Petteri Teikari, PhDhttp://petteri-teikari.com/

version Thu 23 November 2017

Computational DiseaseManagement with Wearable Devices

Machine Learning modelling of disease trajectory with deep

learning and/or Gaussian Processes

https://www.fastcodesign.com/3036175/from-the-designers-of-fitbit-a-digital-tattoo-implanted-under-your-skin

Luggable Wearable Implant

Wearables are so 2015. medium.com/@kaylajheffernan | Kayla J Heffernan

Mingwu Gao et al. (2016)

Why wearables for screening, disease management, diagnosis and ‘health management’?

Biosignals have strong time-dependency

with multiple ‘oscillators’ and confounding factors

See for example intraocular pressure (IOP), used for glaucoma diagnosis and management

IOP variability | Crawford Downs7th World Glaucoma Congress JUNE 28 - JULY 1, 2017 HELSINKIhttps://youtu.be/QNDzq5Rp5RA

Intraocular pressure rises during the night, hours after (or before) you see patients in your office.https://www.reviewofoptometry.com/article/iop-goes-bump-in-the-night

The range of IOP fluctuation was larger in the eyes with normal-tension glaucoma (NTG) than in the nonglaucoma eyes. This larger fluctuation might be one of the reasons underlying the aggravation of the visual field by NTG. - Tojo et al. (2016)

Circadian/diurnal variation

Day/night variation

Short transients

Postural effect

https://doi.org/10.1186/s12886-017-0441-3

And in practice now the patient sees the doctor 1-2 times a year and have one’s IOP measured during those visits. While there is clear rhythmicity that itself have diagnostic utility

Continuous Monitoringthe key

With user experience where people are not even aware of their wearable device

uncomfortable or not is highly subjective

Fitbit‘Synonym’ for a wearable

Wrist-worn activity meter with heart rate sensing

Fitbit wearables will help power NIH's All of Us Research ProgramBy Dave Muoio November 07, 2017http://www.mobihealthnews.com/content/fitbit-wearables-will-help-power-nihs-all-us-research-program

The wearable manufacturer announced that The Participant Center — a unit led by the Scripps Translational Science Institute (STSI) tasked with enrolling diverse populations into the national program — will be distributing 10,000 Fitbit Charge 2 and Fitbit Alta HR devices to a representative sample of All of Us participants. At the end of a one-year study period, researchers will provide the national program with recommendations on how Fitbit’s wearables may be more broadly employed in the national study. These devices will also collect an early data set of the users’ physical activity, heart rate, sleep, and other critical health outcomes.

“As part of the global shift towards precision medicine,

wearable data has the potential to inform highly personalized

healthcare,” Adam Pellegrini, general manager of Fitbit Health

Solutions, said in a statement. “Through this historic initiative, we

will be able to see the role that Fitbit data can play on the path to

better understanding how individualization can help to

prevent and treat disease.”

Next GenImplantable and tattoo-like sensors

If accepted by general public

better user →experience

https://www.dezeen.com/2017/08/14/japanese-research-group-develops-tattoo-style-sensor-lasts-on-skin-for-week-technology/

http://www.mobihealthnews.com/content/fda-clears-abbott%E2%80%99s-smartphone-compatible-cardiac-monitor-implant

https://readwrite.com/2016/02/19/future-of-wearables/

GluSense is developing GlydeTM CGM – a long-term injectable continuous glucose sensor. http://www.glusensemedical.com/vision

Example Diseases

for Management Modeling

with wearable monitoring

EXAMPLE #1

DiabetesType 2

New sweat sensor can monitor glucose, stress and inflammation in type 2 diabetes Jack Woodfield Tue, 27 Jun 2017 http://www.diabetes.co.uk/news/2017/jun/

While this is not the first wearable device to calculate glucose through sweat, this new product can measure two other biomarkers as well: cortisol and interleukin-6.

"If a person has chronic stress, their cortisol levels increase, and their resulting insulin resistance will gradually drive their glucose levels out of the normal range," said lead author Professor Shalini Prasad.

"At that point, one could become prediabetic, which can progress to type 2 diabetes, and so on. If that happens, your body is under a state of inflammation, and this inflammatory marker, interleukin-6, will indicate that your organs are starting to be affected."

