Bio


While studying for his master’s degree in bioinformatics, Dr Schuetz (Schütz in german) worked part-time as a nursing assistant, which sparked his deep desire to help improve healthcare using modern technologies. Based on this experience, his general interests lie at the intersection between digital health and AI/ML applied to improve healthcare, particularly related to remote patient monitoring. Before coming to California, he was a data scientist for a digital health startup at EPFL in Switzerland, where he worked, amongst other things, on translating algorithms from his Ph.D. into a production-level cloud environment and coordinated the launch of a digital health technologies enhanced study with multiple hospitals and Roche Pharmaceuticals. Before his industry stay, Narayan obtained a Ph.D. in Biomedical Engineering at the ARTORG Center for Biomedical Engineering Research in Bern, Switzerland. Narayan’s award-winning Ph.D. thesis focused on remote health monitoring in older adults and people with neurodegenerative diseases to assess health states and detect signs of health deterioration early on, establishing him as a pioneer in this nascent field. Before and during his Ph.D., Narayan also worked as a Software Engineer, leading a small agile team in successfully building a novel cross-platform telerehabilitation software system for patients with aphasia, that is currently used in the clinic.

Professional Experience:
2022 - 2023, Data Scientist, domo.health SA, Switzerland
2016 - 2021, Software Engineer (part-time), ARTORG Center for Biomedical Engineering Research, Switzerland
2014 - 2016, Nursing Assistant (part-time), Senevita AG, Switzerland
2011 - 2019, Medic, Swiss Armed Forces, Switzerland

Honors & Awards


  • Vontobel Award for Research on Age(ing) - $17k, University of Zurich/ Vontobel Stiftung (2023)
  • Preis für Alternsforschung - $11k, Seniorenunveristät Bern (2022)
  • StrongAge Young Investigator Award - $5k, StrongAge (2022)
  • Best PhD Thesis Award - $1k, Competence Center for Medical Technology (CCMT) & University of Bern (2022)
  • Best Poster Award - $2k, Alumni MedBern (2019)

Boards, Advisory Committees, Professional Organizations


  • Member, IEEE (2019 - Present)

Professional Education


  • PhD (summa cum laude), ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland, Biomedical Engineering (2022)
  • MSc (insigni cum laude), University of Bern and Freiburg, Bioinformatics and Computational Biology (2016)

Stanford Advisors


Patents


  • Narayan Schuetz, Angela Botros, Philipp Buluschek, Guillaume DuPasquier, Michael Single, Stephan Gerber, Tobias Nef. "Switzerland Patent WO2022168064A1 ASYNCHRONOUS INTERCORRELATED TIME SERIES DATASETS ALIGNMENT METHOD", DomoHealth SA

Current Research and Scholarly Interests


I am a postdoctoral digital health researcher at Stanford University working on remote patient monitoring technologies. This includes Apple Watch based digital 6-Minute Walk Test assessments, large-scale smartphone data from the MyHeart Counts study, and next-generation walk assessments using computer vision and mmWave technologies.

All Publications


  • A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust. NPJ digital medicine Schütz, N., Knobel, S. E., Botros, A., Single, M., Pais, B., Santschi, V., Gatica-Perez, D., Buluschek, P., Urwyler, P., Gerber, S. M., Müri, R. M., Mosimann, U. P., Saner, H., Nef, T. 2022; 5 (1): 116

    Abstract

    Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person's activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

    View details for DOI 10.1038/s41746-022-00657-y

    View details for PubMedID 35974156

    View details for PubMedCentralID PMC9381599

  • Eigenbehaviour as an Indicator of Cognitive Abilities. Sensors (Basel, Switzerland) Botros, A. A., Schuetz, N., Röcke, C., Weibel, R., Martin, M., Müri, R. M., Nef, T. 2022; 22 (7)

    Abstract

    With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively-it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.

    View details for DOI 10.3390/s22072769

    View details for PubMedID 35408381

    View details for PubMedCentralID PMC9003060

  • A Sensor-Driven Visit Detection System in Older Adults' Homes: Towards Digital Late-Life Depression Marker Extraction. IEEE journal of biomedical and health informatics Schutz, N., Botros, A., Hassen, S. B., Saner, H., Buluschek, P., Urwyler, P., Pais, B., Santschi, V., Gatica-Perez, D., Muri, R. M., Nef, T. 2022; 26 (4): 1560-1569

    Abstract

    Modern sensor technology is increasingly used in older adults to not only provide additional safety but also to monitor health status, often by means of sensor derived digital measures or biomarkers. Social isolation is a known risk factor for late-life depression, and a potential component of social-isolation is the lack of home visits. Therefore, home visits may serve as a digital measure for social isolation and late-life depression. Late-life depression is a common mental and emotional disorder in the growing population of older adults. The disorder, if untreated, can significantly decrease quality of life and, amongst other effects, leads to increased mortality. Late-life depression often goes undiagnosed due to associated stigma and the incorrect assumption that it is a normal part of ageing. In this work, we propose a visit detection system that generalizes well to previously unseen apartments - which may differ largely in layout, sensor placement, and size from apartments found in the semi-annotated training dataset. We find that by using a self-training-based domain adaptation strategy, a robust system to extract home visit information can be built (ROC AUC = 0.773). We further show that the resulting visit information correlates well with the common geriatric depression scale screening tool ( ρ = -0.87, p = 0.001), providing further support for the idea of utilizing the extracted information as a potential digital measure or even as a digital biomarker to monitor the risk of late-life depression.

