Jukka-Pekka Onnela

Jukka-Pekka Onnela
Harvard University | Harvard · Department of Biostatistics

About

195
Publications
49,556
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13,293
Citations
Citations since 2016
134 Research Items
8768 Citations
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201620172018201920202021202205001,0001,500
201620172018201920202021202205001,0001,500

Publications

Publications (195)
Article
Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computat...
Preprint
Full-text available
Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty...
Preprint
Full-text available
The use of digital devices to collect data in mobile health (mHealth) studies introduces a novel application of time series methods, with the constraint of potential data missing at random (MAR) or missing not at random (MNAR). In time series analysis, testing for stationarity is an important preliminary step to inform appropriate later analyses. T...
Preprint
Full-text available
Background Menstrual characteristics are important signs of women's health. We examined the variation of menstrual cycle length by age, race and ethnicity, and body weight using data collected from mobile menstrual tracking apps. Understanding how menstrual characteristics vary by these factors can provide important information for further study of...
Article
Background Use of menstrual tracking data to understand abnormal bleeding patterns has been limited due to lack of incorporation of key demographic and health characteristics and confirmation of menstrual tracking accuracy. Objective(s) The objectives of this study were to identify abnormal uterine bleeding (AUB) patterns and their prevalence and...
Preprint
Full-text available
The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which acti...
Preprint
Locations of people moving about their lives are now commonly tracked through smartphones and wearable devices that access the Global Positioning System (GPS). Immediate measures include the estimated locations that identify visited map points and the travel paths between them. Here we introduce DPLocate, an open-source GPS data analysis pipeline d...
Article
BACKGROUND Optimized stroke systems of care enable access to timely care, including endovascular thrombectomy (EVT). Stroke systems have likely evolved after publication of EVT benefit (2015). Our objective was to map the stroke patient transfer network in California in terms of EVT access and patient transfer patterns, and to examine changes after...
Article
Digital medicine systems (DMSs) offer a potential solution to increase medication adherence, which is an important barrier to treatment of psychiatric disorders. In this pilot, we enrolled N=24 individuals diagnosed with severe mental illness to use an FDA-approved DMS for 5 months. We also collected digital phenotyping smartphone data to study beh...
Preprint
Mobile technology enables unprecedented continuous monitoring of an individual's behavior, social interactions, symptoms, and other health conditions, presenting an enormous opportunity for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous collection of mobile data results in the generatio...
Preprint
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We propose obtaining summary statistics by minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model. We show that minimizing the EPE is equivalent to learning a conditional density est...
Article
Full-text available
During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border travel for social and economic reasons. The relative effectiveness of these approaches for controlling the pandemic has gone largely unstudied. Here we developed a flexible network...
Article
Full-text available
Background Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associat...
Article
Full-text available
Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the mo...
Article
Full-text available
Smartphones can be used to collect granular behavioral data unobtrusively, over long time periods, in real-world settings. To detect aberrant behaviors in large volumes of passively collected smartphone data, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic in our method was a weighted average, with...
Article
Full-text available
College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students’ affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to...
Article
Full-text available
Background: Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients' cognit...
Article
Full-text available
Aims Daily micro-surveys, or the high-frequency administration of patient-reported outcome measures (PROMs), may provide real-time, unbiased assessments of health-related quality of life (HRQoL). We evaluated the feasibility and accuracy of daily micro-surveys using a smartphone platform among patients recovering from cancer surgery. Methods In a...
Conference Paper
Assessing pain-levels is integral for the management of spine patients during pre and post-surgical interventions and are often performed using the Patient Reported Outcome Measures (PROMs) in common clinical practices. PROMs are however known to be inefficient and suffer from the patient recall bias. This study proposes an alternate approach for p...
Article
ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without...
Article
Full-text available
Objectives Universities have turned to SARS-CoV-2 models to examine campus reopening strategies. While these studies have explored a variety of modeling techniques, none have used empirical data. Methods Here, we use an empirical proximity network of college freshmen, ascertained using smartphone Bluetooth, to simulate the spread of the virus. We i...
Poster
Full-text available
Introduction: Digital-phenotyping data from personal digital devices have a great potential for deep characterization of neurological and quality of life assessments in brain cancer patients. Methods: We retrospectively analyzed 36 brain cancer patients (glioma and solitary brain metastasis) enrolled between 2016-2019 through the Neurosurgery Depar...
Article
Full-text available
Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activ...
Article
Objectives: To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth. Summary background data: Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited. Metho...
Article
Full-text available
Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept o...
Preprint
Objective: A patient's activity patterns can be informative about her/his health status. Traditionally, this type of information has been gathered using patient self-report. However, these subjective self-report data can suffer from bias, and the surveys can become burdensome to patients over long time periods. Smartphones offer a unique opportunit...
Article
Full-text available
Objective: Patient-reported outcome measures (PROMs) are currently the gold standard to evaluate patient physical performance and ability to recover after spine surgery. However, PROMs have significant limitations due to the qualitative and subjective nature of the information reported as well as the impossibility of using this method in a continu...
Poster
Introduction: Digital phenotyping provides a means to quantify patient recovery after surgery. We aimed to evaluate differences in post-operative recovery among brain cancer patients by assessing patient mobility patterns based on passive GPS features sampled from smartphones. Methods: We retrospectively analyzed 33 brain cancer patients (grade I-I...
Article
Full-text available
The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for “digital phenotyping,” the collection and an...
Article
Full-text available
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Preprint