A new paradigm in sweat based wearable diagnostics biosensors using Room Temperature Ionic Liquids (RTILs) Rujuta D. Munje, Sriram Muthukumar, Badrinath Jagannath & Shalini Prasad Scientific Reports 7, Article number: 1950 (2017) doi: 10.1038/s41598-017-02133-0

EXAMPLE #2

High Blood Pressure

https://www.wareable.com/health-and-wellbeing/wearable-blood-pressure-tech-559

Heartisans’ mission is to address the disadvantages of blood pressure monitors and make it easy for anyone to track and manage blood pressure. Accelerometer, galvanic skin response, heart rate, and ECG sensor.

Omron's Project Zero 2.0 watch, currently in development

Omron makes at-home blood pressure monitors, but Kellogg says that right now it's at work on a more discreet wearable "the size of an Apple Watch" that will track pressure from the wrist, accounting for the difference from the upper arm. "You'll look at it and it doesn't look like a blood pressure monitor," he says. "We got it into a state that will make it very easy for people to use."

http://www.salu.ca/products.html

Salu Pulse Band is a Canadian startup which senses your blood flow around your wrist and identifies an estimated ambulatory blood pressure. It also tracks personal activity, blood oxygen

saturation, Heart Rate and estimated blood pressure.

‘Non-specific’ beneficial for various conditions

Core body temperature

Non-invasive measurement of core body temperature in Marathon runners Carlo Alberto Boano ; Matteo Lasagni ; Kay Römer https://doi.org/10.1109/BSN.2013.6575484

Basal body temperature measurement by YONO for fertility window measurement https://www.yonolabs.com/how-yono-detects-the-fertility-window/

Non-contact temperature measurement with TMP006 http://www.ti.com/product/TMP006

Body temperature has a circadian and homeostatic components that correlate with athletic and cognitive performance Wright Jr et al (2002)

Body temperature has a circadian and homeostatic components that correlate with athletic and cognitive performance Wright Jr et al (2002)

Thermoregulatory and human-sleep wake cycle connected e.g. warm feet will promote faster sleep onset, and it is useful to measure distal, proximal and core body temperatures with sleep problems. Kurt Kräuchi (2007)

‘Non-specific’ beneficial for various conditions

Circadian phaseNo good way to measure circadian phase in vivo easily. Melatonin as the gold standard which is expensive and cumbersome to measure, and it is only secreted during night.

Would be beneficial to quantify phase changes induced by bright light treatment, melatonin administration and other zeitgeber interventions

Wrist temperature (WT) pattern purified for each studied masking variable.

Demasked WT pattern, expressed as mean ± SEM, following the application of the purification by categories method according to environmental temperature level (A), light exposure (B), activity (C), position (D) and sleep status (E). The shaded area shows the mean sleep period.

All subjects wore a Thermochron iButton DS1921H (Maxim Integrated Products, Mkt cap 15.21B$, Sunnyvale, California, USA) that measured their wrist skin temperature with a precision of ±0.125°C.

Wrist temperature confounded (masked) by other factors, but what if it is cleaned for relatively good indirect estimate of Core Body Temperature (CBT)

‘Non-specific’ beneficial for various conditions

Sleep Quality #1Åkerstedt

Parameters of the three-process model of alertness regulation. S = homeostatic component during waking. S' = homeostatic component during sleep (‘sleep inertia’). C = circadian component. S + C = the alertness prediction. 7 = level of risk.

Åkerstedt and Folkard (1995)

Sleepiness driven by three processes with individual variability of course in circadian (chronotype) and homeostatic (trototype, somnotype) phenotypes

Now for example athletes, flight crews, frequent flying businessmen, soldiers, high-risk operators (e.g. see Chernobyl accident and the role of fatigue) would benefit from predictive modeling optimizing performance (fatigue management).

https://doi.org/10.1371/journal.pcbi.1000418

Chrono@Work https://www.chronoatwork.com/

https://pulsarinformatics.com/products/workforce

‘Non-specific’ beneficial for various conditions

Sleep Quality #2

EEG recording as the gold standard, but can be expensive and uncomfortable to sleep with

Oura Ring | Best Sleep Trackers 2017https://sleeptrackers.io/oura-ring/

Using the blood flow as an indirect measure of the sleep stages (sleep architecture). The app gives amount of light, deep and REM sleep

https://youtu.be/4KeM0x8fQY4

The need for annotations, and the power ‘economies of platform’

Diabetes.co.uk: "If you measure levels every hour on the hour for a full week that provides 168 hours' worth of data

on your health as it changes," said Professor Shalini Prasad.

"People can take more control and improve their own self-care. A user

could learn which unhealthy decisions are more forgiven by their body than

others."

