August 19, 2020

Using artificial intelligence and language technology to help clinicians screen patients with mood disorders and suicide risk with Dr. Philip Resnik

42 minutes

Philip Resnik, PhD, joins host Lorenzo Norris, MD, to discuss the use of AI and natural language processing to help clinicians identify patterns in the behaviors of patients with mental illness.

Dr. Resnik is a professor in the department of linguistics at the University of Maryland, College Park. He also has a joint appointment with the university’s Institute for Advanced Computer Studies.

Dr. Resnik has disclosed being an adviser for Converseon, a social media analysis firm; FiscalNote, a government relationship management platform; and SoloSegment, which specializes in enterprise website optimization. Some of the work Dr. Resnik discusses has been supported by an Amazon AWS Machine Learning Research Award.

Dr. Norris disclosed having no conflicts of interest.

And don’t miss the “Dr. RK” segment, with Renee Kohanski, MD.

Take-home points

Artificial intelligence (AI) refers to the effort to get computers to develop capabilities that humans would consider intelligent when people do them. For example, a “smart” thermostat learns patterns of behaviors and changes the temperature accordingly.
Natural language processing (NLP), an AI approach, focuses on the content of language from the words used and looks for cues within the content. NLP technology allows computers to do things more intelligently with human language, and NLP has generated technologies such as Siri, Alexa, and Google Translate.
Much of clinical work is focused on language, and clinicians look for cues within the content. Dr. Resnik is a technologist who believes that NLP can help facilitate clinical progress, especially in the face of a shortage of mental health clinicians and the limited amount of time that clinicians are able to spend with their patients.
Research aimed at using machine learning and NLP to analyze social media and other types of online presence to evaluate for suicide risk and the presence of mood disorders is underway.
Dr. Resnik imagines an ecosystem in which computers and humans balance their efforts, with each “brain” doing what they are best at; he believes in technology’s ability to save us time so we can prioritize our efforts.

Summary

A common example of NLP is automatic dictation and transcription software embedded in medical records. Dr. Resnik thinks of technology as an enabler and augmentation strategy.
Resnik and his wife, Rebecca Resnik, PsyD, completed a study using NLP to automatically detect clusters of language in the writing samples of college students. NLP software evaluated the natural patterns of language that might correlate with vegetative and somatic symptoms of depression and social isolation. His team was able to home in on language themes specific to college students that suggest specific symptoms of depression.
Another example of NLP in mental health is using predictive modeling, taking in data, and then making a prediction about a pertinent variable to understand mental health outcomes. For example, Glen Coppersmith, PhD, and associates evaluated social media posts with NLP software and concluded that analysis of language in social media posts can accurately identify individuals at risk of suicide and facilitate earlier interventions.
Resnik imagines a future in which speech and language samples are used to give a point-of-care evaluation of a patient’s mood and suicide risk.
“Clinical white space” is all the “space” (for example, the time between clinical encounters) and this is where decompensation occurs. Resnik suggests that NLP software could be used to fill this white space by using apps to collect text samples from patients. Software would analyze the samples and warn of patients who are at risk of decompensation or suicide.
Barriers to using this technology include engaging the technologists and clinicians, and accessing data samples because of privacy concerns, especially because HIPPA was written before the emergence of mega data.

References

Coppersmith G et al. Natural Language Processing of Social Media as Screening for Suicide Risk. Biomed Inform Insights. 2018 Aug 27. doi: 10.1177/1178222618792860.

Zirikly A et al. CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology. 2019 Jun 6. 24-33.

Lynn V et al. CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 2018. 37-46.

Selanikio J. The big-data revolution in health care. TEDx talk.

Graham S et al. Artificial Intelligence for Mental Health and Mental Illnesses: An Overview. Curr Psychiatry Rep. 2019 Nov 7;21(11):116. doi: 10.1007/s11920-019-1094-0.

Show notes by Jacqueline Posada, MD, who is associate producer of the Psychcast and consultation-liaison psychiatry fellow with the Inova Fairfax Hospital/George Washington University program in Falls Church, Va. Dr. Posada has no conflicts of interest.

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