Publications

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with highresolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Using high-resolution physiological data from non-human primate studies of Ebola and Marburg viruses, we pre-processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm. In most subjects detection is achieved well before the onset of fever; subject cross-validation lead to 52±14h mean early detection (at >0.90 area under the receiver-operating characteristic curve). Cross-cohort tests across pathogens and exposure routes also lead to successful early detection (28±16h and 43±22h, respectively). We discuss which physiological indicators are most informative for early detection and options for extending this capability to lower data resolution and wearable, non-invasive sensors.

Real-time monitoring of physiological data can reduce the likelihood of injury in noncombat military personnel and first-responders. MIT Lincoln Laboratory is developing a tactical Real-Time Physiological Status Monitoring (RT-PSM) system architecture and reference implementation named OBAN (Open Body Area Network), the purpose of which is to provide an open, government-owned framework for integrating multiple wearable sensors and applications. The OBAN implementation accepts data from various sensors enabling calculation of physiological strain information which may be used by squad leaders or medics to assess the team's health and enhance safety and effectiveness of mission execution. Security in terms of measurement integrity, confidentiality, and authenticity is an area of interest because OBAN system components exchange sensitive data in contested environments. In this paper, we analyze potential cyber-security threats and their associated risks to a generalized version of the OBAN architecture and identify directions for future research. The threat analysis is intended to inform the development of secure RT-PSM architectures and implementations.