1
|
Liu P, Zheng G. CVCL: Context-aware Voxel-wise Contrastive Learning for label-efficient multi-organ segmentation. Comput Biol Med 2023; 160:106995. [PMID: 37187134 DOI: 10.1016/j.compbiomed.2023.106995] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 11/26/2022] [Revised: 04/02/2023] [Accepted: 05/01/2023] [Indexed: 05/17/2023]
Abstract
Despite the significant performance improvement on multi-organ segmentation with supervised deep learning-based methods, the label-hungry nature hinders their applications in practical disease diagnosis and treatment planning. Due to the challenges in obtaining expert-level accurate, densely annotated multi-organ datasets, label-efficient segmentation, such as partially supervised segmentation trained on partially labeled datasets or semi-supervised medical image segmentation, has attracted increasing attention recently. However, most of these methods suffer from the limitation that they neglect or underestimate the challenging unlabeled regions during model training. To this end, we propose a novel Context-aware Voxel-wise Contrastive Learning method, referred as CVCL, to take full advantage of both labeled and unlabeled information in label-scarce datasets for a performance improvement on multi-organ segmentation. Experimental results demonstrate that our proposed method achieves superior performance than other state-of-the-art methods.
Collapse
Affiliation(s)
- Peng Liu
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai, 200240, China
| | - Guoyan Zheng
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai, 200240, China.
| |
Collapse
|
2
|
Mu N, Lyu Z, Rezaeitaleshmahalleh M, Zhang X, Rasmussen T, McBane R, Jiang J. Automatic segmentation of abdominal aortic aneurysms from CT angiography using a context-aware cascaded U-Net. Comput Biol Med 2023; 158:106569. [PMID: 36989747 PMCID: PMC10625464 DOI: 10.1016/j.compbiomed.2023.106569] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/22/2022] [Accepted: 01/22/2023] [Indexed: 01/24/2023]
Abstract
We delineate abdominal aortic aneurysms, including lumen and intraluminal thrombosis (ILT), from contrast-enhanced computed tomography angiography (CTA) data in 70 patients with complete automation. A novel context-aware cascaded U-Net configuration enables automated image segmentation. Notably, auto-context structure, in conjunction with dilated convolutions, anisotropic context module, hierarchical supervision, and a multi-class loss function, are proposed to improve the delineation of ILT in an unbalanced, low-contrast multi-class labeling problem. A quantitative analysis shows that the automated image segmentation produces comparable results with trained human users (e.g., DICE scores of 0.945 and 0.804 for lumen and ILT, respectively). Resultant morphological metrics (e.g., volume, surface area, etc.) are highly correlated to those parameters generated by trained human users. In conclusion, the proposed automated multi-class image segmentation tool has the potential to be further developed as a translational software tool that can be used to improve the clinical management of AAAs.
Collapse
Affiliation(s)
- Nan Mu
- Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA
| | - Zonghan Lyu
- Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA
| | | | | | | | | | - Jingfeng Jiang
- Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Health Research Institute, Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, 49931, USA.
| |
Collapse
|
3
|
Stojchevska M, Steenwinckel B, Van Der Donckt J, De Brouwer M, Goris A, De Turck F, Van Hoecke S, Ongenae F. Assessing the added value of context during stress detection from wearable data. BMC Med Inform Decis Mak 2022; 22:268. [PMID: 36243691 PMCID: PMC9571684 DOI: 10.1186/s12911-022-02010-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 09/19/2022] [Indexed: 11/15/2022] Open
Abstract
Background Insomnia, eating disorders, heart problems and even strokes are just some of the illnesses that reveal the negative impact of stress overload on health and well-being. Early detection of stress is therefore of utmost importance. Whereas the gold-standard for detecting stress is by means of questionnaires, more recent work uses wearable sensors to find continuous and qualitative physical markers of stress. As some physiological stress responses, e.g. increased heart rate or sweating and chills, might also occur when doing sports, a more profound approach is needed for stress detection than purely considering physiological data. Methods In this paper, we analyse the added value of context information during stress detection from wearable data. We do so by comparing the performance of models trained purely on physiological data and models trained on physiological and context data. We consider the user’s activity and hours of sleep as context information, where we compare the influence of user-given context versus machine learning derived context. Results Context-aware models reach higher accuracy and lower standard deviations in comparison to the baseline (physiological) models. We also observe higher accuracy and improved weighted F1 score when incorporating machine learning predicted, instead of user-given, activities as context information. Conclusions In this paper we show that considering context information when performing stress detection from wearables leads to better performance. We also show that it is possible to move away from human labeling and rely only on the wearables for both physiology and context. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-02010-5.
