- Email: [email protected]
Temporal data analytics
YJBIN 2587
No. of Pages 2, Model 5G
5 July 2016 Journal of Biomedical Informatics xxx (2016) xxx–xxx 1
Contents lists available at ScienceDirect
Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin
2
Call for Papers
5 4 6 7
Temporal biomedical data analytics
8
1. Due date for submissions: November 1, 2016
9
Temporal issues are intrinsic to the biomedical domain, due to its inherent longitudinal nature. This observation holds whether referring to Electronic Health Records (EHRs) emphasizing the slowly accumulating data in chronic-care domains such as the management of diabetes patients, or the data accumulating rapidly in fast-paced domains such as in the Intensive Care Unit (ICU), or the data collected continuously in varying forms by personal monitoring devices (e.g., smart-watches, wearable devices, etc.). Although the temporal aspect of biomedical data is well recognized as essential, and several methods for time series analysis, temporal reasoning, and temporal data mining have been developed over the past decades, there is much room for additional contributions [1–3]. The data mining [3–5] and biomedical informatics [6–12] literatures increasingly feature studies dealing with the main challenges related to analyzing the data of EHRs that include large numbers of variables, varying sampling frequencies, and different types of events, either instantaneous or having a duration. These challenges had necessitated the use of methods from multiple scientific fields, such as temporal abstraction, frequent-pattern mining, temporal regression models, hidden Markov models, and more. In addition to enhancing the computational efficiency of the analytical methods, the investigation of such techniques in timeoriented domains promises to improve the quality of patient care through the discovery of meaningful clinical knowledge. Thus, in this call for papers, we encourage participation from researchers in all fields related to medical data research, including mainstream temporal data mining, time series processing, and more. The topics of this special issue include, but are not limited to, the following:
59
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 47 45 46 48
Temporal Pattern Discovery o Sequential Mining o Time Intervals Mining o HMM patterns o Streams Data Mining o Periodic Pattern Mining
49 50
Patient Behavior Analysis
51 52 53 54 55 56
Time Series Analysis o Univariate time series o Multivariate time series o Numeric and regression analysis o Symbolic and discretization based methods http://dx.doi.org/10.1016/j.jbi.2016.07.002
o Irregular temporal data analysis o Imputation for temporal data
57 58 60 61
Temporal Reasoning o Knowledge-based temporal reasoning o Knowledge-based temporal abstraction o Complex Events Processing
62 66 63
Big Data Temporal Data Mining o Parallelizing Temporal Data Mining
69
64 65 67 71 68 70 72 73
Causality Analysis
74 75
Prediction and Forecasting Temporal Data Retrieval o Dynamic Time Warping o Time Series Similarity
76 77 81 78 79 80 82 83
JBI is particularly interested in publishing methodological reviews on topics relevant to special issues, and we encourage submissions of this type.
84
2. Peer-review process
87
All submitted papers must be original and will go through a rigorous peer-review process with at least two reviewers. All submissions should follow the guidelines for authors available through a link on the Journal of Biomedical Informatics web site (https:// www.elsevier.com/journals/journal-of-biomedical-informatics/). JBI’s editorial policy is also outlined on that page and will be strictly followed by special issue reviewers. Note in particular that JBI emphasizes the publication of papers that introduce innovative and generalizable methods of interest to the informatics community. Specific applications can be described to motivate the methodology being introduced, but papers the focus solely on a specific application are not suitable for JBI.
88
3. Submissions
100
Authors must submit their papers via the online Elsevier Editorial System (EES) at http://ees.elsevier.com/jbi by November 1, 2016. Authors can register and upload their text, tables, and figures as well as subsequent revisions through this website. Potential authors may contact the Publishing Services Coordinator in the journal’s editorial office ([email protected]) for questions regarding this process. Authors are also welcome to discuss their potential submissions with the editors by sending an email to
101
85 86
89 90 91 92 93 94 95 96 97 98 99
102 103 104 105 106 107 108
YJBIN 2587
No. of Pages 2, Model 5G
5 July 2016 2
Call for Papers / Journal of Biomedical Informatics xxx (2016) xxx–xxx
110
Robert Moskovitch ([email protected]) regarding the potential fit of their submission with this special issue.
