When we identify patients with clinically elevated symptoms, physicians can act right away. The benefits of early intervention include:

• Better short- and long-term health outcomes for children, adolescents and adults
• Better quality of life
• Earlier connections to impactful services
• Less illness later in life

Anxiety: Our Initial Focus

In our work we focus on clinical anxiety (with its clinical significant symptoms) and the corresponding DSM-5 anxiety disorder diagnoses.

For this research we developed a clinical anxiety phenotype in partnership with the clinicians at Cincinnati Children’s who treat patients with anxiety. We identified key instruments, indicators and characteristics considered when diagnosing a patient with anxiety.

The resulting set of anxiety indicators contains data from all aspects of a patient’s life, including data from the electronic health record (EHR), such as:

• Demographic data
• Diagnosis information
• Instruments/survey results
• Keywords or phrases documented in the notes
• Medications
• Patient history

Other information used to diagnose and identify anxiety are data from outside the patient’s clinical history, including:

To design a mental health trajectory, we performed three separate chart review efforts with three main goals to:

1. Identify where key information is captured for patients in structured and unstructured data areas
2. Determine if there are criteria that can be used to create clean case/control cohorts
3. Understand the processes that clinicians use to make these assessments

In June 2022, we performed our first concept design experiment. We trained a machine learning model on clinical and environmental data from 3,000 anxiety and non-anxiety patients. Then we analyzed what the model would say about individual known-anxiety and known-non-anxiety patients at different points in their medical history. This exercise allowed us to see what similar trajectory-style models may look like.

Foundational Data Lake

The team has created a foundational data lake of clinical structured and unstructured data transformed to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

Additionally, we developed geospatial linkage methods to allow for the linkage of environmental and population-level information to determine the impact certain events may have on clinical outcomes.

Our team has incorporated environmental and population data into the foundational data lake.

Our environmental datasets include:

• Daily ambient fine particulate matter
• National Walkability Index
• Neighborhood-level measures of environmental exposures related to lead paint, diesel combustion, traffic, water discharges and superfund sites
• Proximity and length of major roadways and traffic
• Satellite-based high-resolution land usage (greenspaces, urban imperviousness and development)
• Satellite-derived greenness
• Weather

Our socioeconomic datasets include:

• Child Opportunity Index
• Community Deprivation Index
• Community Resilience
• Mental Health Professional Shortage Areas
• Neighborhood Atlas
• US Department of Agriculture (USDA) Food Access data

Program Goals

The Mental Health Trajectory Program includes several scientific and administrative goals.

Our scientific goals are to:

• Create a multimodal data repository for computing trajectories
• Create methods for extracting all scientifically validated indicators of mental illness and use machine learning to discover new ones
• Create and validate trajectories of depression, anxiety and suicide
• Determine how to operationalize the results to clinical settings

Other goals include:

• Establish a framework for data collection and sharing
• Create data-sharing partnership with other regional stakeholders
• Develop new strategic partnerships

Incorporating Trajectories into Everyday Medical Practice

Machine-learning analysis of spoken language can be promising as a useful screening alternative when traditional information-gathering approaches—such as phone calls, doctor visits and school health clinics—are unwieldy.

Data from our current research suggest that using AI with larger datasets may allow us to identify anxiety and bipolar disorders.

The next step for our research is to validate the trajectories clinically and incorporate them into everyday medical practice. To achieve this, we are working on producing several deliverables:

• A platform technology that creates an integrated foundation dataset
• Validated trajectory models for anxiety, depression and suicidality
• Real-world pilots in psychiatry clinics, pediatrician offices, psychology clinics and adolescent medicine clinics
• Intellectual property for methods of data integration, trajectory computations and user experience