Although mental health is currently one of the top global health priorities, there has been no major progress in treating psychiatric disorders since the 60s. The available therapies are ineffective, and prophylaxis is practically non-existent. To find a way out of this deadlock, psychiatry needs to consider different research approaches.
One of the major
obstacles for moving psychiatry forward is the enormous inter-individual
variability. Patients with somewhat similar symptoms receive the same diagnosis,
although the symptoms might have very different biological backgrounds. This is
comparable to including migraine, hypertension and brain cancer into one
diagnostic category, because all of them are characterised by headache. As a
result, at the moment, we are blindly trying to use same treatment for a number
of different diseases. Although DSM-5* was intended to reflect the biology of
the diseases, it remains largely based on clinical symptoms. This is not going
to change any time soon, because the mechanisms underpinning mental illnesses
are still largely unknown. However, as long as psychiatric research relies on a
symptom-based diagnostic system, our studies will remain inconclusive.
A potential way to
end this impasse it to move the focus from diseases to symptoms in order to
stratify patients into more homogenous categories. We could achieve this by
applying big data approaches pioneered by the rare disease field. Automated
computer analyses of big clinical datasets can reveal tendencies that would
otherwise remain undetected, while reducing the time and effort required. By
transforming how we classify psychiatric disorders, big data can help us avoid
a lengthy process of therapeutic trial and error, and enable more personalised
treatment. In fact, this approach could push forward not only psychiatry, but also
other medical fields, such as diabetes and cancer.
data, such as medical records kept by healthcare providers, are a valuable
source of information that could be analysed using big data approaches.
However, this requires revamping the way in which information is collected and
stored. Automated analysis relies on whether data is readable for machines. To
help researchers and healthcare providers store data in an optimal way, the FAIR
(Findable, Accessible, Interoperable and Reusable) data principles (1) provide
guidance on good data stewardship practice. One of the FAIR ways is to use ontologies.
Ontologies are hierarchically organised terminology lists that allow encoding
information in a standardised way. For example, OMIM (2), which is an ontology
for genetic disorders, encodes schizophrenia as 181500. On the other hand, the
Human Phenotype Ontology (HPO) (3) focuses on symptoms, e.g. “Hallucinations”,
encoded as HP:0000738, belong to the wider category “Behavioural abnormality”,
encoded as HP:0000708. The advantage of using ontologies is that the codes are
the same even when the medical records are written in different languages or
use different words to describe the same symptom.
records contain clinical descriptions in sentences rather than in lists, so
they cannot be analysed by computers. Translating thousands of medical records
to digital ontology codes would be unfeasible, but several existing tools help
to automatize this process. For example, Health 29 Phenotyper (4) extracts HPO
terms from electronic medical records in just a few clicks. The tool recognises
medical terms in several languages. In case of paper medical records, a photo
of the text can be automatically processed by any optical character recognition
software to create its digital copy.
We should pay
attention not only to the format but also to the content of medical records. Detailed
and precise information, e.g. “auditory hallucinations”, will provide better
results than just broad categories, e.g. “psychosis”. It is important to record
all symptoms, even when they seem irrelevant, because further analysis may
reveal unknown comorbidities. Based on comprehensive data, artificial
intelligence is already able to assist clinicians in making diagnosis and even
correct them in cases of mistakes. Large-scale analysis of medical records can
also help discover previously overlooked prodromal symptoms. Since many
psychiatric diseases have a developmental component, early identification of
individuals at risk would be key for developing disease prevention strategies.
examples above focus on phenotypic information, big data approaches can be
applied to any data type used in clinical practice, including blood tests,
brain scan images, genome sequencing data and even comprehensive records of patients’
environmental exposures history. By combining different data types, we can have
a more holistic approach to diagnosis and treatment. Joint analyses could let
us, for example, discover new gene-environment associations and identify early
Health data are
sensitive data, and their large-scale analysis is associated with several
ethical, legal and social issues. It is important to remember that patient
benefit is the main priority of all studies, and patient communities should
actively participate in determining what data are used, how and for what
purposes. Together, patients, clinicians and data experts can steer psychiatry
to a new direction, that might result in important discoveries, better
diagnosis and new treatments.
*Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, published in 2013
Throughout her career, Dorota has been dedicated to interdisciplinarity and science communication. She completed a PhD in neuroscience at the Max-Planck Institute of Experimental Medicine in Göttingen, Germany. Her research focused on studying mouse models to understand how genetics combined with risk factors such as stress contributes to symptoms of schizophrenia. Next, she worked at the Newcastle University, UK as science communication officer for RD-Connect, an EU-funded platform enabling rare disease researchers to share data and samples around the world. Currently, she is an editor for an interdisciplinary scientific journal iScience at Cell Press.