111
References
112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
[1] P.B. Jensen, L.J. Jensen, S. Brunak, Mining electronic health records: towards better research applications and clinical care, Nat. Rev. Genet. 13 (6) (2012). [2] G. Hripcsak, D. Albers, A. Perotte, Parameterizing time in electronic health records studies, J. Am. Med. Inform. Assoc. 22 (2015) 794–804. [3] R. Moskovitch, Y. Shahar, Classification driven temporal discretization of multivariate time series, Data Min. Knowl. Disc. 29 (4) (2015) 871–913. [4] F. Wang, N. Lee, J. Hu, J. Sun, S. Ebadollahi, A.F. Laine, A framework for mining signatures from event sequences and its applications in healthcare data, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013) 272–285. [5] R. Moskovitch, C. Walsh, F. Wang, G. Hripsack, N. Tatonetti, Outcomes prediction via time intervals related patterns, in: IEEE International Conference on Data Mining (ICDM), Atlantic City, USA, 2015. [6] A. Singh, G. Nadkarni, O. Gottesman, S.B. Ellis, E.P. Bottinger, J.V. Guttag, Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration, J. Biomed. Inform. 53 (2015) 220–228. [7] L. Sacchi, C. Larizza, P. Magni, R. Bellazzi, Precedence temporal networks to represent temporal relationships in gene expression data, J. Biomed. Inform. 40 (2007) 6. [8] Y. Lin, H. Chen, R.A. Brown, MedTime: a temporal information extraction system for clinical narratives, J. Biomed. Inform. 46 (2013). [9] M. Hauskrecht, I. Batal, M. Valko, S. Visweswaran, G.F. Cooper, G. Clermont, Outlier detection for patient monitoring and altering, J. Biomed. Inform. 46 (2013) 1. [10] J. Sun, C.D. McNaughton, P. Zhang, A. Perer, A. Gkoulalas-Divanis, J.C. Denny, J. Kirby, T. Lasko, A. Saip, B.A. Malin, Predicting changes in hypertension control using electronic health records from a chronic disease management program, J. Am. Med. Inform. Assoc. 21 (2014) 337–344.
109
[11] M. Last, O. Tosas, T.G. Cassarino, Z. Kozlakidis, J. Edgeworth, Evolving classification of intensive care patients from event data, Artif. Intell. Med. 69 (2016). [12] J.L. Warner, P. Zhang, J. Liu, G. Alterovitz, Classification of hospital acquired complications using temporal clinical information from a large electronic health record, J. Biomed. Inform. 59 (2016) 209–217.
139 140 141 142 143 144
Guest Editors Robert Moskovitch Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel E-mail address: [email protected]
145
Fei Wang Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA E-mail address: [email protected]
150
Yuval Shahar Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel E-mail address: [email protected]
154
George Hripcsak Department of Biomedical Informatics, Columbia College of Physicians & Surgeons, New York, NY, USA E-mail address: [email protected]
158
146 147 148 149
151 152 153
155 156 157
159 160 161 162
Available online xxxx
163 164 165
No. of Pages 2, Model 5G
5 July 2016 Journal of Biomedical Informatics xxx (2016) xxx–xxx 1
Contents lists available at ScienceDirect
Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin
2
Call for Papers
5 4 6 7
Temporal biomedical data analytics
8
1. Due date for submissions: November 1, 2016
9
Temporal issues are intrinsic to the biomedical domain, due to its inherent longitudinal nature. This observation holds whether referring to Electronic Health Records (EHRs) emphasizing the slowly accumulating data in chronic-care domains such as the management of diabetes patients, or the data accumulating rapidly in fast-paced domains such as in the Intensive Care Unit (ICU), or the data collected continuously in varying forms by personal monitoring devices (e.g., smart-watches, wearable devices, etc.). Although the temporal aspect of biomedical data is well recognized as essential, and several methods for time series analysis, temporal reasoning, and temporal data mining have been developed over the past decades, there is much room for additional contributions [1–3]. The data mining [3–5] and biomedical informatics [6–12] literatures increasingly feature studies dealing with the main challenges related to analyzing the data of EHRs that include large numbers of variables, varying sampling frequencies, and different types of events, either instantaneous or having a duration. These challenges had necessitated the use of methods from multiple scientific fields, such as temporal abstraction, frequent-pattern mining, temporal regression models, hidden Markov models, and more. In addition to enhancing the computational efficiency of the analytical methods, the investigation of such techniques in timeoriented domains promises to improve the quality of patient care through the discovery of meaningful clinical knowledge. Thus, in this call for papers, we encourage participation from researchers in all fields related to medical data research, including mainstream temporal data mining, time series processing, and more. The topics of this special issue include, but are not limited to, the following:
59
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 47 45 46 48
Temporal Pattern Discovery o Sequential Mining o Time Intervals Mining o HMM patterns o Streams Data Mining o Periodic Pattern Mining
49 50
Patient Behavior Analysis
51 52 53 54 55 56
Time Series Analysis o Univariate time series o Multivariate time series o Numeric and regression analysis o Symbolic and discretization based methods http://dx.doi.org/10.1016/j.jbi.2016.07.002
o Irregular temporal data analysis o Imputation for temporal data
57 58 60 61
Temporal Reasoning o Knowledge-based temporal reasoning o Knowledge-based temporal abstraction o Complex Events Processing
62 66 63
Big Data Temporal Data Mining o Parallelizing Temporal Data Mining
69
64 65 67 71 68 70 72 73
Causality Analysis
74 75
Prediction and Forecasting Temporal Data Retrieval o Dynamic Time Warping o Time Series Similarity
76 77 81 78 79 80 82 83
JBI is particularly interested in publishing methodological reviews on topics relevant to special issues, and we encourage submissions of this type.
