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Li K, Habre R, Deng H, Urman R, Morrison J, Gilliland FD, Ambite JL, Stripelis D, Chiang YY, Lin Y, Bui AA, King C, Hosseini A, Vliet EV, Sarrafzadeh M, Eckel SP. Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data. JMIR Mhealth Uhealth 2019; 7:e11201. [PMID: 30730297 PMCID: PMC6386646 DOI: 10.2196/11201] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 09/30/2018] [Accepted: 11/14/2018] [Indexed: 12/20/2022] Open
Abstract
Background Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. Results In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.
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Affiliation(s)
- Kenan Li
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - Rima Habre
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - Huiyu Deng
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - Robert Urman
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - John Morrison
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - Frank D Gilliland
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - José Luis Ambite
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Dimitris Stripelis
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Yao-Yi Chiang
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Yijun Lin
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Alex At Bui
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Christine King
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Anahita Hosseini
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, United States
| | - Eleanne Van Vliet
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - Majid Sarrafzadeh
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, United States
| | - Sandrah P Eckel
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
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Zide M, Caswell K, Peterson E, Aberle DR, Bui AA, Arnold CW. Consumers' Patient Portal Preferences and Health Literacy: A Survey Using Crowdsourcing. JMIR Res Protoc 2016; 5:e104. [PMID: 27278634 PMCID: PMC4917738 DOI: 10.2196/resprot.5122] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 01/05/2016] [Accepted: 03/23/2016] [Indexed: 11/29/2022] Open
Abstract
Background eHealth apps have the potential to meet the information needs of patient populations and improve health literacy rates. However, little work has been done to document perceived usability of portals and health literacy of specific topics. Objective Our aim was to establish a baseline of lung cancer health literacy and perceived portal usability. Methods A survey based on previously validated instruments was used to assess a baseline of patient portal usability and health literacy within the domain of lung cancer. The survey was distributed via Amazon’s Mechanical Turk to 500 participants. Results Our results show differences in preferences and literacy by demographic cohorts, with a trend of chronically ill patients having a more positive reception of patient portals and a higher health literacy rate of lung cancer knowledge (P<.05). Conclusions This article provides a baseline of usability needs and health literacy that suggests that chronically ill patients have a greater preference for patient portals and higher level of health literacy within the domain of lung cancer.
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Affiliation(s)
- Mary Zide
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States.
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Hsu W, Han SX, Arnold CW, Bui AA, Enzmann DR. A data-driven approach for quality assessment of radiologic interpretations. J Am Med Inform Assoc 2015; 23:e152-6. [PMID: 26606938 DOI: 10.1093/jamia/ocv161] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 09/23/2015] [Indexed: 11/12/2022] Open
Abstract
Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g., pathology) as a reference standard for individual radiologists, subspecialty sections, imaging modalities, and entire departments. Downstream diagnostic conclusions from the electronic medical record are utilized as "truth" to which upstream diagnoses generated by radiology are compared. The described system automatically extracts and compares patient medical data to characterize concordance between clinical sources. Initial results are presented in the context of breast imaging, matching 18 101 radiologic interpretations with 301 pathology diagnoses and achieving a precision and recall of 84% and 92%, respectively. The presented data-driven method highlights the challenges of integrating multiple data sources and the application of information extraction tools to facilitate healthcare quality improvement.
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Affiliation(s)
- William Hsu
- Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Simon X Han
- Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Corey W Arnold
- Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Alex At Bui
- Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Dieter R Enzmann
- Department of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
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Abstract
Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.
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Affiliation(s)
- A A Bui
- University of California at Los Angeles (UCLA), 90024, USA.
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Bui AA, Aberle DR, McNitt-Gray MF, Cardenas AF, Goldin J. The evolution of an integrated timeline for oncology patient healthcare. Proc AMIA Symp 1998:165-9. [PMID: 9929203 PMCID: PMC2232288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Abstract
The introduction of computers in the medical environment has contributed to the proliferation of medical data, often making it difficult to consolidate information on a single patient. In patients with complex medical problems, such as oncology patients, the lack of data integration can negatively impact on patient care. This paper presents an infrastructure for the creation of an integrated multimedia timeline that automatically combines patient information from distributed hospital information sources, and creates a visual summary of pertinent events in a patient's medical history. In this prototype, we focus on oncology patients under treatment for advanced cancers.
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Affiliation(s)
- A A Bui
- University of California, Los Angeles, USA
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