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Nguyen TK, Ahmad Z, Kim JM. Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest. SENSORS (BASEL, SWITZERLAND) 2023; 23:9087. [PMID: 38005477 PMCID: PMC10674913 DOI: 10.3390/s23229087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 10/30/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023]
Abstract
In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy.
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Affiliation(s)
| | | | - Jong-Myon Kim
- Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea; (T.-K.N.); (Z.A.)
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2
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Zhang Y, Shirakawa M, Hara T. Generalized durative event detection on social media. J Intell Inf Syst 2023; 60:73-95. [PMID: 36818487 PMCID: PMC9927034 DOI: 10.1007/s10844-022-00730-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 11/26/2022]
Abstract
Given the recent availability of large volumes of social media discussions, finding temporal unusual phenomena, which can be called events, from such data is of great interest. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from their usual behavior, for a sustained period. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect durative events in time series in a general sense. In addition, we also provide an incremental version of the algorithm for the purpose of real-time detection. We test our approaches on synthetic data and two real-world tasks. With the synthetic dataset, we compare the performance of retrospective and incremental versions of the algorithm. In the first real-world task, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. In the second real-world task, we use the event captured to help improve the accuracy of stock market movement prediction. We show that our event-based approach has a clear advantage compared to other ways of adding social media information.
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Affiliation(s)
- Yihong Zhang
- Graduate School of Information Science and Technology, Multimedia Data Engineering Lab, Osaka University, Osaka, Japan
| | - Masumi Shirakawa
- Graduate School of Information Science and Technology, Multimedia Data Engineering Lab, Osaka University, Osaka, Japan
| | - Takahiro Hara
- Graduate School of Information Science and Technology, Multimedia Data Engineering Lab, Osaka University, Osaka, Japan
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3
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Casanova IJ, Campos M, Juarez JM, Gomariz A, Lorente-Ros M, Lorente JA. Using the diagnostic odds ratio to select multivariate sequential patterns in order to build an interpretable pattern-based classifier in a clinical domain (Preprint). JMIR Med Inform 2021; 10:e32319. [PMID: 35947437 PMCID: PMC9403826 DOI: 10.2196/32319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 02/26/2022] [Accepted: 03/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background It is important to exploit all available data on patients in settings such as intensive care burn units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients to predict their survival. However, pattern discovery algorithms generate a large number of patterns, of which only some are relevant for classification. Objective We propose to use the diagnostic odds ratio (DOR) to select multivariate sequential patterns used in the classification in a clinical domain, rather than employing frequency properties. Methods We used data obtained from the ICBU at the University Hospital of Getafe, where 6 temporal variables for 465 patients were registered every day during 5 days, and to model the evolution of these clinical variables, we used multivariate sequential patterns by applying 2 different discretization methods for the continuous attributes. We compared 4 ways in which to employ the DOR for pattern selection: (1) we used it as a threshold to select patterns with a minimum DOR; (2) we selected patterns whose differential DORs are higher than a threshold with regard to their extensions; (3) we selected patterns whose DOR CIs do not overlap; and (4) we proposed the combination of threshold and nonoverlapping CIs to select the most discriminative patterns. As a baseline, we compared our proposals with Jumping Emerging Patterns, one of the most frequently used techniques for pattern selection that utilizes frequency properties. Results We have compared the number and length of the patterns eventually selected, classification performance, and pattern and model interpretability. We show that discretization has a great impact on the accuracy of the classification model, but that a trade-off must be found between classification accuracy and the physicians’ capacity to interpret the patterns obtained. We have also identified that the experiments combining threshold and nonoverlapping CIs (Option 4) obtained the fewest number of patterns but also with the smallest size, thus implying the loss of an acceptable accuracy with regard to clinician interpretation. The best classification model according to the trade-off is a JRIP classifier with only 5 patterns (20 items) that was built using unsupervised correlation preserving discretization and differential DOR in a beam search for the best pattern. It achieves a specificity of 56.32% and an area under the receiver operating characteristic curve of 0.767. Conclusions A method for the classification of patients’ survival can benefit from the use of sequential patterns, as these patterns consider knowledge about the temporal evolution of the variables in the case of ICBU. We have proved that the DOR can be used in several ways, and that it is a suitable measure to select discriminative and interpretable quality patterns.
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Affiliation(s)
- Isidoro J Casanova
- AIKE Research Team (INTICO), Computer Science Faculty, University of Murcia, Murcia, Spain
| | - Manuel Campos
- AIKE Research Team (INTICO), Computer Science Faculty, University of Murcia, Murcia, Spain
- Murcian Bio-Health Institute (IMIB-Arrixaca), Murcia, Spain
- CIBERFES Fragilidad y Envejecimiento Saludable, Madrid, Spain
| | - Jose M Juarez
- AIKE Research Team (INTICO), Computer Science Faculty, University of Murcia, Murcia, Spain
| | | | - Marta Lorente-Ros
- Department of Medicine, Mount Sinai St Luke's-Roosevelt Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jose A Lorente
- Intensive Care Unit, University Hospital of Getafe, Getafe, Spain
- School of Medicine, European University of Madrid, Madrid, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Department of Bioengineering, Universidad Carlos III, Madrid, Spain
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4
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A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks. SENSORS 2021; 21:s21134267. [PMID: 34206373 PMCID: PMC8272188 DOI: 10.3390/s21134267] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 11/16/2022]
Abstract
Cyber-physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.