Current generation of wearable basically useless in clinical settings

Need more versatile wearables with more sensors and good user experience

“These are basically measuring devices,” said Eric Finkelstein, a professor at Duke-NUS Medical School in Singapore, who led the research. “Knowing how active you are doesn’t translate into getting people to do more and the novelty of having that information wears off pretty quickly.”

https://doi.org/10.1371/journal.pmed.1001953:

Recent surveys showed that 32% of users stop wearing these devices after six months, and 50% after one year [14]. Many wearables suffer from being a “solution in search of a problem." In other words, they don’t add functional value that is already expected from personal technology of that type, and they require too much effort, which breaks the seamless user experience [15]. Poor implementation of user experience principles [16] alongside the ad hoc design of user interfaces stems in part from the rapid nature of development, which may also explain the lack of randomised trials.

Successful applications of “intelligent” computing and the use of multiple consumer sensors requires a truly interdisciplinary approach in order to decode “individual big data.” Computer and data scientists, who write such computational algorithms, have to work closely with clinicians to accurately quantify various health conditions and risk factors. Behavioural scientists and interface designers have to be on board to facilitate and develop more personalised, intuitive, and user-friendly systems of behavioural engagement and feedback.

While many champion wearables as data-rich devices that will revolutionise 21st century medicine, it remains highly probable that, like many technological trends, these mass-marketed gadgets will drift into obscurity.

If frameworks are in place allowing wearable devices to be integrated into health care systems, this could, in turn, kick-start the development of validation programmes that would sit alongside appropriate training for health care professionals. This knowledge and understanding could then be disseminated to patients as validated devices become standardised, providing both individual and aggregated data for patients, governments, and health care providers.

Phase Response CurvesFor example, it matters when (in relation to internal timing, not external, see e.g. the concept social jetlag)

And how much you administer your intervention (light, melatonin) for circadian phase adjustments

Or some other medication or treatment in general chronotherapeutic sense

Circadian phase shift by light

Circadian phase shift by exogenous melatonin

Circadian phase shift by caffeine

Khalsa et al. (2003) Burgess et al. (2008) Burke et al. (2015)

https://doi.org/10.1016/S1470-2045(00)00326-0Cited by 197 articles

Dose Response CurvesEvening light (especially melanopsin-stimulating, ~490 nm) is delaying sleep onset as the phase-response curve on previous slide would predict.

Similarly athletes, students, etc., may want to know how evening coffee, alcohol, heavy exercise, spicy food, etc. quantitatively is affecting their sleep architecture.

This sort frequent sampling requires the use of wearables worn every day.

(left) Dynamics of slow-wave activity (SWA; 0.75–4.5 Hz) per NREM-REM sleep cycles 1–3 after sleep onset for the derivations F4, C4, P4, and O2. Values are expressed as percentages of the dark condition and are plotted against relative clock time (n = 8) for blue light (460 nm, blue circle), green light (550 nm, green triangle, down), and dark condition (0 lux, black triangle). (right) Time course of core body temperature (CBT) during blue light (460 nm, ○), green light (550 nm, grey triangle, down), and dark condition (0 lux, black triangle) plotted from 2130 until 1030 the next morning. - Münch et al. (2006)

Evening blue light shortened REM sleep duration, and increased third cycle SWA activity. Similarly CBT took longer to reach control levels.

Similar alerting effect of evening blue light can be seen with heart rate that is increased by melanopsin-stimulating light. – Cajochen et al. (2005)

That was just mainly the effect of light to human physiology

”In the wild” people hardly are documenting rigorously their light exposure, caffeine and alcohol consumption, exercise intensity and various other possible factors influencing the measured variable.

How to make annotation the easiest for patients and users.

For example, synergies with medication adherence startups?

Adherence to MedicationLars Osterberg, M.D., and Terrence Blaschke, M.D.N Engl J Med 2005; 353:487-497 August 4, 2005 DOI: 10.1056/NEJMra050100 | Cited by 6542

Connected technology solutions dramatically improve medication adherence, according to new study from Philipshttps://doctordementia.com/2016/06/08/tech-dramatically-improves-medication-adherence/

Medication Adherence Tech: A dynamic and crowded market, but where are the winners in the space?By Dan Gebremedhin and Kara Werner August 25, 2017mobihealthnews.com

“Medication nonadherence is a national epidemic. Millions of patients per year do not fill their medications or take their medications as prescribed, costing the U.S. healthcare system $100 billion to $290 billion annually.”

“Instead of reminding patients to take their medication at 8 a.m., say, let’s use behavior. For example, remind me when I wake up, or before I leave home, or before I exercise,” is how Neura CEO Gilad Meiri describes it.

