Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Abstract

Background Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early warning signs of impending symptomatic relapses would allow clinicians to intervene before the patient’s condition worsens. Objective In this study, we aim to create the first models, exclusively using passive sensing data from a smartphone, to predict behavioral anomalies that could indicate early warning signs of a psychotic relapse. Methods Data used to train and test the models were collected during the CrossCheck study. Hourly features derived from smartphone passive sensing data were extracted from 60 patients with SSDs (42 nonrelapse and 18 relapse >1 time throughout the study) and used to train models and test performance. We trained 2 types of encoder-decoder neural network models and a clustering-based local outlier factor model to predict behavioral anomalies that occurred within the 30-day period before a participant's date of relapse (the near relapse period). Models were trained to recreate participant behavior on days of relative health (DRH, outside of the near relapse period), following which a threshold to the recreation error was applied to predict anomalies. The neural network model architecture and the percentage of relapse participant data used to train all models were varied. Results A total of 20,137 days of collected data were analyzed, with 726 days of data (0.037%) within any 30-day near relapse period. The best performing model used a fully connected neural network autoencoder architecture and achieved a median sensitivity of 0.25 (IQR 0.15-1.00) and specificity of 0.88 (IQR 0.14-0.96; a median 108% increase in behavioral anomalies near relapse). We conducted a post hoc analysis using the best performing model to identify behavioral features that had a medium-to-large effect (Cohen d>0.5) in distinguishing anomalies near relapse from DRH among 4 participants who relapsed multiple times throughout the study. Qualitative validation using clinical notes collected during the original CrossCheck study showed that the identified features from our analysis were presented to clinicians during relapse events. Conclusions Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs.

Anomaly detection to predict relapse risk in schizophrenia

“…Relapse was defined a priori as one of the following: (1) psychiatric hospitalization, (2) 25% increase in PANSS from baseline, (3) a CGI change score of 6 or 7 corresponding to “much worse” or “very much worse,” and (4) increase in the level of care or exacerbation symptoms that required immediate clinical management as assessed by the patient’s own personal clinician (ie non-study clinician) through medical records and review with that clinician - after that patient concluded the study protocol. This definition is in line with current definitions 14 used across the field and also in smartphone app research related to relapse 15 in the same population. These clinical targets related to relapse were then compared to derived anomalies to determine the sensitivity and specificity of anomaly detection in predicting relapse.…”

A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study

“…Second, our system used anomaly detection algorithms (instead of a binary classification), which have been studied extensively in the detection of system failures in infrastructure and factories, malware detection, and computer vision [ 22 ]. Anomaly detection algorithms are also used in medicine, such as medical images [ 25 , 26 ], electrocardiograms [ 27 ], and remote medicine [ 28 , 29 ]. Although classification techniques are the most common approaches to anomaly detection, data sets often lack sufficient labeled anomalies.…”

A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study (Preprint)

“…Second, our system used anomaly detection algorithms (instead of a binary classification), which have been studied extensively in the detection of system failures in infrastructure and factories, malware detection, and computer vision [22]. Anomaly detection algorithms are also used in medicine, such as medical images [25,26], electrocardiograms [27], and remote medicine [28,29]. Although classification techniques are the most common approaches to anomaly detection, data sets often lack sufficient labeled anomalies.…”