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Schvetz M, Fuchs L, Novack V, Moskovitch R. Outcomes prediction in longitudinal data: Study designs evaluation, use case in ICU acquired sepsis. J Biomed Inform 2021; 117:103734. [PMID: 33711544 DOI: 10.1016/j.jbi.2021.103734] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/23/2022]
Abstract
Outcomes' prediction in Electronic Health Records (EHR) and specifically in Critical Care is increasingly attracting more exploration and research. In this study, we used clinical data from the Intensive Care Unit (ICU), focusing on ICU acquired sepsis. Looking at the current literature, several evaluation approaches are reported, inspired by epidemiological designs, in which some do not always reflect real-life application's conditions. This problem seems relevant generally to outcomes' prediction in longitudinal EHR data, or generally longitudinal data, while in this study we focused on ICU data. Unlike in most previous studies that investigated all sepsis admissions, we focused specifically on ICU-Acquired Sepsis. Due to the sparse nature of the longitudinal data, we employed the use of Temporal Abstraction and Time Interval-Related Patterns discovery, which are further used as classification features. Two experiments were designed using three different outcomes prediction study designs from the literature, implementing various levels of real-life conditions to evaluate the prediction models. The first experiment focused on predicting whether a patient would suffer from ICU-acquired sepsis and when during her admission, given a sliding observation time window, and the comparison of the three study designs behavior. The second experiment focused only on predicting whether the patient will suffer from ICU-acquired sepsis, based on data taken relatively to his admission start time. Our results show that using Temporal Discretization for Classification (TD4C) led to better performance than using the Equal-Width Discretization, Knowledge-Based, or SAX. Also, using two states abstraction was better than three or four. Using the default Binary TIRP representation method performed better than Mean Duration, Horizontal Support, and horizontally normalized horizontal support. Using XGBoost as a classifier performed better than Logistic Regression, Neural Net, or Random Forest. Additionally, it is demonstrated why the use of case-crossover-control is most appropriate for real life application conditions evaluation, unlike other incomplete designs that may even result in "better performance".
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Affiliation(s)
- Maya Schvetz
- Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel.
| | - Lior Fuchs
- Medical Intensive Care Unit and Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Victor Novack
- Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Robert Moskovitch
- Department of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel.
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6
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Bonomi L, Fan L, Jiang X. Noise-tolerant similarity search in temporal medical data. J Biomed Inform 2020; 113:103667. [PMID: 33359112 DOI: 10.1016/j.jbi.2020.103667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 01/12/2023]
Abstract
Temporal medical data are increasingly integrated into the development of data-driven methods to deliver better healthcare. Searching such data for patterns can improve the detection of disease cases and facilitate the design of preemptive interventions. For example, specific temporal patterns could be used to recognize low-prevalence diseases, which are often under-diagnosed. However, searching these patterns in temporal medical data is challenging, as the data are often noisy, complex, and large in scale. In this work, we propose an effective and efficient solution to search for patients who exhibit conditions that resemble the input query. In our solution, we propose a similarity notion based on the Longest Common Subsequence (LCSS), which is used to measure the similarity between the query and the patient's temporal medical data and to ensure robustness against noise in the data. Our solution adopts locality sensitive hashing techniques to address the high dimensionality of medical data, by embedding the recorded clinical events (e.g., medications and diagnosis codes) into compact signatures. To perform pattern search in large EHR datasets, we propose a filtering approach based on tandem patterns, which effectively identifies candidate matches while discarding irrelevant data. The evaluations conducted using a real-world dataset demonstrate that our solution is highly accurate while significantly accelerating the similarity search.
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Affiliation(s)
- Luca Bonomi
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, United States of America.
| | - Liyue Fan
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, United States of America.
| | - Xiaoqian Jiang
- UTHealth School of Biomedical Informatics, Houston, United States of America.
