1@article{stats1,
author={Liu, Li
and Oza, Shefali
and Hogan, Dan
and Chu, Yue
and Perin, Jamie
and Zhu, Jun
and Lawn, Joy E.
and Cousens, Simon
and Mathers, Colin
and Black, Robert E.},
title={Global, regional, and national causes of under-5 mortality in 2000\&\#x2013;15: an updated systematic analysis with implications for the Sustainable Development Goals},
journal={The Lancet},
year={2016},
month={Dec},
day={17},
publisher={Elsevier},
volume={388},
number={10063},
pages={3027-3035},
issn={0140-6736},
doi={10.1016/S0140-6736(16)31593-8},
url={https://doi.org/10.1016/S0140-6736(16)31593-8}
}




@article{ stats2,
author = {"Pfunter, A
and Wier, LM
and Steiner, C."},
title = {"Costs for Hospital Stays in the United States, 2011"},
year = {2013} ,
month={Dec},
url={http://www.hcup-us.ahrq.gov/reports/statbriefs/sb168-Hospital-Costs-United-States-2011.pdf}
}

@article{mimic,
author = {Johnson, AEW
and Pollard, TJ
and Shen, Land 
Lehman, L
and Feng, M
and Ghassemi, M
and Moody, B
and Szolovits, P
and Celi, LA
and Mark RG},
note = {Scientific Data},
year = {2016},
url = {http://www.nature.com/articles/sdata201635},
comment = {DOI: 10.1038/sdata.2016.35},
}

@article{AptaSDSS,
title = {AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction},
journal = {Expert Systems with Applications},
volume = {34},
number = {4},
pages = {2465 - 2479},
year = {2008},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2007.04.015},
url = {http://www.sciencedirect.com/science/article/pii/S095741740700139X},
author = {Jae-Hong Eom and Sung-Chun Kim and Byoung-Tak Zhang"},
keywords = {Clinical decision support system (CDSS), Cardiovascular disease, Classifier ensemble, Support vector machines, Neural networks, Decision trees, Bayesian networks, Machine learning},
abstract = {Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94\%) and comparably small prediction difference intervals (<6\%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.}
}

@inproceedings{Dementia,
 author = {Yan, Chunli and Lindgren, Helena},
 title = {Diagnostic Reasoning Guided by a Decision-Support System: A Case Study},
 booktitle = {Proceedings of the European Conference on Cognitive Ergonomics 2017},
 series = {ECCE 2017},
 year = {2017},
 isbn = {978-1-4503-5256-7},
 location = {Ume, Sweden},
 pages = {25--30},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/3121283.3121307},
 doi = {10.1145/3121283.3121307},
 acmid = {3121307},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Clinical decision support systems, Cognition, Continued medical education, Dementia, Diagnostic reasoning},
} 

@inproceedings{DiagTree,
 author = {Kim, Yejin and Choi, Jingyun and Chong, Yosep and Jiang, Xiaoqian and Yu, Hwanjo},
 title = {DiagTree: Diagnostic Tree for Differential Diagnosis},
 booktitle = {Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
 series = {CIKM '17},
 year = {2017},
 isbn = {978-1-4503-4918-5},
 location = {Singapore, Singapore},
 pages = {1179--1188},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/3132847.3132924},
 doi = {10.1145/3132847.3132924},
 acmid = {3132924},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {decision process, discrete optimization, early diagnosis},
} 

@book{mette,
author = {author, Kim 1974-},
publisher = {Palgrave Macmillan},
address = {London ; New York, NY},
isbn = {9781137463005, 1137463007},
title = {Theatre and feminism},
date = 2016,
language = {eng},
keywords = {Feminism and theater, Feminist literary criticism, PERFORMING ARTS / Theater / History & Criticism}
}

@Article{Ebadollahi2010,
author={Ebadollahi, Shahram
and Sun, Jimeng
and Gotz, David
and Hu, Jianying
and Sow, Daby
and Neti, Chalapathy},
title={Predicting Patient's Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics},
journal={AMIA Annu Symp Proc},
year={2010},
month={Nov},
day={13},
publisher={American Medical Informatics Association},
volume={2010},
pages={192-196},
abstract={Providing near-term prognostic insight to clinicians helps them to better assess the near-term impact of their decisions and potential impending events affecting the patient. In this work, we present a novel system, which leverages inter-patient similarity for retrieving patients who display similar trends in their physiological time-series data. Data from the retrieved patient cohort is then used to project patient data into the future to provide insights for the query patient. The proposed approach and system were tested using the MIMIC II database, which consists of physiological waveforms, and accompanying clinical data obtained for ICU patients. In the experiments we report the effectiveness of the inter-patient similarity measure and the accuracy of the projection of patients' data. We also discuss the visual interface that conveys the near-term prognostic decision support to the user.},
note={21346967[pmid]},
note={PMC3041306[pmcid]},
issn={1942-597X},
url={https://www.ncbi.nlm.nih.gov/pubmed/21346967}
}