Collapse
Affiliation(s)
- Marija Stojchevska
- IDLab, Ghent University - imec, Technologiepark Zwijnaarde 126, Ghent, Belgium.
| | - Bram Steenwinckel
- IDLab, Ghent University - imec, Technologiepark Zwijnaarde 126, Ghent, Belgium
| | | | - Mathias De Brouwer
- IDLab, Ghent University - imec, Technologiepark Zwijnaarde 126, Ghent, Belgium
| | - Annelies Goris
- OnePlanet Research Center, imec, Bronland 10, 6708, Wageningen, The Netherlands
| | - Filip De Turck
- IDLab, Ghent University - imec, Technologiepark Zwijnaarde 126, Ghent, Belgium
| | - Sofie Van Hoecke
- IDLab, Ghent University - imec, Technologiepark Zwijnaarde 126, Ghent, Belgium
| | - Femke Ongenae
- IDLab, Ghent University - imec, Technologiepark Zwijnaarde 126, Ghent, Belgium
| |
Collapse
|
4
|
Li Y, Zhou X, Ma J, Ma X, Cheng P, Gong T, Li C. Distinguished representation of identical mentions in bio-entity coreference resolution. BMC Med Inform Decis Mak 2022; 22:116. [PMID: 35501781 PMCID: PMC9063119 DOI: 10.1186/s12911-022-01862-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/20/2022] [Indexed: 12/04/2022] Open
Abstract
Background Bio-entity Coreference Resolution (CR) is a vital task in biomedical text mining. An important issue in CR is the differential representation of identical mentions as their similar representations may make the coreference more puzzling. However, when extracting features, existing neural network-based models may bring additional noise to the distinction of identical mentions since they tend to get similar or even identical feature representations. Methods We propose a context-aware feature attention model to distinguish similar or identical text units effectively for better resolving coreference. The new model can represent the identical mentions based on different contexts by adaptively exploiting features, which enables the model reduce the text noise and capture the semantic information effectively. Results The experimental results show that the proposed model brings significant improvements on most of the baseline for coreference resolution and mention detection on the BioNLP dataset and CRAFT-CR dataset. The empirical studies further demonstrate its superior performance on the differential representation and coreferential link of identical mentions. Conclusions Identical mentions impose difficulties on the current methods of Bio-entity coreference resolution. Thus, we propose the context-aware feature attention model to better distinguish identical mentions and achieve superior performance on both coreference resolution and mention detection, which will further improve the performance of the downstream tasks.
Collapse
Affiliation(s)
- Yufei Li
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Xiangyu Zhou
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Jie Ma
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Xiaoyong Ma
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Pengzhen Cheng
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Tieliang Gong
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Chen Li
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China. .,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China. .,Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
| |
Collapse
|
5
|
De Brouwer M, Vandenbussche N, Steenwinckel B, Stojchevska M, Van Der Donckt J, Degraeve V, Vaneessen J, De Turck F, Volckaert B, Boon P, Paemeleire K, Van Hoecke S, Ongenae F. mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients. BMC Med Inform Decis Mak 2022; 22:87. [PMID: 35361224 PMCID: PMC8969243 DOI: 10.1186/s12911-022-01813-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 03/09/2022] [Indexed: 12/04/2022] Open
Abstract
Background The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data. Methods The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables’ data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented. Results In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored. Conclusions Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection. Trial registration This trial was retrospectively registered with number NCT04949204 on 24 June 2021 at www.clinicaltrials.gov. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01813-w.