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8
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Morid MA, Sheng ORL, Kawamoto K, Abdelrahman S. Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction. J Biomed Inform 2020; 111:103565. [DOI: 10.1016/j.jbi.2020.103565] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 08/27/2020] [Accepted: 09/07/2020] [Indexed: 01/20/2023]
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9
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Gan W, Lin JCW, Chao HC, Fournier-Viger P, Wang X, Yu PS. Utility-Driven Mining of Trend Information for Intelligent System. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2020. [DOI: 10.1145/3391251] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Useful knowledge, embedded in a database, is likely to change over time. Identifying the recent changes in temporal data can provide valuable up-to-date information to decision makers. Nevertheless, techniques for mining high-utility patterns (HUPs) seldom consider recency as a criterion to discover patterns. Thus, the traditional utility mining framework is inadequate for obtaining up-to-date insights about real-world data. In this article, we address this issue by introducing a novel framework, named utility-driven mining of recent/trend high-utility patterns (RUPs), in temporal databases for intelligent systems, based on user-specified minimum recency and minimum utility thresholds. The utility-driven RUP algorithm is based on novel global and conditional downward closure properties, and a recency-utility tree. Moreover, it adopts a vertical compact recency-utility list structure to store the information required by the mining process. The developed RUP algorithm recursively discovers recent high-utility patterns. It is also fast and consumes a small amount of memory due to its pattern discovery approach that does not generate candidates. Two improved versions of the algorithm with additional pruning strategies are also designed to speed up the discovery of patterns by reducing the search space. Results of a substantial experimental evaluation show that the proposed algorithm can efficiently identify all recent HUPs in large-scale databases, and that the improved algorithm performs best.
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Affiliation(s)
- Wensheng Gan
- Jinan University, Guangzhou, Guangdong Province, China
| | - Jerry Chun-Wei Lin
- Western Norway University of Applied Sciences, Inndalsveien, Bergen, Norway
| | | | | | - Xuan Wang
- Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Philip S. Yu
- University of Illinois at Chicago, Chicago, IL, United States
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Dagliati A, Geifman N, Peek N, Holmes JH, Sacchi L, Bellazzi R, Sajjadi SE, Tucker A. Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records. Artif Intell Med 2020; 108:101930. [PMID: 32972659 PMCID: PMC7536308 DOI: 10.1016/j.artmed.2020.101930] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 05/21/2020] [Accepted: 07/11/2020] [Indexed: 11/17/2022]
Abstract
Topological Data and Pseudo Time Series to discover Type 2 Diabetes temporal phenotypes. Temporal phenotypes inferred from state-space model based on hidden-states transitions. Study of states continuous transitions visually delivered in an easily explainable way. Mined phenotypes characterized by significant differences in disease deterioration.
Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify disease trajectories and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.
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Affiliation(s)
- Arianna Dagliati
- Centre for Health Informatics, University of Manchester, Manchester, United Kingdom; Manchester Molecular Pathology Innovation Centre, University of Manchester, United Kingdom; Department of Electrical, Computer & Biomedical Engineering University of Pavia, Italy.
| | - Nophar Geifman
- Centre for Health Informatics, University of Manchester, Manchester, United Kingdom
| | - Niels Peek
- Centre for Health Informatics, University of Manchester, Manchester, United Kingdom; NIHR Manchester Biomedical Research Centre, University of Manchester, United Kingdom
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Penn Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, USA
| | - Lucia Sacchi
- Department of Electrical, Computer & Biomedical Engineering University of Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer & Biomedical Engineering University of Pavia, Italy
| | | | - Allan Tucker
- Department of Computer Science, Brunel University London, United Kingdom
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11
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Morid MA, Sheng ORL, Del Fiol G, Facelli JC, Bray BE, Abdelrahman S. Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury. JMIR Med Inform 2020; 8:e14272. [PMID: 32181753 PMCID: PMC7109618 DOI: 10.2196/14272] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 11/23/2019] [Accepted: 01/22/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients' data to extract the temporal features using their structural temporal patterns, that is, trends. OBJECTIVE This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI). METHODS Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation. RESULTS Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001). CONCLUSIONS Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.
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Affiliation(s)
- Mohammad Amin Morid
- Department of Information Systems and Analytics, Leavey School of Business, Santa Clara University, Santa Clara, CA, United States
| | - Olivia R Liu Sheng
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Julio C Facelli
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
- Center for Clinical and Translational Science, University of Utah, Salt Lake City, UT, United States
| | - Bruce E Bray
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
- Division of Cardiovascular Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Samir Abdelrahman
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
- Computer Science Department, Faculty of Computers and Information, Cairo University, Cairo, Egypt
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12
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Novitski P, Cohen CM, Karasik A, Shalev V, Hodik G, Moskovitch R. All-Cause Mortality Prediction in T2D Patients. Artif Intell Med 2020. [DOI: 10.1007/978-3-030-59137-3_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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13
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Kocheturov A, Momcilovic P, Bihorac A, Pardalos PM. Extended vertical lists for temporal pattern mining from multivariate time series. EXPERT SYSTEMS 2019; 36:e12448. [PMID: 33162636 PMCID: PMC7646935 DOI: 10.1111/exsy.12448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 05/10/2019] [Indexed: 06/11/2023]
Abstract
In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level-wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.