@inproceedings{Multivariate,
 author = {Liu, Zitao and Hauskrecht, Milos},
 title = {A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection},
 booktitle = {Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
 series = {CIKM '17},
 year = {2017},
 isbn = {978-1-4503-4918-5},
 location = {Singapore, Singapore},
 pages = {1169--1177},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/3132847.3132859},
 doi = {10.1145/3132847.3132859},
 acmid = {3132859},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {forecasting, multivariate time series, personalized medicine},
} 

@inproceedings{TemporalPatterns,
 author = {Ghalwash, Mohamed F. and Radosavljevic, Vladan and Obradovic, Zoran},
 title = {Utilizing Temporal Patterns for Estimating Uncertainty in Interpretable Early Decision Making},
 booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '14},
 year = {2014},
 isbn = {978-1-4503-2956-9},
 location = {New York, New York, USA},
 pages = {402--411},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2623330.2623694},
 doi = {10.1145/2623330.2623694},
 acmid = {2623694},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {earliness, interpretability, reliability, time series, uncertainty},
} 


@phdthesis{ShaharTA,
 author = {Shahar, Yuval},
 title = {A Knowledge-based Method for Temporal Abstraction of Clinical Data},
 year = {1995},
 note = {UMI Order No. GAX95-16915},
 publisher = {Stanford University},
 address = {Stanford, CA, USA},
} 

@inproceedings{NatLang,
  TITLE = {{Using Natural Language Generation Technology to Improve Information Flows in Intensive Care Units}},
  AUTHOR = {Hunter, Jim and Gatt, Albert and Portet, Fran{\c c}ois and Reiter, Ehud and Sripada, Somayajulu},
  URL = {https://hal.archives-ouvertes.fr/hal-00998608},
  BOOKTITLE = {{ECAI}},
  ADDRESS = {Patras, Greece},
  PAGES = {678-682},
  YEAR = {2008},
  MONTH = Jul,
  PDF = {https://hal.archives-ouvertes.fr/hal-00998608/file/pais_2008_bt.pdf},
  HAL_ID = {hal-00998608},
  HAL_VERSION = {v1},
}





@article{TemporalAbstraction,
title = "Temporal abstraction in intelligent clinical data analysis: A survey",
journal = "Artificial Intelligence in Medicine",
volume = "39",
number = "1",
pages = "1 - 24",
year = "2007",
issn = "0933-3657",
doi = "https://doi.org/10.1016/j.artmed.2006.08.002",
url = "http://www.sciencedirect.com/science/article/pii/S0933365706001345",
author = "Michael Stacey and Carolyn McGregor",
keywords = "Temporal abstraction, Intelligent data analysis, Patient monitoring, Decision support systems, Intensive care, Temporal reasoning",
abstract = "Summary
Objective
Intelligent clinical data analysis systems require precise qualitative descriptions of data to enable effective and context sensitive interpretation to take place. Temporal abstraction (TA) provides the means to achieve such descriptions, which can then be used as input to a reasoning engine where they are evaluated against a knowledge base to arrive at possible clinical hypotheses. This paper surveys previous research into the development of intelligent clinical data analysis systems that incorporate TA mechanisms and presents research synergies and trends across the research reviewed, especially those associated with the multi-dimensional nature of real-time patient data streams. The motivation for this survey is case study based research into the development of an intelligent real-time, high-frequency patient monitoring system to provide detection of temporal patterns within multiple patient data streams.
Results
The survey was based on factors that are of importance to broaden research into temporal abstraction and on characteristics we believe will assume an increasing level of importance for future clinical IDA systems. These factors were: aspects of the data that is abstracted such as source domain and sample frequency, complexity available within abstracted patterns, dimensionality of the TA and data environment and the knowledge and reasoning underpinning TA processes.
Conclusion
It is evident from the review that for intelligent clinical data analysis systems to progress into the future where clinical environments are becoming increasingly data-intensive, the ability for managing multi-dimensional aspects of data at high observation and sample frequencies must be provided. Also, the detection of complex patterns within patient data requires higher levels of TA than are presently available. The conflicting matters of computational tractability and temporal reasoning within a real-time environment present a non-trivial problem for investigation in regard to these matters. Finally, to be able to fully exploit the value of learning new knowledge from stored clinical data through data mining and enable its application to data abstraction, the fusion of data mining and TA processes becomes a necessity."
}