Mechanistic network models specify the mechanisms by which networks grow and change, allowing researchers to investigate complex systems using both simulation and analytical techniques. Unfortunately, it is difficult to write likelihoods for instances of graphs generated with mechanistic models because of a combinatorial explosion in outcomes of re...
Article
Objective We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distan...
Preprint
UNSTRUCTURED Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Here we introduce an open-source pipeline for the deep phenotyping of sleep, "DPSleep", that uses algorithms to detect missing data, calculate activity levels, and finally estimate the major Sleep Episode ons...
Article
Background: Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Objective: This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variabl...
Preprint
Full-text available
During the COVID-19 pandemic, many countries implemented international travel restrictions that aimed to contain viral spread while still allowing necessary cross-border travel for social and economic reasons. The relative effectiveness of these approaches for controlling the pandemic has gone largely unstudied. Here we developed a flexible network...
Article
Patient expectations of the impact of surgery on postoperative health-related quality of life (HRQL) may reflect the effectiveness of patient-provider communication. We sought to compare expected versus experienced HRQL among patients undergoing cancer surgery. Methods: Adults undergoing cancer surgery were eligible for inclusion (2017-2019). Pre...
Article
Full-text available
There is concern that the COVID-19 pandemic may cause increased risk of suicide. In the current study, we tested whether suicidal thinking has increased during the COVID-19 pandemic and whether such thinking was predicted by increased feelings of social isolation. In a sample of 55 individuals recently hospitalized for suicidal thinking or behavior...
Preprint
Full-text available
A bstract Universities have turned to SARS-CoV-2 models to examine campus reopening strategies 1–9 . While these studies have explored a variety of modeling techniques, all have relied on simulated data. Here, we use an empirical proximity network of college freshmen ¹⁰ , ascertained using smartphone Bluetooth, to simulate the spread of the virus....
Article
Background: Acute ischemic stroke (AIS) patients are frequently transferred between hospitals, however it is not clear whether these transfers are optimized with respect to proximity and quality of the destination hospital. Our primary object was to identify hospital characteristics associated with sending and receiving AIS patients. Methods: Using...
Preprint
Full-text available
Minimum spanning trees (MSTs) are used in a variety of fields, from computer science to geography. Infectious disease researchers have used them to infer the transmission pathway of certain pathogens. However, these are often the MSTs of sample networks, not population networks, and surprisingly little is known about what can be inferred about a po...
Article
With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unwei...
Preprint
Full-text available
Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Here we introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify relationships between derived sleep metrics and other variables of interest. The pipeline released her...
Article
Full-text available
Importance: Disparities in quality of care according to patient race and socioeconomic status persist in the US. Differential referral patterns to specialist physicians might be associated with observed disparities. Objective: To examine whether differences exist between Black and White Medicare beneficiaries in the observed patterns of patient...
Preprint
Approximate Bayesian Computation (ABC) now serves as one of the major strategies to perform model choice and parameter inference on models with intractable likelihoods. An essential component of ABC involves comparing a large amount of simulated data with the observed data through summary statistics. To avoid the curse of dimensionality, summary st...
Article
Full-text available
Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmen...
Preprint
Full-text available
The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphone applications for "digital phenotyping," the collection of phone...
Article
mHealth can be used to deliver interventions to optimize Health-related quality of life (HRQoL) of cancer patients. In this systematic-review and meta-analysis, we explored the possible impact of health interventions delivered via mHealth tools on HRQoL of cancer patients. The systematic literature search was performed on July 20, 2019, to identify...
Article
Full-text available
Objective: Digital monitoring technologies (e.g., smart-phones and wearable devices) provide unprecedented opportunities to study potentially harmful behaviors such as suicide, violence, and alcohol/substance use in real-time. The use of these new technologies has the potential to significantly advance the understanding, prediction, and prevention...
Article
Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires the ability to forward simulate datasets from a candidate model, but because the sizes of the observed and simulated datasets usually need to match, this can be computatio...
Preprint
Full-text available
Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires the ability to forward simulate datasets from a candidate model, but because the sizes of the observed and simulated datasets usually need to match, this can be computatio...
Article
Objective: To investigate the impact of sampling patients on descriptive characteristics of physician patient-sharing networks. Data sources: Medicare claims data from 10 hospital referral regions (HRRs) in the United States in 2010. Study design: We form a sampling frame consisting of the full cohort of patients (Medicare enrollees) with clai...
Article
Objective: Studies that use patient smartphones to collect ecological momentary assessment and sensor data, an approach frequently referred to as digital phenotyping, have increased in popularity in recent years. There is a lack of formal guidelines for the design of new digital phenotyping studies so that they are powered to detect both populatio...
Article
Objective: To determine the prevalence of clinically significant decision conflict (CSDC) among patients undergoing cancer surgery and associations with postoperative physical activity, as measured through smartphone accelerometer data. Background: Patients with cancer face challenging treatment decisions, which may lead to CSDC. CSDC negatively...
Article
PurposeWe sought to determine whether smartphone GPS data uncovered differences in recovery after breast-conserving surgery (BCS) and mastectomy, and how these data aligned with self-reported quality of life (QoL).Methods In a prospective pilot study, adult smartphone-owners undergoing breast surgery downloaded an application that continuously coll...
Preprint
Full-text available
Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmen...
Article
Full-text available
Spatially-embedded networks represent a large class of real-world networks of great scientific and societal interest. For example, transportation networks (such as railways), communication networks (such as Internet routers), and biological networks (such as fungal foraging networks) are all spatially embedded. Both the density of interactions (pre...
Article
Full-text available
Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphone...
Article
The broad adoption and use of smartphones has led to fundamentally new opportunities for capturing social, behavioral, and cognitive phenotypes in free-living settings, outside of research laboratories and clinics. Predicated on the use of existing personal devices rather than the introduction of additional instrumentation, smartphone-based digital...