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Affiliation(s)
- Anton Kocheturov
- Center for Applied Optimization, Industrial and Systems Engineering, University of Florida, Gainesville, Florida
| | - Petar Momcilovic
- Industrial and Systems Engineering, University of Florida, Gainesville, Florida
| | - Azra Bihorac
- Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida, Gainesville, Florida
| | - Panos M. Pardalos
- Center for Applied Optimization, Industrial and Systems Engineering, University of Florida, Gainesville, Florida
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Li Z, Roberts K, Jiang X, Long Q. Distributed learning from multiple EHR databases: Contextual embedding models for medical events. J Biomed Inform 2019; 92:103138. [PMID: 30825539 DOI: 10.1016/j.jbi.2019.103138] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/15/2019] [Accepted: 02/16/2019] [Indexed: 11/26/2022]
Abstract
Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compared with regular clinical data, EHR data are known for their irregularity and complexity. In addition, analyzing EHR data involves privacy issues and sharing such data is often infeasible among multiple research sites due to regulatory and other hurdles. A recently published work uses contextual embedding models and successfully builds one predictive model for more than seventy common diagnoses. Despite of the high predictive power, the model cannot be generalized to other institutions without sharing data. In this work, a novel method is proposed to learn from multiple databases and build predictive models based on Distributed Noise Contrastive Estimation (Distributed NCE). We use differential privacy to safeguard the intermediary information sharing. The numerical study with a real dataset demonstrates that the proposed method not only can build predictive models in a distributed manner with privacy protection, but also preserve model structure well and achieve comparable prediction accuracy. The proposed methods have been implemented as a stand-alone Python library and the implementation is available on Github (https://github.com/ziyili20/DistributedLearningPredictor) with installation instructions and use-cases.
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Affiliation(s)
- Ziyi Li
- Emory University, Department of Biostatistics and Bioinformatics, Atlanta, GA 30332, USA
| | - Kirk Roberts
- University of Texas, Health Science Center at Houston, School of Biomedical Informatics, Houston, TX 77030, USA
| | - Xiaoqian Jiang
- University of Texas, Health Science Center at Houston, School of Biomedical Informatics, Houston, TX 77030, USA.
| | - Qi Long
- University of Pennsylvania, Perelman School of Medicine, Department of Biostatistics, Epidemiology and Informatics, Philadelphia, PA 19104, USA.
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15
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Moskovitch R, Shahar Y, Wang F, Hripcsak G. Temporal biomedical data analytics. J Biomed Inform 2019; 90:103092. [PMID: 30654029 PMCID: PMC9745669 DOI: 10.1016/j.jbi.2018.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 12/24/2018] [Indexed: 02/07/2023]
Affiliation(s)
- Robert Moskovitch
- Department of Information Systems Engineering, Ben Gurion University of the Negev, Beersheba, Israel
| | - Yuval Shahar
- Department of Information Systems Engineering, Ben Gurion University of the Negev, Beersheba, Israel
| | - Fei Wang
- Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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Huang JW, Jaysawal BP, Chen KY, Wu YB. Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns. Knowl Inf Syst 2019. [DOI: 10.1007/s10115-019-01333-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Dagliati A, Geifman N, Peek N, Holmes JH, Sacchi L, Sajjadi SE, Tucker A. Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series. Artif Intell Med 2019. [DOI: 10.1007/978-3-030-21642-9_50] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Mining Compact Predictive Pattern Sets Using Classification Model. Artif Intell Med 2019; 11526:386-396. [DOI: 10.1007/978-3-030-21642-9_49] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Shknevsky A, Shahar Y, Moskovitch R. Consistent discovery of frequent interval-based temporal patterns in chronic patients' data. J Biomed Inform 2017; 75:83-95. [PMID: 28987378 DOI: 10.1016/j.jbi.2017.10.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Revised: 08/23/2017] [Accepted: 10/02/2017] [Indexed: 11/24/2022]
Abstract
Increasingly, frequent temporal patterns discovered in longitudinal patient records are proposed as features for classification and prediction, and as means to cluster patient clinical trajectories. However, to justify that, we must demonstrate that most frequent temporal patterns are indeed consistently discoverable within the records of different patient subsets within similar patient populations. We have developed several measures for the consistency of the discovery of temporal patterns. We focus on time-interval relations patterns (TIRPs) that can be discovered within different subsets of the same patient population. We expect the discovered TIRPs (1) to be frequent in each subset, (2) preserve their "local" metrics - the absolute frequency of each pattern, measured by a Proportion Test, and (3) preserve their "global" characteristics - their overall distribution, measured by a Kolmogorov-Smirnov test. We also wanted to examine the effect on consistency, over a variety of settings, of varying the minimal frequency threshold for TIRP discovery, and of using a TIRP-filtering criterion that we previously introduced, the Semantic Adjacency Criterion (SAC). We applied our methodology to three medical domains (oncology, infectious hepatitis, and diabetes). We found that, within the minimal frequency ranges we had examined, 70-95% of the discovered TIRPs were consistently discoverable; 40-48% of them maintained their local frequency. TIRP global distribution similarity varied widely, from 0% to 65%. Increasing the threshold usually increased the percentage of TIRPs that were repeatedly discovered across different patient subsets within the same domain, and the probability of a similar TIRP distribution. Using the SAC principle, enhanced, for most minimal support levels, the percentage of repeating TIRPs, their local consistency and their global consistency. The effect of using the SAC was further strengthened as the minimal frequency threshold was raised.