84
2. Peer-review process
87
All submitted papers must be original and will go through a rigorous peer-review process with at least two reviewers. All submissions should follow the guidelines for authors available through a link on the Journal of Biomedical Informatics web site (https:// www.elsevier.com/journals/journal-of-biomedical-informatics/). JBI’s editorial policy is also outlined on that page and will be strictly followed by special issue reviewers. Note in particular that JBI emphasizes the publication of papers that introduce innovative and generalizable methods of interest to the informatics community. Specific applications can be described to motivate the methodology being introduced, but papers the focus solely on a specific application are not suitable for JBI.
88
3. Submissions
100
Authors must submit their papers via the online Elsevier Editorial System (EES) at http://ees.elsevier.com/jbi by November 1, 2016. Authors can register and upload their text, tables, and figures as well as subsequent revisions through this website. Potential authors may contact the Publishing Services Coordinator in the journal’s editorial office ([email protected]) for questions regarding this process. Authors are also welcome to discuss their potential submissions with the editors by sending an email to
101
85 86
89 90 91 92 93 94 95 96 97 98 99
102 103 104 105 106 107 108
YJBIN 2587
No. of Pages 2, Model 5G
5 July 2016 2
Call for Papers / Journal of Biomedical Informatics xxx (2016) xxx–xxx
110
Robert Moskovitch ([email protected]) regarding the potential fit of their submission with this special issue.
111
References
112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
[1] P.B. Jensen, L.J. Jensen, S. Brunak, Mining electronic health records: towards better research applications and clinical care, Nat. Rev. Genet. 13 (6) (2012). [2] G. Hripcsak, D. Albers, A. Perotte, Parameterizing time in electronic health records studies, J. Am. Med. Inform. Assoc. 22 (2015) 794–804. [3] R. Moskovitch, Y. Shahar, Classification driven temporal discretization of multivariate time series, Data Min. Knowl. Disc. 29 (4) (2015) 871–913. [4] F. Wang, N. Lee, J. Hu, J. Sun, S. Ebadollahi, A.F. Laine, A framework for mining signatures from event sequences and its applications in healthcare data, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013) 272–285. [5] R. Moskovitch, C. Walsh, F. Wang, G. Hripsack, N. Tatonetti, Outcomes prediction via time intervals related patterns, in: IEEE International Conference on Data Mining (ICDM), Atlantic City, USA, 2015. [6] A. Singh, G. Nadkarni, O. Gottesman, S.B. Ellis, E.P. Bottinger, J.V. Guttag, Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration, J. Biomed. Inform. 53 (2015) 220–228. [7] L. Sacchi, C. Larizza, P. Magni, R. Bellazzi, Precedence temporal networks to represent temporal relationships in gene expression data, J. Biomed. Inform. 40 (2007) 6. [8] Y. Lin, H. Chen, R.A. Brown, MedTime: a temporal information extraction system for clinical narratives, J. Biomed. Inform. 46 (2013). [9] M. Hauskrecht, I. Batal, M. Valko, S. Visweswaran, G.F. Cooper, G. Clermont, Outlier detection for patient monitoring and altering, J. Biomed. Inform. 46 (2013) 1. [10] J. Sun, C.D. McNaughton, P. Zhang, A. Perer, A. Gkoulalas-Divanis, J.C. Denny, J. Kirby, T. Lasko, A. Saip, B.A. Malin, Predicting changes in hypertension control using electronic health records from a chronic disease management program, J. Am. Med. Inform. Assoc. 21 (2014) 337–344.
109
[11] M. Last, O. Tosas, T.G. Cassarino, Z. Kozlakidis, J. Edgeworth, Evolving classification of intensive care patients from event data, Artif. Intell. Med. 69 (2016). [12] J.L. Warner, P. Zhang, J. Liu, G. Alterovitz, Classification of hospital acquired complications using temporal clinical information from a large electronic health record, J. Biomed. Inform. 59 (2016) 209–217.
139 140 141 142 143 144
Guest Editors Robert Moskovitch Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel E-mail address: [email protected]
145
Fei Wang Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA E-mail address: [email protected]
150
Yuval Shahar Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel E-mail address: [email protected]
154
George Hripcsak Department of Biomedical Informatics, Columbia College of Physicians & Surgeons, New York, NY, USA E-mail address: [email protected]
158
146 147 148 149
151 152 153
155 156 157
159 160 161 162
Available online xxxx
163 164 165