    View details for DOI 10.1109/JBHI.2021.3114595

    View details for PubMedID 34550895

  • Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study. JMIR mHealth and uHealth Schütz, N., Saner, H., Botros, A., Pais, B., Santschi, V., Buluschek, P., Gatica-Perez, D., Urwyler, P., Müri, R. M., Nef, T. 2021; 9 (6): e24666

    Abstract

    Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults.In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults.We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis.Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed-measured by the number toss-and-turn events-as the most predictive sleep parameter (t score=-0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection.Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

    View details for DOI 10.2196/24666

    View details for PubMedID 34114966

    View details for PubMedCentralID PMC8235297

  • Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks Alberti, M., Botros, A., Schuetz, N., Ingold, R., Liwicki, M., Seuret, M., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 8204-8211
  • Development and Evaluation of Maze-Like Puzzle Games to Assess Cognitive and Motor Function in Aging and Neurodegenerative Diseases. Frontiers in aging neuroscience Nef, T., Chesham, A., Schütz, N., Botros, A. A., Vanbellingen, T., Burgunder, J. M., Müllner, J., Martin Müri, R., Urwyler, P. 2020; 12: 87

    Abstract

    There is currently a need for engaging, user-friendly, and repeatable tasks for assessment of cognitive and motor function in aging and neurodegenerative diseases. This study evaluated the feasibility of a maze-like Numberlink puzzle game in assessing differences in game-based measures of cognition and motor function due to age and neurodegenerative diseases. Fifty-five participants, including young (18-31 years, n = 18), older (64-79 years, n = 14), and oldest adults (86-98 years, n = 14), and patients with Parkinson's (59-76 years, n = 4) and Huntington's disease (HD; 35-66 years, n = 5) played different difficulty levels of the Numberlink puzzle game and completed usability questionnaires and tests for psychomotor, attentional, visuospatial, and constructional and executive function. Analyses of Numberlink game-based cognitive (solving time and errors) and motor [mean velocity and movement direction changes (MDC)] performance metrics revealed statistically significant differences between age groups and between patients with HD and older adults. However, patients with Parkinson's disease (PD) did not differ from older adults. Correlational analyses showed significant associations between game-based performance and movement metrics and performance on neuropsychological tests for psychomotor, attentional, visuospatial, and constructional and executive function. Furthermore, varying characteristics of the Numberlink puzzle game succeeded in creating graded difficulty levels. Findings from this study support recent suggestions that data from a maze-like puzzle game provide potential "digital biomarkers" to assess changes in psychomotor, visuoconstructional, and executive function related to aging and neurodegeneration. In particular, game-based movement measures from the maze-like puzzle Numberlink games are promising as a tool to monitor the progression of motor impairment in neurodegenerative diseases. Further studies are needed to more comprehensively establish the cognitive validity and test-retest reliability of using Numberlink puzzles as a valid cognitive assessment tool.

    View details for DOI 10.3389/fnagi.2020.00087

    View details for PubMedID 32372942

    View details for PubMedCentralID PMC7188385

  • A comparative study of pattern recognition algorithms for predicting the inpatient mortality risk using routine laboratory measurements ARTIFICIAL INTELLIGENCE REVIEW Schutz, N., Leichtle, A. B., Riesen, K. 2019; 52 (4): 2559-2573
  • Validity of pervasive computing based continuous physical activity assessment in community-dwelling old and oldest-old. Scientific reports Schütz, N., Saner, H., Rudin, B., Botros, A., Pais, B., Santschi, V., Buluschek, P., Gatica-Perez, D., Urwyler, P., Marchal-Crespo, L., Müri, R. M., Nef, T. 2019; 9 (1): 9662

    Abstract

    In older adults, physical activity is crucial for healthy aging and associated with numerous health indicators and outcomes. Regular assessments of physical activity can help detect early health-related changes and manage physical activity targeted interventions. The quantification of physical activity, however, is difficult as commonly used self-reported measures are biased and rather unprecise point in time measurements. Modern alternatives are commonly based on wearable technologies which are accurate but suffer from usability and compliance issues. In this study, we assessed the potential of an unobtrusive ambient-sensor based system for continuous, long-term physical activity quantification. Towards this goal, we analysed one year of longitudinal sensor- and medical-records stemming from thirteen community-dwelling old and oldest old subjects. Based on the sensor data the daily number of room-transitions as well as the raw sensor activity were calculated. We did find the number of room-transitions, and to some degree also the raw sensor activity, to capture numerous known associations of physical activity with cognitive, well-being and motor health indicators and outcomes. The results of this study indicate that such low-cost unobtrusive ambient-sensor systems can provide an adequate approximation of older adults' overall physical activity, sufficient to capture relevant associations with health indicators and outcomes.

    View details for DOI 10.1038/s41598-019-45733-8

    View details for PubMedID 31273234

    View details for PubMedCentralID PMC6609627