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Affiliation(s)
- Alexander Shknevsky
- Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva, Israel.
| | - Yuval Shahar
- Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva, Israel.
| | - Robert Moskovitch
- Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva, Israel.
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Nissim N, Shahar Y, Elovici Y, Hripcsak G, Moskovitch R. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods. Artif Intell Med 2017; 81:12-32. [PMID: 28456512 PMCID: PMC5937023 DOI: 10.1016/j.artmed.2017.03.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 03/03/2017] [Indexed: 01/20/2023]
Abstract
BACKGROUND AND OBJECTIVES Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. METHODS We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. RESULTS The AL methods: produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p=0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275-0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers' different models during the training phase, compared to the variance of the induced models' AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p=0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p=0.29), as was the difference between the Combination_XA and Exploitation methods (p=0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired t-test, the difference between the intra-labeler AUC standard deviation when using the consensus label, versus that value when using the other two labeling strategies, was significant only when using the passive learning method (p=0.014), but not when using any of the three AL methods. CONCLUSIONS The use of AL methods, (a) reduces intra-labeler variability in the performance of the induced models during the training phase, and thus reduces the risk of halting the process at a local minimum that is significantly different in performance from the rest of the learned models; and (b) reduces Inter-labeler performance variance, and thus reduces the dependence on the use of a particular labeler. In addition, the use of a consensus label, agreed upon by a rather uneven group of labelers, might be at least as good as using the gold standard labeler, who might not be available, and certainly better than randomly selecting one of the group's individual labelers. Finally, using the AL methods: when provided by the consensus label reduced the intra-labeler AUC variance during the learning phase, compared to using passive learning.
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Affiliation(s)
- Nir Nissim
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Yuval Shahar
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yuval Elovici
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics, Columbia University, New York, NY, USA
| | - Robert Moskovitch
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Biomedical Informatics, Columbia University, New York, NY, USA
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Moskovitch R, Polubriaginof F, Weiss A, Ryan P, Tatonetti N. Procedure prediction from symbolic Electronic Health Records via time intervals analytics. J Biomed Inform 2017; 75:70-82. [PMID: 28823923 DOI: 10.1016/j.jbi.2017.07.018] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Revised: 06/19/2017] [Accepted: 07/25/2017] [Indexed: 11/18/2022]
Abstract
Prediction of medical events, such as clinical procedures, is essential for preventing disease, understanding disease mechanism, and increasing patient quality of care. Although longitudinal clinical data from Electronic Health Records provides opportunities to develop predictive models, the use of these data faces significant challenges. Primarily, while the data are longitudinal and represent thousands of conceptual events having duration, they are also sparse, complicating the application of traditional analysis approaches. Furthermore, the framework presented here takes advantage of the events duration and gaps. International standards for electronic healthcare data represent data elements, such as procedures, conditions, and drug exposures, using eras, or time intervals. Such eras contain both an event and a duration and enable the application of time intervals mining - a relatively new subfield of data mining. In this study, we present Maitreya, a framework for time intervals analytics in longitudinal clinical data. Maitreya discovers frequent time intervals related patterns (TIRPs), which we use as prognostic markers for modelling clinical events. We introduce three novel TIRP metrics that are normalized versions of the horizontal-support, that represents the number of TIRP instances per patient. We evaluate Maitreya on 28 frequent and clinically important procedures, using the three novel TIRP representation metrics in comparison to no temporal representation and previous TIRPs metrics. We also evaluate the epsilon value that makes Allen's relations more flexible with several settings of 30, 60, 90 and 180days in comparison to the default zero. For twenty-two of these procedures, the use of temporal patterns as predictors was superior to non-temporal features, and the use of the vertically normalized horizontal support metric to represent TIRPs as features was most effective. The use of the epsilon value with thirty days was slightly better than the zero.
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Affiliation(s)
- Robert Moskovitch
- Department of Biomedical Informatics, Columbia University, NY, USA; Department of Systems Biology, Columbia University, NY, USA; Department of Medicine, Columbia University, NY, USA; Observational Health Data Sciences and Informations (OHDSI), NY, USA; Department of Software and Information Systems Engineering, Ben Gurion Univeristy, Beer Sheva, Israel.
| | - Fernanda Polubriaginof
- Department of Biomedical Informatics, Columbia University, NY, USA; Department of Systems Biology, Columbia University, NY, USA; Department of Medicine, Columbia University, NY, USA; Observational Health Data Sciences and Informations (OHDSI), NY, USA
| | - Aviram Weiss
- Department of Software and Information Systems Engineering, Ben Gurion Univeristy, Beer Sheva, Israel
| | - Patrick Ryan
- Department of Biomedical Informatics, Columbia University, NY, USA
| | - Nicholas Tatonetti
- Department of Biomedical Informatics, Columbia University, NY, USA; Department of Systems Biology, Columbia University, NY, USA; Department of Medicine, Columbia University, NY, USA; Observational Health Data Sciences and Informations (OHDSI), NY, USA.
