent
detects context and lays it over to the recommendation service,and how the recommendation service uses contextual parameters
to generate a personalized knowledge graph.

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Figure 7. Prototype of the recommendation system. (A) Question-answering (QA) view (1) send/revise suggested questions. (B) Health care practitioner
assessment panel, including (2) social determinants of health (SDoH) detection, (3) knowledge graph and queries, (4) pool of previously asked questions,(5)alerts for detected adverse childhood experiences (ACEs) symptoms, (6) geocoded resource allocation, (7) explain recommendations, (8) and visual
analytics.

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Textbox 3. Four use case scenarios.
Scenario 1: Assessing needs relating to SDoH. SDoH: social determinants of health; N/A: not applicable; ACEs: adverse childhood experiences;
UPHO: Urban Population Health Observatory.
“I am currently residing in a safe place, but I’m concerned about my household income as I am currently unemployed due to legal issues. I have somecollege and I am interested in furthering my education, but would prefer a job first.”
Symptoms: N/A
Risk factors: ACEs detected: Unemployed due to legal issues (Legal, Textbox 1)
I’m interested in furthering my education (Education, Textbox 1)
Outcomes: N/AIntervention: Information on how to get into the school
Follow up on legal issues.
Scenario 2: Assessing issues relating to ACEs
“My husband is an alcoholic and he had served time in jail and right now it is hard to soothe my 4-year-old baby boy or calm him down. He also
bounces back quickly when things do not go his way. This just puts a lot of pressure on me”
Symptoms: Hard to soothe or calm down (behavioral)
Bounces back quickly (behavioral)
Risk factors: ACEs detected: living in a household with substance abuse
ACEs detected: living with a household member who was in jail
Outcomes: provided (stress)
inferred (asthma)
Intervention: Therapeutic (schedule psychologist appointment)
Scenario 3: Mixed model of ACEs and SDoH
“I have a couple of issues. My 7-year-old son is developmentally delayed, and we have food insecurities that we hope we could resolve before theholidays. But I am mostly concerned about food.”
Symptoms: Child developmental delay (behavioral)
Risk factors: food insecurities
Outcomes: N/AIntervention: Provide information about food pantries
Schedule psychologist appointment
Scenario 4: Detecting risk factors for potential negative health outcomes and providing early diagnosis“I am a Hispanic 21-year-old female living in Memphis. My 6-year-old child experiences night terror. I have recently separated from my husband.”
Symptoms: Night terror (emotional neglect causes night terror)
Risk factors: ACEs detected:
provided (living in a household of divorce)
inferred (ontology, emotional neglect)(UPHO, a neighborhood with blight)
Outcomes: inferred (asthma)
Intervention: Therapeutic (schedule a medical appointment)

Based on Scenario 4, Alice is a Hispanic female located in acity with zip code 38103 and has a 6-year-old child who suffersfrom night terrors. Alice can either provide vague (Figure 5a)or detailed (Figure 5b) text. Either way, the agent collects as
many contextual parameters as possible and then passes themover to the recommendation service.
The service starts building the personalized graph for Alice byadding a node labeled zip code and has_value 38103. It thenhttps://medinform.jmir.org/2020/11/e18752/