“We are not an app,” Meiri says. “We’re an ingredient inside apps. Once inside the app, we use passive signals to build a picture around you. We might use phone sensors, such as light, or audio, or behavior — some people charge their phone when they go to sleep — and then log that to make sure you take a medication.”

https://medcitynews.com/2017/02/397186/

Annotation Innovations

Most of the people does not have the time to mark down all of their activities.

For example, the double espresso (if bought) can be obtained from bank account activities. The start of exercises could be read from gym account, and the continuous variables could be recorded by the wearable.

DoubleEspresso

DoubleEspresso

Glass ofWine

HIITTraining

Discrete Events

Continuous VariablesHeart Rate

Light Exposure

Now one could try to model for example the recovery from heavy exercise, and try to avoid injuries and optimize training results.

Diabetes.co.uk: Shalini Prasad: "People can take more control and improve their own self-care. A

user could learn which unhealthy decisions are more forgiven by their body than

others."

Example Modeling

from literature to illustrate briefly what could be done

Disease Progression Modeling

Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data Joseph Futoma, Mark Sendak, Blake Cameron, Katherine Heller ; Proceedings of the 1st Machine Learning for Healthcare Conference, PMLR 56:42-54, 2016. http://proceedings.mlr.press/v56/Futoma16.html | https://arxiv.org/abs/1608.04615 | Related articles

Our proposed hierarchical latent variable model jointly models each patient’s multivariate longitudinal data by using a Gaussian Process (GP) for each individual variable, with shared latent variables inducing dependence between the mean functions.

The first term admits population-level fixed effects for each lab using observed baseline covariates, e.g. gender, race, age.

The second term is a sub-population component. Each subpopulation is associated with a unique trajectory

The third term is a random effects component, allowing for individual-specific longterm deviations in trajectory that are learned dynamically as more data becomes available.

Our model yields good performance on the task of predicting future kidney function and related lab values. Our work is a promising early work for developing machine learning models from EHR data and applying them to real clinical tasks.

Jointly modeling multiple event processes (e.g. emergency department visits, heart attacks, strokes) will allow us to learn correlations between different types of events. Given that much of the data recorded in the EHR is in the form of administrative billing codes, future work should incorporate these into the models as well, perhaps in an unsupervised fashion. Finally, models incorporating additional outcomes such as medical costs, hospitalizations, and patient quality of life are of significant practical interest.

Phenotyping Alternative

i.e. the second sub-population component

The Use of Autoencoders for Discovering Patient Phenotypes Harini Suresh, Peter Szolovits, Marzyeh Ghassemi. MIT CSAIL, Cambridge, MA. Submitted on 20 Mar 2017 https://arxiv.org/abs/1703.07004

Future Work We plan to compare the performance of patient embeddings from different autoencoder structures to predict onset of and weaning off of dialysis. We will compare its performance on its own and as an additional feature matrix concatenated to the raw feature values. It will also be interesting to experiment with deeper sequential autoencoder structures, or to use bidirectional LSTM cells in the hidden layers to better reconstruct inputs.

Combine Wearable data with high-fidelity clinical measurements

A probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends.

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling P. Perdikaris, M. Raissi, A. Damianou, N. D. Lawrence, G. E. Karniadakis Published 8 February 2017 DOI: 10.1098/rspa.2016.0751

Deeper Gaussian Processes

Deep gaussian processes Andreas C. Damianou, Neil Lawrence - Artificial Intelligence and Statistics, 2013. http://proceedings.mlr.press/v31/damianou13a.pdf | Cited by 176

Our proposed hierarchical latent variable model jointly models each patient’s multivariate longitudinal data by using a Gaussian Process (GP) for each individual variable, with shared latent variables inducing dependence between the mean functions.

Deep Neural Networks as Gaussian Processes Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S.

Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein Google Brain (Submitted on 1 Nov 2017) https://arxiv.org/abs/1711.00165

We suggest a few additional interesting directions to pursue, among those already mentioned. In our experiments, we observed the performance of the optimized neural network appears to approach that of the GP computation with increasing width. Whether gradient-based stochastic optimization implements an approximate Bayesian computation is an interesting question for further investigation. Recent work (Mandt et al. 2017) has suggested that SGD can be made to approximately sample from a Bayesian posterior. Further study is needed to determine if SGD does approximately implement Bayesian inference under the conditions typically employed in practice.

Additionally, the NNGP provides explicit estimates of uncertainty. This may be useful in predicting model failure in critical applications of deep learning, or for active learning tasks where it can be used to identify the best datapoints to hand label.