Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection

Romero-Medrano, Lorena; Moreno-Muñoz, Pablo; Artés‐Rodríguez, Antonio

doi:10.1007/s11265-021-01705-8

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…Missing data are handled differently by the model when observations are partially missing (one or some features are totally or partially missing within a day) or totally missing (every feature is missing that day). The first case is handled in the daily profiling step through the Heterogeneous Mixture Model, following the approach detailed in [36,37]. The second situation is tackled in the second stage, when the change-point detection model is applied through the sequence of daily profile representations.…”

Section: Discussionmentioning

confidence: 99%

“…The second situation is tackled in the second stage, when the change-point detection model is applied through the sequence of daily profile representations. In this case, we consider a Bayesian approach that is based on marginalization of missing observations, as also detailed in [36,37]. Treating missing data through algorithms instead of heuristic approaches provides robustness and allows to reduce the false alarm rate.…”

Section: Discussionmentioning

confidence: 99%

“…The change-point detection model used is based on the hierarchical extension of the Bayesian online change-point detection algorithm [ 36 ]. We assumed that the main sequence of daily profiles could be divided into nonoverlapping behavioral patterns separated by behavioral changes.…”

Section: Methodsmentioning

confidence: 99%

“…Since we cannot observe the true sequence of daily profiles representing a behavior, we must use the sequence of probability profiling vectors. The change-point detection method used was presented in our previous work [ 36 ] and uses samples of the posterior distributions to fully characterize the probability of each profile for each day. Using this method, a discrete variable is introduced into the model that counts the exact number of days since the last change-point occurred.…”

Section: Methodsmentioning

confidence: 99%

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One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

Barrigón¹,

Romero-Medrano²,

Moreno-Muñoz³

et al. 2023

J Med Internet Res

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Background Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions We describe an innovative method to identify mental health crises based on passively collected information from patients’ smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

Barrigón¹,

Romero-Medrano²,

Moreno-Muñoz³

et al. 2023

J Med Internet Res

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“…• Behavior indicator: This variable is 1 if a new behavior has started this day and 0 otherwise. • Stability indicator: This variable is 1 if the user is stable this day and 0 otherwise (26).…”

Section: Behavioral Indicatorsmentioning

confidence: 99%

Smartphone-Based Ecological Momentary Assessment for the Measurement of the Performance Status and Health-Related Quality of Life in Cancer Patients Under Systemic Anticancer Therapies: Development and Acceptability of a Mobile App

et al. 2022

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BackgroundWe have defined a project to develop a mobile app that continually records smartphone parameters which may help define the Eastern Cooperative Oncology Group performance status (ECOG-PS) and the health-related quality of life (HRQoL), without interaction with patients or professionals. This project is divided into 3 phases. Here we describe phase 1. The objective of this phase was to develop the app and assess its usability concerning patient characteristics, acceptability, and satisfaction.MethodsThe app eB2-ECOG was developed and installed in the smartphone of cancer patients who will be followed for six months. Criteria inclusion were: age over 18-year-old; diagnosed with unresectable or metastatic lung cancer, gastrointestinal stromal tumor, sarcoma, or head and neck cancer; under systemic anticancer therapies; and possession of a Smartphone. The app will collect passive and active data from the patients while healthcare professionals will evaluate the ECOG-PS and HRQoL through conventional tools. Acceptability was assessed during the follow-up. Patients answered a satisfaction survey in the app between 3-6 months from their inclusion.ResultsThe app developed provides a system for continuously collecting, merging, and processing data related to patient’s health and physical activity. It provides a transparent capture service based on all the available data of a patient. Currently, 106 patients have been recruited. A total of 36 patients were excluded, most of them (21/36) due to technological reasons. We assessed 69 patients (53 lung cancer, 8 gastrointestinal stromal tumors, 5 sarcomas, and 3 head and neck cancer). Concerning app satisfaction, 70.4% (20/27) of patients found the app intuitive and easy to use, and 51.9% (17/27) of them said that the app helped them to improve and handle their problems better. Overall, 17 out of 27 patients [62.9%] were satisfied with the app, and 14 of them [51.8%] would recommend the app to other patients.ConclusionsWe observed that the app’s acceptability and satisfaction were good, which is essential for the continuity of the project. In the subsequent phases, we will develop predictive models based on the collected information during this phase. We will validate the method and analyze the sensitivity of the automated results.

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One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study (Preprint)

Barrigón¹,

Romero-Medrano²,

Moreno-Muñoz³

et al. 2022

Preprint

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BACKGROUND Suicide is a major global public health issue becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. OBJECTIVE We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized via real-time smartphone monitoring in a cohort of suicidal patients METHODS We recruited 225 patients between February 2018 and March 2020 with a history of suicidal behavior and/or ideation, as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on suicide risk events. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide risk events were tested. RESULTS During follow-up, 8% of participants attempted suicide and 6.2% presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of one week with an area under the curve of 0.78, indicating good accuracy. CONCLUSIONS We describe an innovative method to identify suicide risk based on passively collected information from patients’ smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crisis.

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Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection

Cited by 3 publications

References 11 publications

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

Smartphone-Based Ecological Momentary Assessment for the Measurement of the Performance Status and Health-Related Quality of Life in Cancer Patients Under Systemic Anticancer Therapies: Development and Acceptability of a Mobile App

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study (Preprint)

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