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links the node to all concepts related to a zip code, including
neighborhood and physical characteristics. The physicalcharacteristics are then linked to all related concepts based on
the ACESO, including blight, walkability, and crime, and it
populates these nodes with values from the UPHO. If blight has
the maximum value, it links the blight concept with related
concepts based on the axiom (blight causes exposure to toxicantsand exposure to toxicants leads to asthma). Thus, it links it to
the toxicant and asthma nodes. Additionally, it adds the age
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node and populates it with a contextual parameter value of 6,
and links the age concept to the school-age concept. It then adds
the symptom node and links it to night terror based on theconcept subtype relation in the ontology. It then links night
terror to emotional neglect based on an axiom. The inference
path in Alice’s scenario is shown on the graph in red in Figure
6 to illustrate how Alice’s case leads to a recommendation for
pediatric asthma physicians. The metapath, derived from that
concrete path, can be used in future recommendations if an
individual is encountered with similar contextual parameters.
The resulting graph is a property graph in that both nodes andrelations have properties. For instance, the inferred relations(eg, shouldBeScreenedFor asthma, has an ACEs score of 4, andlivesIn a neighborhood with 80% blight prevalence) can belabeled with the source of inference, confidence probability,
severity, and type of intervention. Sources of knowledge
inference appear in yellow boxes at the bottom of Figure 6,
which include UPHO, research papers, the ACESO, and others.
We can enrich the graph further with a clinic locator graph ofphysicians who can provide prescriptions for pediatric asthma,
and then filter the resource nodes further based on provided
contextual parameters. For instance, we can keep only clinics
that fall within a small radius from Alice’s residential zip codeand that provide Spanish language–speaking services based onher ethnicity.
The health care practitioner can observe the inferred knowledge
through several features rendered on the prototype interface asfollows: (1) ACEs alerts (eg, Alice’s case indicates an ACEsscore of 4, night terror as a symptom, and suggests an early
diagnosis of asthma as an outcome) (Figure 7, B.2); (2) SDoH
alerts (eg, the risk factors associated with Alice’s case) (Figure7, B.5); (3) visual aids for explainability by clicking on the (?)
icons to display links to inference sources (eg, the ontology linkon BioPortal and the identifier for the CDC-Kaiser paper)(Figure 7, B.7); (4) geocoded view of the suggested resources(eg, clinics in Memphis) (Figure 7, B.6); and (5) which questions
to ask next (Figure 7, B.4).

Discussion
Principal Findings
The significance of the proposed approach lies in its ability toprovide recommendations to the QA agent with the least effort
from both the user and the health care practitioner. It aims tomaximize knowledge about the patient without having to delve
into all of the questions that are often asked in ACEs and SDoH
intake assessments. It also provides the ability to explain why
a certain question or resource was suggested.
Preliminary rapid prototyping of the recommender systemallowed for early verification of the functionalities through themultiple case scenarios described in this paper. We anticipate
rapid feedback from end users on various features during an
iterative development process, and finally establish acomprehensive usability and user experience test. We haveevaluated the SPACES semantic framework and its underlying
ontology automatically using description logic reasoners suchas Fact++ [20] to ensure the consistency and satisfiability ofhttps://medinform.jmir.org/2020/11/e18752/