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Moskovitch R, Choi H, Hripcsak G, Tatonetti N. Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:555-563. [PMID: 27429447 PMCID: PMC5486920 DOI: 10.1109/tcbb.2016.2591539] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Accurate prognosis of outcome events, such as clinical procedures or disease diagnosis, is central in medicine. The emergence of longitudinal clinical data, like the Electronic Health Records (EHR), represents an opportunity to develop automated methods for predicting patient outcomes. However, these data are highly dimensional and very sparse, complicating the application of predictive modeling techniques. Further, their temporal nature is not fully exploited by current methods, and temporal abstraction was recently used which results in symbolic time intervals representation. We present Maitreya, a framework for the prediction of outcome events that leverages these symbolic time intervals. Using Maitreya, learn predictive models based on the temporal patterns in the clinical records that are prognostic markers and use these markers to train predictive models for eight clinical procedures. In order to decrease the number of patterns that are used as features, we propose the use of three one class feature selection methods. We evaluate the performance of Maitreya under several parameter settings, including the one-class feature selection, and compare our results to that of atemporal approaches. In general, we found that the use of temporal patterns outperformed the atemporal methods, when representing the number of pattern occurrences.
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23
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Kostakis O, Papapetrou P. On searching and indexing sequences of temporal intervals. Data Min Knowl Discov 2017. [DOI: 10.1007/s10618-016-0489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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24
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Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine Learning and Data Mining Methods in Diabetes Research. Comput Struct Biotechnol J 2017; 15:104-116. [PMID: 28138367 PMCID: PMC5257026 DOI: 10.1016/j.csbj.2016.12.005] [Citation(s) in RCA: 340] [Impact Index Per Article: 48.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 12/20/2016] [Accepted: 12/27/2016] [Indexed: 12/14/2022] Open
Abstract
The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.
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Affiliation(s)
- Ioannis Kavakiotis
- Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Institute of Applied Biosciences, CERTH, Thessaloniki, Greece
| | - Olga Tsave
- Laboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Athanasios Salifoglou
- Laboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Nicos Maglaveras
- Institute of Applied Biosciences, CERTH, Thessaloniki, Greece
- Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Ioannis Vlahavas
- Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Ioanna Chouvarda
- Institute of Applied Biosciences, CERTH, Thessaloniki, Greece
- Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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Ordoñez P, Schwarz N, Figueroa-Jiménez A, Garcia-Lebron LA, Roche-Lima A. Learning stochastic finite-state transducer to predict individual patient outcomes. HEALTH AND TECHNOLOGY 2016; 6:239-245. [PMID: 27942425 PMCID: PMC5124435 DOI: 10.1007/s12553-016-0146-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 10/05/2016] [Indexed: 11/30/2022]
Abstract
The high frequency data in intensive care unit is flashed on a screen for a few seconds and never used again. However, this data can be used by machine learning and data mining techniques to predict patient outcomes. Learning finite-state transducers (FSTs) have been widely used in problems where sequences need to be manipulated and insertions, deletions and substitutions need to be modeled. In this paper, we learned the edit distance costs of a symbolic univariate time series representation through a stochastic finite-state transducer to predict patient outcomes in intensive care units. The Nearest-Neighbor method with these learned costs was used to classify the patient status within an hour after 10 h of data. Several experiments were developed to estimate the parameters that better fit the model regarding the prediction metrics. Our best results are compared with published works, where most of the metrics (i.e., Accuracy, Precision and F-measure) were improved.
