Data-driven healthcare: from patterns to actions

The era of big data opens up new opportunities in personalised medicine, preventive care, chronic disease management and in telemonitoring and managing of patients with implanted devices. The rich data accumulating within online services and internet companies provide a microscope to study human behaviour at scale, and to ask completely new questions about the interplay between behavioural patterns and health. In this paper, we shed light on a particular aspect of data-driven healthcare: autonomous decision-making. We first look at three examples where we can expect data-driven decisions to be taken autonomously by technology, with no or limited human intervention. We then discuss some of the technical and practical challenges that can be expected, and sketch the research agenda to address them.

The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph Databases

This chapter presents fuzzy cognitive maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. The corresponding Web KnowARR framework incorporates findings from fuzzy logic. To this end, a first emphasis is particularly on the Web KnowARR framework along with a stakeholder management use case to illustrate the framework’s usefulness as a second focal point. This management form is to help projects to acceptance and assertiveness where claims for company decisions are actively involved in the management process. Stakeholder maps visually (re-) present these claims. On one hand, they resort to non-public content and on the other they resort to content that is available to the public (mostly on the Web). The Semantic Web offers opportunities not only to present public content descriptively but also to show relationships. The proposed framework can serve as the basis for the public content of stakeholder maps.

Applying the fuzzy analytical hierarchy process in digital marketing

The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today’s information overload makes digital marketing optimization, which is needed to continuously improve one’s business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities’ fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

Symptoms of endocrine treatment and outcome in the BIG 1-98 study

There may be a relationship between the incidence of vasomotor and arthralgia/myalgia symptoms and treatment outcomes for postmenopausal breast cancer patients with endocrine-responsive disease who received adjuvant letrozole or tamoxifen. Data on patients randomized into the monotherapy arms of the BIG 1-98 clinical trial who did not have either vasomotor or arthralgia/myalgia/carpal tunnel (AMC) symptoms reported at baseline, started protocol treatment and were alive and disease-free at the 3-month landmark (n = 4,798) and at the 12-month landmark (n = 4,682) were used for this report. Cohorts of patients with vasomotor symptoms, AMC symptoms, neither, or both were defined at both 3 and 12 months from randomization. Landmark analyses were performed for disease-free survival (DFS) and for breast cancer free interval (BCFI), using regression analysis to estimate hazard ratios (HR) and 95 % confidence intervals (CI). Median follow-up was 7.0 years. Reporting of AMC symptoms was associated with better outcome for both the 3- and 12-month landmark analyses [e.g., 12-month landmark, HR (95 % CI) for DFS = 0.65 (0.49–0.87), and for BCFI = 0.70 (0.49–0.99)]. By contrast, reporting of vasomotor symptoms was less clearly associated with DFS [12-month DFS HR (95 % CI) = 0.82 (0.70–0.96)] and BCFI (12-month DFS HR (95 % CI) = 0.97 (0.80–1.18). Interaction tests indicated no effect of treatment group on associations between symptoms and outcomes. While reporting of AMC symptoms was clearly associated with better DFS and BCFI, the association between vasomotor symptoms and outcome was less clear, especially with respect to breast cancer-related events.

Risk of severe hypoglycemia in sulfonylurea-treated patients from diabetes centers in Germany/Austria: how big is the problem? which patients are at risk?

BACKGROUND We investigated the rate of severe hypoglycemic events and confounding factors in patients with type-2-diabetes treated with sulfonylurea (SU) at specialized diabetes centers, documented in the German/Austrian DPV-Wiss-database. METHODS Data from 29,485 SU-treated patients were analyzed (median[IQR] age 70.8[62.2-77.8]yrs, diabetes-duration 8.2[4.3-12.8]yrs). The primary objective was to estimate the event-rate of severe hypoglycemia (requiring external help, causing unconsciousness/coma/convulsion and/or emergency.hospitalization). Secondary objectives included exploration of confounding risk-factors through group-comparison and Poisson-regression. RESULTS Severe hypoglycemic events were reported in 826(2.8%) of all patients during their most recent year of SU-treatment. Of these, n = 531(1.8%) had coma, n = 501(1.7%) were hospitalized at least once. The adjusted event-rate of severe hypoglycemia [95%CI] was 3.9[3.7-4.2] events/100 patient-years (coma: 1.9[1.8-2.1]; hospitalization: 1.6[1.5-1.8]). Adjusted event-rates by diabetes-treatment were 6.7 (SU + insulin), 4.9 (SU + insulin + other OAD), 3.1 (SU + other OAD), and 3.8 (SU only). Patients with ≥1 severe event were older (p < 0.001) and had longer diabetes-duration (p = 0.020) than patients without severe events. Participation in educational diabetes-programs and indirect measures of insulin-resistance (increased BMI, plasma-triglycerides) were associated with fewer events (all p < 0.001). Impaired renal function was common (N = 3,113 eGFR ≤30 mL/min) and associated with an increased rate of severe events (≤30 mL/min: 7.7; 30-60 mL/min: 4.8; >60 mL/min: 3.9). CONCLUSIONS These real-life data showed a rate of severe hypoglycemia of 3.9/100 patient-years in SU-treated patients from specialized diabetes centers. Higher risk was associated with known risk-factors including lack of diabetes-education, older age, and decreased eGFR, but also with lower BMI and lower triglyceride-levels, suggesting that SU-treatment in those patients should be considered with caution. This article is protected by copyright. All rights reserved.

Shape model, reference system definition, and cartographic mapping standards for comet 67P/Churyumov-Gerasimenko Stereo-photogrammetric analysis of Rosetta/OSIRIS image data

We analyzed more than 200 OSIRIS NAC images with a pixel scale of 0.9-2.4 m/pixel of comet 67P/Churyumov-Gerasimenko (67P) that have been acquired from onboard the Rosetta spacecraft in August and September 2014 using stereo-photogrammetric methods (SPG). We derived improved spacecraft position and pointing data for the OSIRIS images and a high-resolution shape model that consists of about 16 million facets (2 m horizontal sampling) and a typical vertical accuracy at the decimeter scale. From this model, we derive a volume for the northern hemisphere of 9.35 km(3) +/- 0.1 km(3). With the assumption of a homogeneous density distribution and taking into account the current uncertainty of the position of the comet's center-of-mass, we extrapolated this value to an overall volume of 18.7 km(3) +/- 1.2 km(3), and, with a current best estimate of 1.0 X 10(13) kg for the mass, we derive a bulk density of 535 kg/m(3) +/- 35 kg/m(3). Furthermore, we used SPG methods to analyze the rotational elements of 67P. The rotational period for August and September 2014 was determined to be 12.4041 +/- 0.0004 h. For the orientation of the rotational axis (z-axis of the body-fixed reference frame) we derived a precession model with a half-cone angle of 0.14 degrees, a cone center position at 69.54 degrees/64.11 degrees (RA/Dec J2000 equatorial coordinates), and a precession period of 10.7 days. For the definition of zero longitude (x-axis orientation), we finally selected the boulder-like Cheops feature on the big lobe of 67P and fixed its spherical coordinates to 142.35 degrees right-hand-rule eastern longitude and -0.28 degrees latitude. This completes the definition of the new Cheops reference frame for 67P. Finally, we defined cartographic mapping standards for common use and combined analyses of scientific results that have been obtained not only within the OSIRIS team, but also within other groups of the Rosetta mission.