Collapse
Affiliation(s)
| | - Nicolas Vandenbussche
- Department of Neurology, Ghent University Hospital, 9000, Ghent, Belgium.,4BRAIN, Institute for Neuroscience, Department of Head and Skin, Ghent University, 9000, Ghent, Belgium
| | | | | | | | - Vic Degraeve
- IDLab, Ghent University - imec, 9052, Ghent, Belgium
| | | | | | | | - Paul Boon
- Department of Neurology, Ghent University Hospital, 9000, Ghent, Belgium.,4BRAIN, Institute for Neuroscience, Department of Head and Skin, Ghent University, 9000, Ghent, Belgium
| | - Koen Paemeleire
- Department of Neurology, Ghent University Hospital, 9000, Ghent, Belgium
| | | | - Femke Ongenae
- IDLab, Ghent University - imec, 9052, Ghent, Belgium
| |
Collapse
|
6
|
Ponce V, Abdulrazak B. Ambient intelligence governance review: from service-oriented to self-service. PeerJ Comput Sci 2022; 8:e788. [PMID: 35111905 PMCID: PMC8771785 DOI: 10.7717/peerj-cs.788] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/28/2021] [Indexed: 06/14/2023]
Abstract
The current generation of connected devices and the Internet of Things augment people's capabilities through ambient intelligence. Ambient Intelligence (AmI) support systems contain applications consuming available services in the environment to serve users. A well-known design of these applications follows a service architecture style and implement artificial intelligence mechanisms to maintain an awareness of the context: The service architecture style enables the distribution of capabilities and facilitates interoperability. Intelligence and context-awareness provide an adaptation of the environment to improve the interaction. Smart objects in distributed deployments and the increasing machine awareness of devices and people context also lead us to architectures, including self-governed policies providing self-service. We have systematically reviewed and analyzed ambient system governance considering service-oriented architecture (SOA) as a reference model. We applied a systematic mapping process obtaining 198 papers for screening (out of 712 obtained after conducting searches in research databases). We then reviewed and categorized 68 papers related to 48 research projects selected by fulfilling ambient intelligence and SOA principles and concepts. This paper presents the result of our analysis, including the existing governance designs, the distribution of adopted characteristics, and the trend to incorporate service in the context-aware process. We also discuss the identified challenges and analyze research directions.
Collapse
|
7
|
Wu Y, Xia T, Jatowt A, Zhang H, Feng X, Shibasaki R, Kim KS. Context-aware heatstroke relief station placement and route optimization for large outdoor events. Int J Health Geogr 2021; 20:23. [PMID: 34034758 PMCID: PMC8147018 DOI: 10.1186/s12942-021-00275-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/06/2021] [Indexed: 11/10/2022] Open
Abstract
Background Heatstroke is becoming an increasingly serious threat to outdoor activities, especially, at the time of large events organized during summer, including the Olympic Games or various types of happenings in amusement parks like Disneyland or other popular venues. The risk of heatstroke is naturally affected by a high temperature, but it is also dependent on various other contextual factors such as the presence of shaded areas along traveling routes or the distribution of relief stations. The purpose of the study is to develop a method to reduce the heatstroke risk of pedestrians for large outdoor events by optimizing relief station placement, volume scheduling and route. Results Our experiments conducted on the planned site of the Tokyo Olympics and simulated during the two weeks of the Olympics schedule indicate that planning routes and setting relief stations with our proposed optimization model could effectively reduce heatstroke risk. Besides, the results show that supply volume scheduling optimization can further reduce the risk of heatstroke. The route with the shortest length may not be the route with the least risk, relief station and physical environment need to be considered and the proposed method can balance these factors. Conclusions This study proposed a novel emergency service problem that can be applied in large outdoor event scenarios with multiple walking flows. To solve the problem, an effective method is developed and evaluates the heatstroke risk in outdoor space by utilizing context-aware indicators which are determined by large and heterogeneous data including facilities, road networks and street view images. We propose a Mixed Integer Nonlinear Programming model for optimizing routes of pedestrians, determining the location of relief stations and the supply volume in each relief station. The proposed method can help organizers better prepare for the event and pedestrians participate in the event more safely.
Collapse
Affiliation(s)
- Yan Wu
- The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, 277-0882, Japan.,Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, 710049, Shaanxi, China
| | - Tianqi Xia
- The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, 277-0882, Japan. .,National Institute of Advanced Industrial Science and Technology, Aomi, Koto, Tokyo, 135-0064, Japan.
| | - Adam Jatowt
- National Institute of Advanced Industrial Science and Technology, Aomi, Koto, Tokyo, 135-0064, Japan.,University of Innsbruck, Innrain 52, 6020, Innsbruck, Austria
| | - Haoran Zhang
- The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, 277-0882, Japan
| | - Xiao Feng
- Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, 710049, Shaanxi, China
| | - Ryosuke Shibasaki
- The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, 277-0882, Japan
| | - Kyoung-Sook Kim
- National Institute of Advanced Industrial Science and Technology, Aomi, Koto, Tokyo, 135-0064, Japan
| |
Collapse
|
8
|
Campana MG, Delmastro F. ContextLabeler dataset: Physical and virtual sensors data collected from smartphone usage in-the-wild. Data Brief 2021; 37:107164. [PMID: 34113703 PMCID: PMC8170106 DOI: 10.1016/j.dib.2021.107164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 05/19/2021] [Indexed: 11/24/2022] Open
Abstract
This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment.