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Affiliation(s)
- Patricia Ordoñez
- Rio Piedras Campus, University of Puerto Rico, San Juan, Puerto Rico
| | - Nelson Schwarz
- Rio Piedras Campus, University of Puerto Rico, San Juan, Puerto Rico
| | | | | | - Abiel Roche-Lima
- Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico
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Wang Y, Li PF, Tian Y, Ren JJ, Li JS. A Shared Decision-Making System for Diabetes Medication Choice Utilizing Electronic Health Record Data. IEEE J Biomed Health Inform 2016; 21:1280-1287. [PMID: 28113528 DOI: 10.1109/jbhi.2016.2614991] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The use of a shared decision-making (SDM) process in antihyperglycemic medication strategy decisions is necessary due to the complexity of the conditions of diabetes patients. Knowledge of guidelines is used as decision aids in clinical situations, and during this process, no patient health conditions are considered. In this paper, we propose an SDM system framework for type-2 diabetes mellitus (T2DM) patients that not only contains knowledge abstracted from guidelines but also employs a multilabel classification model that uses class-imbalanced electronic health record (EHR) data and that aims to provide a recommended list of available antihyperglycemic medications to help physicians and patients have an SDM conversation. The use of EHR data to serve as a decision-support component in decision aids helps physicians and patients to reach a more intuitive understanding of current health conditions and allows the tailoring of the available knowledge to each patient, leading to a more effective SDM. Real-world data from 2542 T2DM inpatient EHRs were substituted by 77 features and eight output labels, i.e., eight antihyperglycemic medications, and these data were utilized to build and validate the recommendation model. The multilabel recommendation model exhibited stable performance in every single-label classification and showed the ability to predict minority positive cases in which the average recall value of the eight classes was 0.9898. As a whole multilabel classifier, the recommendation model demonstrated outstanding performance, with scores of 0.0941 for Hamming Loss, 0.7611 for Accuracyexam, 0.9664 for Recallexam, and 0.8269 for Fexam.
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27
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Ghosh S, Feng M, Nguyen H, Li J. Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure. IEEE J Biomed Health Inform 2016; 20:1416-1426. [PMID: 26168449 PMCID: PMC5219944 DOI: 10.1109/jbhi.2015.2453478] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.
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Pimus I, Peleg M, Schertz M. Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm. Methods Inf Med 2016; 55:223-33. [PMID: 26848079 DOI: 10.3414/me15-01-0142] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 12/21/2015] [Indexed: 11/09/2022]
Abstract
OBJECTIVES Understanding the progression of comorbid neurodevelopmental disorders (NDD) during different critical time periods may contribute to our comprehension of the underlying pathophysiology of NDDs. The objective of our study was to identify frequent temporal sequences of developmental diagnoses in noisy patient data. METHODS We used a data set of 2810 patients, documenting NDD diagnoses given to them by an NDD expert at a child developmental center during multiple visits at different ages. Extensive preprocessing steps were developed in order to allow the data set to be processed by an efficient sequence mining algorithm (SPADE). RESULTS The discovered sequences were validated by cross validation for 10 iterations; all correlation coefficients for support, confidence and lift measures were above 0.75 and their proportions were similar. No signifi- cant differences between the distributions of sequences were found using Kolmogorov-Smirnov test. CONCLUSIONS We have demonstrated the feasibility of using the SPADE algorithm for discovery of valid temporal sequences of comorbid disorders in children with NDDs. The identification of such sequences would be beneficial from clinical and research perspectives. Moreover, these sequences could serve as features for developing a full-fledged temporal predictive model.
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Affiliation(s)
| | - Mor Peleg
- Mor Peleg, Ph.D., Assoc. Prof., Department of Information Systems, Rabin Building, room 7047, Faculty of Social Sciences, University of Haifa, Haifa, Israel, 3498838, E-mail:
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Nissim N, Boland MR, Tatonetti NP, Elovici Y, Hripcsak G, Shahar Y, Moskovitch R. Improving condition severity classification with an efficient active learning based framework. J Biomed Inform 2016; 61:44-54. [PMID: 27016383 DOI: 10.1016/j.jbi.2016.03.016] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 01/31/2016] [Accepted: 03/21/2016] [Indexed: 02/07/2023]
Abstract
Classification of condition severity can be useful for discriminating among sets of conditions or phenotypes, for example when prioritizing patient care or for other healthcare purposes. Electronic Health Records (EHRs) represent a rich source of labeled information that can be harnessed for severity classification. The labeling of EHRs is expensive and in many cases requires employing professionals with high level of expertise. In this study, we demonstrate the use of Active Learning (AL) techniques to decrease expert labeling efforts. We employ three AL methods and demonstrate their ability to reduce labeling efforts while effectively discriminating condition severity. We incorporate three AL methods into a new framework based on the original CAESAR (Classification Approach for Extracting Severity Automatically from Electronic Health Records) framework to create the Active Learning Enhancement framework (CAESAR-ALE). We applied CAESAR-ALE to a dataset containing 516 conditions of varying severity levels that were manually labeled by seven experts. Our dataset, called the "CAESAR dataset," was created from the medical records of 1.9 million patients treated at Columbia University Medical Center (CUMC). All three AL methods decreased labelers' efforts compared to the learning methods applied by the original CAESER framework in which the classifier was trained on the entire set of conditions; depending on the AL strategy used in the current study, the reduction ranged from 48% to 64% that can result in significant savings, both in time and money. As for the PPV (precision) measure, CAESAR-ALE achieved more than 13% absolute improvement in the predictive capabilities of the framework when classifying conditions as severe. These results demonstrate the potential of AL methods to decrease the labeling efforts of medical experts, while increasing accuracy given the same (or even a smaller) number of acquired conditions. We also demonstrated that the methods included in the CAESAR-ALE framework (Exploitation and Combination_XA) are more robust to the use of human labelers with different levels of professional expertise.
