Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

OC-163 identification of inflammatory bowel disease (IBD) using field asymmetric ion mobility spectrometry (FAIMS)

Introduction Resident colonic bacteria, principally anaerobes and firmicutes, ferment undigested fibre. The resultant volatile organic compounds (VOCs) formed are dissolved in the faeces but also absorbed and excreted in the urine. We have previously shown that electronic nose (E-nose) analysis of urine VOCs distinguishes between Crohn's disease (CD), ulcerative colitis (UC) and healthy volunteers (HV): the underlying principle is pattern recognition of disease-specific “chemical fingerprint”. High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) offers a possible alternative. The underlying principle is separation of VOC chemical components based on their different ion mobilties in high electric fields. We performed a pilot study in the above groups, the patients in remission (Rem) or with active disease (AD), to assess if this technology could achieve separation between the groups. The results were validated against E-nose analysis. Methods 59 subjects were studied; HV n=14, UC (Rem) n=18, UC (AD) n=4; CD (Rem) n=19, CD (AD) n=4. Urine samples (7 ml) in universal containers (25 ml) were heated to 40±0.1 C. The headspace (the air above the sample) was then analysed using FAIMS. The data were analysed by Fisher Discriminant Analysis. Results The technique distinguished between the three groups. Additionally, patients with active disease could be distinguished from those in remission. These results were concordant with E-nose analysis. Conclusion This pilot shows that urine VOCs, analysed by the different approaches of E-nose and FAIMS, the latter a novel application, can distinguish the healthy from those with UC and CD when disease is active or in remission. The two technologies together offer a non-invasive approach to diagnosis and follow-up in inflammatory bowel disease

Development of machine learning schemes for use in non-invasive and continuous patient health monitoring

Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1