Collapse
|
9
|
Abstract
When the COVID-19 coronavirus hit, the context-aware application users were willing to relax their context privacy preferences during the lockdown to cope their lives while staying home. Such disturbance in the privacy behavior affected the performance of Machine Learning (ML) algorithm that is trained on normal behavior. In this paper, we present the impact of the pandemic on the efficiency of the learning algorithm implementation of a privacy protection system. The system is composed of three modules, in this work we focus on Privacy Preferences Manager (PPM) module which is implemented using hybrid methodology based on a Statistical Model (SM) and Logistic Regression (LR) learning algorithm. The efficiency of the hybrid methodology is assessed using two real-world datasets collected prior and during the COVID-19 pandemic. The results show that the pandemic significantly impacted the efficiency of the hybrid methodology by 13.05% and 15.22% for the accuracy and F1 score respectively.
Collapse
Affiliation(s)
| | - Tahani Hussain
- Kuwait Institute for Scientific Research, P.O.Box 24885 Safat 13109, Kuwait
| |
Collapse
|
10
|
Kaffash-Charandabi N, Alesheikh AA, Sharif M. A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals' contexts. Environ Sci Pollut Res Int 2019; 26:7525-7539. [PMID: 30656587 DOI: 10.1007/s11356-019-04185-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
Air pollutants and allergens are the main stimuli that have considerable effects on asthmatic patients' health. Seamless monitoring of patients' conditions and the surrounding environment, limiting their exposure to allergens and irritants, and reducing the exacerbation of symptoms can aid patients to deal with asthma better. In this context, ubiquitous healthcare monitoring systems can provide any service to any user everywhere and every time through any device and network. In this regard, this research established a GIS-based outdoor asthma monitoring framework in light of ubiquitous systems. The proposed multifaceted model was designed in three layers: (1) pre-processing, for cleaning and interpolating data, (2) reasoning, for deducing knowledge and extract contextual information from data, and (3) prediction, for estimating the asthmatic conditions of patients ubiquitously. The effectiveness of the proposed model is assessed by applying it on a real dataset that comprised of internal context information including patients' personal information (age, gender, height, medical history), patients' locations, and their peak expiratory flow (PEF) values, as well as external context information including air pollutant data (O3, SO2, NO2, CO, PM10), meteorological data (temperature, pressure, humidity), and geographic information related to the city of Tehran, Iran. With more than 92% and 93% accuracies in reasoning and estimation mechanism, respectively, the proposed method showed remarkably effective in asthma monitoring and management.
Collapse
Affiliation(s)
- Neda Kaffash-Charandabi
- Department of Geomatics Engineering, Marand Technical College, Tabriz University, Tabriz, Iran.
| | - Ali Asghar Alesheikh
- Department of Geospatial Information Systems, Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mohammad Sharif
- Department of Geography, Faculty of Literature and Human Science, University of Hormozgan, Bandar Abbas, Iran
| |
Collapse
|
11
|
Ho BJ, Nikzad N, Balaji B, Srivastava M. Emu: Engagement Modeling for User Studies. Proc ACM Int Conf Ubiquitous Comput 2017; 2017:959-964. [PMID: 29629432 PMCID: PMC5889142 DOI: 10.1145/3123024.3124568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed Emu, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the Emu API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.
Collapse
Affiliation(s)
- Bo-Jhang Ho
- University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nima Nikzad
- Scripps Translational Science Institute, La Jolla, CA 92037, USA
| | - Bharathan Balaji
- University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Mani Srivastava
- University of California, Los Angeles, Los Angeles, CA 90095, USA
| |
Collapse
|
12
|
Chen D, Jin D, Goh TT, Li N, Wei L. Context-Awareness Based Personalized Recommendation of Anti-Hypertension Drugs. J Med Syst 2016; 40:202. [PMID: 27473866 DOI: 10.1007/s10916-016-0560-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 07/20/2016] [Indexed: 10/21/2022]
Abstract
The World Health Organization estimates that almost one-third of the world's adult population are suffering from hypertension which has gradually become a "silent killer". Due to the varieties of anti-hypertensive drugs, patients are interested in how these drugs can be selected to match their respective conditions. This study provides a personalized recommendation service system of anti-hypertensive drugs based on context-awareness and designs a context ontology framework of the service. In addition, this paper introduces a Semantic Web Rule Language (SWRL)-based rule to provide high-level context reasoning and information recommendation and to overcome the limitation of ontology reasoning. To make the information recommendation of the drugs more personalized, this study also devises three categories of information recommendation rules that match different priority levels and uses a ranking algorithm to optimize the recommendation. The experiment conducted shows that combining the anti-hypertensive drugs personalized recommendation service context ontology (HyRCO) with the optimized rule reasoning can achieve a higher-quality personalized drug recommendation service. Accordingly this exploratory study of the personalized recommendation service for hypertensive drugs and its method can be easily adopted for other diseases.
Collapse
|