Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure

• URL: http://arxiv.org/abs/2104.01511v1
• Date: Sun, 4 Apr 2021 01:04:29 GMT
• Title: Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure
• Authors: Ayse S. Cakmak, Samuel Densen, Gabriel Najarro, Pratik Rout, Christopher J. Rozell, Omer T. Inan, Amit J. Shah, Gari D. Clifford
• Abstract summary: Heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Twenty-eight participants were monitored using a smartphone app after discharge from hospitals. Motion, social, location, and clinical survey data collected via the smartphone-based monitoring system were used to develop an algorithm for predicting or classifying HF decompensation events.
• Score: 10.26574533594142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization. Early detection of HF symptoms and pro-active management may reduce adverse events. Approach: Twenty-eight participants were monitored using a smartphone app after discharge from hospitals, and each clinical event during the enrollment (N=110 clinical events) was recorded. Motion, social, location, and clinical survey data collected via the smartphone-based monitoring system were used to develop and validate an algorithm for predicting or classifying HF decompensation events (hospitalizations or clinic visit) versus clinic monitoring visits in which they were determined to be compensated or stable. Models based on single modality as well as early and late fusion approaches combining patient-reported outcomes and passive smartphone data were evaluated. Results: The highest AUCPr for classifying decompensation with a late fusion approach was 0.80 using leave one subject out cross-validation. Significance: Passively collected data from smartphones, especially when combined with weekly patient-reported outcomes, may reflect behavioral and physiological changes due to HF and thus could enable prediction of HF decompensation.

Related papers

• Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System [0.1253467217038036]
  TARL is an innovative approach that models the structural relationships of representative subsequences, known as shapelets, in heart rate time series.<n> TARL creates a shapelet-transition knowledge graph to model shapelet dynamics in heart rate time series.<n>These representations capture explanatory structures and predict future heart rate trends, aiding early illness detection.
  arXiv  Detail & Related papers  (2025-05-02T14:32:44Z)
• AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence [11.376437695524087]
  Adherence to prescribed treatments is crucial for individuals with chronic conditions to avoid costly or adverse health outcomes. We propose Adherence Forecasting and Intervention with Machine Intelligence (AIMI) AIMI uses smartphone sensors and previous medication history to estimate the likelihood of forgetting to take a prescribed medication.
  arXiv  Detail & Related papers  (2025-03-20T12:32:35Z)
• Cross-platform Prediction of Depression Treatment Outcome Using Location Sensory Data on Smartphones [55.992010576087424]
  We explore using location sensory data collected passively on smartphones to predict treatment outcome. Our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires.
  arXiv  Detail & Related papers  (2025-03-10T22:00:07Z)
• Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings [0.0]
  This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings. The platform provides real-time insights into patient behavior and interactions through video analysis. The dataset, compiled in collaboration with 11 hospital partners, encompasses over 300 high-risk fall patients.
  arXiv  Detail & Related papers  (2024-12-17T18:23:33Z)
• Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data [0.0]
  This study presents a novel semi-supervised approach for ASD detection using AutoEncoder-based Machine Learning (ML) methods. Our approach utilizes data collected manually through a serious game specifically designed for this purpose. Since the sensitive data collected by the gamified application are susceptible to privacy leakage, we developed a Federated Learning framework.
  arXiv  Detail & Related papers  (2024-10-25T23:00:12Z)
• Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care [0.9503773054285559]
  We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care. Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration.
  arXiv  Detail & Related papers  (2024-07-25T08:21:46Z)
• Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
  Engagement measurement finds application in healthcare, education, services. Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
  arXiv  Detail & Related papers  (2024-04-05T20:39:16Z)
• Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status [0.9867627975175174]
  Existing patient assessments in the ICU are mostly sporadic and administered manually. We developed a new "masked loss computation" technique that addresses the data imbalance problem. We performed AU inference on 634,054 frames to evaluate the association between facial AUs and clinically important patient conditions.
  arXiv  Detail & Related papers  (2023-11-01T15:07:03Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression [4.43322868663347]
  Data-quality problem of original clinical signs is less discussed in the literature. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible approach for high missing rate time series. It achieves a very high accuracy performance of 0.96 AUC-ROC and 0.81 AUC-PR, which exceeds the previous benchmark.
  arXiv  Detail & Related papers  (2023-05-19T15:28:02Z)
• Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones [75.23250968928578]
  We present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset. The proposed method shows promising results in predicting three medication statuses objectively.
  arXiv  Detail & Related papers  (2022-07-26T02:08:08Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data [56.53715632642495]
  Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
  arXiv  Detail & Related papers  (2021-02-08T15:09:07Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals [4.659599449441919]
  Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled.
  arXiv  Detail & Related papers  (2020-06-09T21:21:38Z)
• Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis [8.118172725250805]
  This study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients. We built a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network.
  arXiv  Detail & Related papers  (2020-02-24T01:39:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.