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Ammar & Shaban-Nejad
the ontology and semantic model. We also used inputs fromour collaborating domain experts to assess the soundness andcompleteness of the ontology by examining how well it has
been aligned with the required criteria defined in our domain
and scope. Furthermore, we evaluated the usability of ACESO
based on its functionality and capability to respond and answer
the target queries. Finally, further work is underway to develop
and conduct a series of formal evaluation practices for acomprehensive assessment of the accuracy, usability, coverage,
confidence, trustability, as well as adaptivity and scalability of
the recommender system [21]. Moreover, we will assess theutility and impact of the system on the surveillance of ACEs togenerate a timely response and intervention, ultimately
informing public health planning and policymaking.
The proposed approach might face some limitations. One
limitation is in providing an overall guiding architecture to
support transfer ability between health domains. Several of the
QA platforms (eg, Google DialogFlow and IBM Watson) readrules on how to answer questions from backend sources (eg,HTML FAQ files, Plaintext files). These sources can help load
the questions into the QA agent. They also require training theagent with concepts that may appear in the QA text. The
ontology in our case helps load the concepts automatically,
which is separate from the QA platform implementation itself.
Each domain will have its own concepts that can be encoded
in a separate ontology, and we can either develop new ontologies
or reuse existing ones.
Another limitation is that the recommendation system has accessto only population-wide data, where the population’s
characteristics might be different than the characteristics of theindividuals living in that population or neighborhood. However,for specific users and specific use cases, it needs access toindividual data. For instance, a pediatrician trying to decide
whether a child is suffering from ACEs will need to have accessto the child’s health history and other relevant information but
should not be allowed to access information about the parent’s
finances or criminal history (outside of what is publicly
available). Moreover, a local judge will want to have access tothe criminal history of the family members if they want to decide
whether the child should be removed from their parents to ensure
their safety, but they should not be able to access any of their
medical records. Thinking about how to control mediated accessto sensitive information will be a key part of the developmentof the recommendation system. We are currently working on
integrating the recommendation system into a personalized
health library [22].
The adoption of recommendation systems may be hindered bya poor user-interface design or poor integration into clinicalworkflows. Human factors engineering can improve efficiency,
reduce errors, increase technology adoption, and reduce theearly abandonment of systems. This paper lacks an objective
evaluation of how health care practitioners will benefit fromthe explanations or how the quality of those explanations would
be assessed. We plan to use an iterative user-centered design
and formative evaluation by conducting predevelopment focus
groups, which might reveal issues related to manual data entry
from target users and the time spent reviewing generated
knowledge from providers. Ongoing research on this project
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JMIR MEDICAL INFORMATICS
will also involve implementing an optimization algorithm of
the recommendations. We will also consider technicallyevaluating the system for precision and performance.
All updates in the underlying ontologies and semantic structure
will be managed through our previously implemented framework[23, 24]. As for the target audience, the system is intended fora variety of users depending on the domain, including social
workers, health care providers (eg, nurses, physicians), andcaregivers (child-parent). The digital assistant or QA agent is
the part intended and available for end users (eg, caregivers).
It is a simulation of the conversations that occur between healthcare providers or social workers and caregivers as they discuss
their case. The text exchanged through the chat is used in thebackend for both enhancing the QA agent and refining thepersonal knowledge graph of the current user. The visual graphs
and underlying reasoning are intended for AI explainability,
which is critical for health care providers to make decisions. In
particular, the analytics part is intended for monitoring andresearch purposes, and therefore is more appropriate for healthprofessionals and policymakers.
Finally, we discuss the implications for policy and practice. We
believe that early intervention is the best way to prevent theAmmar & Shaban-Nejad
progression of negative health outcomes to their end stage, andthat well-designed early detection systems can aid clinicians bygenerating knowledge that can be aligned with clinicalworkflows. Thus, systems tailored toward such interventions
by utilizing knowledge about self-reported or detected SDoH
and ACEs would be most useful. Through the framework,
decision makers can (1) identify risk factors, (2) integrate andvalidate ACEs and SDoH exposure at individual and populationlevels, and (3) detect high-risk groups. The analytics component
could be most useful for policymakers for this purpose and isintended for monitoring and research purposes.

Conclusions
In this study, we leveraged explainable AI to present a
proof-of-concept prototype for a knowledge-driven
evidence-based recommendation system to improve thesurveillance of ACEs. The proposed approach will enhance thehealth care practitioner’s ability to provide explanations for thedecisions that they make. Further development and official
evaluation are underway to include a privacy layer through apersonal health library and to conduct a clinical trial for formallyassessing both the usability and usefulness of theimplementation.

Acknowledgments
We would like to thank Dr Robert L Davis, Dr Jonathan A McCullers, Dr Jason Yaun, Dr Sandra R Arnold, and the entire teamat the Family Resilience Initiative at Le Bonheur Children’s Hospital, Memphis, Tennessee, for their support and insights. This
research was partially supported by the Memphis Research Consortium.

Conflicts of Interest
None declared.

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Abbreviations
ACEs: adverse childhood experiences
ACESO: Adverse Childhood Experiences Ontology
AI: artificial intelligence
API: application programming interface
FRI: Family Resilience Initiative
ML: machine learning
QA: question-answering
RDF: Resource Description Framework
SDoH: social determinants of health
SPACES: Semantic Platform for Adverse Childhood Experiences Surveillance
UPHO: Urban Population Health Observatory

Edited by J Bian; submitted 16.03.20; peer-reviewed by SA Chun, M Mulvenna; comments to author 22.06.20; revised version received
25.08.20; accepted 08.10.20; published 04.11.20
Please cite as:
Ammar N, Shaban-Nejad A
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences:
Proof-of-Concept Prototype Development
JMIR Med Inform 2020;8(11):e18752
URL: https://medinform.jmir.org/2020/11/e18752/
doi: 10.2196/18752
PMID: 33146623
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Ammar & Shaban-Nejad©Nariman Ammar, Arash Shaban-Nejad. Originally published in JMIR Medical Informatics (http://medinform.jmir.org),
04.11.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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