In the sections that follow, we will discuss two promising application of digital phenotyping. The first employs machine learning to characterize the speech patterns of individuals afflicted with PTSD. The second analyzes social media trends to localize infectious outbreaks. While these two applications of DP are quite distinct—the former provides insights into the mental states of individual patients, while the latter aggregates numerous data points to arrive at broader public health conclusions—they both demonstrate the striking potential of this nascent technology.
15.4.1 Case Study 1: Mental Health Diagnostics
One application of digital phenotyping that has shown a great deal of promise in recent years is mental health diagnostics, particularly in resource-limited contexts. The UN estimates that there were over 65 million refugees throughout the world as of 2015, displaced from their homelands by warfare or natural disasters and exposed to torture, genocide, forced isolation, sexual violence, and other harrowing traumas (Ruzek 2016). As many as 86% of refugees worldwide are thought to suffer from Post-Traumatic Stress Disorder (PTSD) (Owen 2015). Traditional clinical screenings for PTSD and other affective disorders such as depression require patients to sit with a professional healthcare provider and answer a series of diagnostic questions; however, this approach is not generalizable to low and middle-income countries for at least two primary reasons. One is the limited availability of professional psychiatric care within refugee camps and other resource-limited contexts (Springer 2018). The second is that, due to cultural taboos and deeply-entrenched stigmas associated with mental health issues, patients might not provide honest answers to their psychiatric evaluators (Doumit 2018). Thus, web and smartphone-based telemental health interventions are being touted as potential solutions to the high rates of PTSD in humanitarian settings.
Researchers and clinicians have noted that patients diagnosed with depression often speak more slowly and with more pauses than non-depressed individuals (Mundt 2012). These so-called “suprasegmental” features of speech (which include speed, pitch, inflection, etc.) are far more difficult to manipulate at will and therefore serve as reliable diagnostic biomarkers. Thus, suprasegmental analysis provides an alternative to traditional question and answer-based diagnostic protocols and has been successfully employed to diagnose generalized anxiety disorder, depression, and other affective ailments (Mundt 2007; Nilsonne 1987; Stassen 1991). A well-trained machine learning algorithm can detect depression based on the following suprasegmental features: voice quality, resonance, pitch, loudness, intonation, articulation, speed, respiration, percent pause time, pneumo-phono-articulatory coordination, standard deviation of the voice fundamental frequency distribution, standard deviation of the rate of change of the voice fundamental frequency, and average speed of voice change (Mundt 2007; Nilsonne 1987).
In recent years, researchers at the University of Vermont and the United States Department of Defense have proposed the development of an ML-driven, cell phone-based approach that uses suprasegmental linguistic features to diagnose PTSD (Xu 2012). An AI-driven chatbot could perform suprasegmental analysis on the incoming audio signal and then, using a pretrained classifier, categorize the caller according to the severity of his or her symptoms, as shown in Fig. 15.1. The most urgent cases (i.e. those that scored above a certain empirical threshold) would be escalated to local responders or remote psychotherapists. This low-cost mobile platform would allow more PTSD victims to obtain immediate symptomatic support and potentially life-saving diagnoses, even without access to the internet. This methodology has already been partially validated by several studies. A group at MIT has developed an ML-driven artificial intelligence agent that assigns a “probability of depression” index to audio content (Alhanai 2018). A study conducted by the University of Vermont in conjunction with the United States Department of Defense, in which acoustic biomarkers were used to screen for PTSD, determined that the standard deviation of fundamental frequency was found to be significantly smaller in PTSD sufferers than in non-sufferers. Analyses of this and other features were combined to achieve a detection accuracy of 94.4% using only 4 s of audio data from a given speaker (Xu 2012).
15.4.2 Case Study 2: Mapping the Spread of Infectious Disease Using Social Media and Search Engines
While Case Study 1 focuses on diagnosing individuals with a particular affliction, Case Study 2 invokes existing knowledge of disease prevalence to predict future epidemiological outcomes. Imagine that several hundred Twitter users independently complain of nausea, vomiting, diarrhea, and fever, while also referencing food they recently consumed at a particular fast-food joint (Harris 2018). A natural language processing engine that detects references to symptoms and maps them onto specific diseases, in conjunction with a geolocation module that aggregates tweets based on proximity, could conceivably be used to spot an E. coli outbreak originating from a single restaurant. This gives rise to a powerful platform for identifying infectious disease outbreaks—foodborne and otherwise—and pinpointing their sources far more rapidly and accurately than through formal reporting channels. Researchers have noted that engaging with disease outbreaks via tweets (as in the case of a St. Louis food-poisoning incident) results in a larger number of reports filed with the relevant public health organizations (Harris 2017). The organization HealthMap streamlines and formalizes this sort of analysis, harnessing social media trends and aggregating news sources to predict and characterize the spread of disease throughout the world (Freifeld 2008) (Fig. 15.2).
Search engine trends can also provide insight into infectious disease outbreaks. The search engine query tool Google Search Trends can be used to determine interest in a particular search term, and it allows for an impressive degree of geographical and temporal granularity. Search interest can then be used to approximate the incidence of a given disease or condition or to analyze population-level sentiment associated with said disease or condition. In one recent study, Google Trends data was able to recapitulate the traditional surveillance data of the 2015–2016 Colombian Zika outbreak with remarkable accuracy, suggesting that non-traditional digital data sources could be used to track epidemiological transmission dynamics in near real-time (Majumder 2016). Another example can be found in the work of Mauricio Santillana, whose team was able to retroactively reconstruct influenza transmission dynamics in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay over a four-year period by analyzing internet search terms. Their model substantially outperformed other methods of recapitulating historical flu observations, paving the way for search term-based predictive outbreak modeling (Clemente 2019).