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Affiliation(s)
- Nir Nissim
- Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| | - Mary Regina Boland
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics, Columbia University, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Department of Medicine, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics, Columbia University, New York, NY, USA
| | - Yuval Elovici
- Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics, Columbia University, New York, NY, USA
| | - Yuval Shahar
- Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Robert Moskovitch
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Department of Medicine, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics, Columbia University, New York, NY, USA.
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Batal I, Cooper G, Fradkin D, Harrison J, Moerchen F, Hauskrecht M. An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data. Knowl Inf Syst 2016; 46:115-150. [PMID: 26752800 PMCID: PMC4704806 DOI: 10.1007/s10115-015-0819-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Revised: 08/31/2014] [Accepted: 12/06/2014] [Indexed: 11/27/2022]
Abstract
This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.
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Affiliation(s)
| | - Gregory Cooper
- Department of Biomedical Informatics, University of Pittsburgh,
| | | | - James Harrison
- Department of Public Health Sciences, University of Virginia,
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31
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Zhao J, Henriksson A, Asker L, Boström H. Predictive modeling of structured electronic health records for adverse drug event detection. BMC Med Inform Decis Mak 2015; 15 Suppl 4:S1. [PMID: 26606038 PMCID: PMC4660129 DOI: 10.1186/1472-6947-15-s4-s1] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Methods Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Results Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined. Conclusions We have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two.
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Sacchi L, Dagliati A, Bellazzi R. Analyzing complex patients' temporal histories: new frontiers in temporal data mining. Methods Mol Biol 2015; 1246:89-105. [PMID: 25417081 DOI: 10.1007/978-1-4939-1985-7_6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In recent years, data coming from hospital information systems (HIS) and local healthcare organizations have started to be intensively used for research purposes. This rising amount of available data allows reconstructing the compete histories of the patients, which have a strong temporal component. This chapter introduces the major challenges faced by temporal data mining researchers in an era when huge quantities of complex clinical temporal data are becoming available. The analysis is focused on the peculiar features of this kind of data and describes the methodological and technological aspects that allow managing such complex framework. The chapter shows how heterogeneous data can be processed to derive a homogeneous representation. Starting from this representation, it illustrates different techniques for jointly analyze such kind of data. Finally, the technological strategies that allow creating a common data warehouse to gather data coming from different sources and with different formats are presented.
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Affiliation(s)
- Lucia Sacchi
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Via Ferrata 1, Pavia, 27100, Italy,
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Syed H, Das AK. Identifying Chemotherapy Regimens in Electronic Health Record Data Using Interval-Encoded Sequence Alignment. Artif Intell Med 2015. [DOI: 10.1007/978-3-319-19551-3_17] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Classification of multivariate time series via temporal abstraction and time intervals mining. Knowl Inf Syst 2014. [DOI: 10.1007/s10115-014-0784-5] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Nguyen Q, Valizadegan H, Hauskrecht M. Learning classification models with soft-label information. J Am Med Inform Assoc 2014; 21:501-8. [PMID: 24259520 PMCID: PMC3994863 DOI: 10.1136/amiajnl-2013-001964] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Revised: 10/24/2013] [Accepted: 11/01/2013] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. MATERIALS AND METHODS Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. RESULTS Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. CONCLUSIONS A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.
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Affiliation(s)
- Quang Nguyen
- Computer Science Department, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Ho JC, Lee CH, Ghosh J. Septic Shock Prediction for Patients with Missing Data. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2014. [DOI: 10.1145/2591676] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Sepsis and septic shock are common and potentially fatal conditions that often occur in intensive care unit (ICU) patients. Early prediction of patients at risk for septic shock is therefore crucial to minimizing the effects of these complications. Potential indications for septic shock risk span a wide range of measurements, including physiological data gathered at different temporal resolutions and gene expression levels, leading to a nontrivial prediction problem. Previous works on septic shock prediction have used small, carefully curated datasets or clinical measurements that may not be available for many ICU patients. The recent availability of a large, rich ICU dataset called MIMIC-II has provided the opportunity for more extensive modeling of this problem. However, such a large clinical dataset inevitably contains a substantial amount of missing data. We investigate how different imputation selection criteria and methods can overcome the missing data problem. Our results show that imputation methods in conjunction with predictive modeling can lead to accurate septic shock prediction, even if the features are restricted primarily to noninvasive measurements. Our models provide a generalized approach for predicting septic shock in any ICU patient.
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Valizadegan H, Nguyen Q, Hauskrecht M. Learning classification models from multiple experts. J Biomed Inform 2013; 46:1125-35. [PMID: 24035760 PMCID: PMC3922063 DOI: 10.1016/j.jbi.2013.08.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2013] [Revised: 07/15/2013] [Accepted: 08/17/2013] [Indexed: 10/26/2022]
Abstract
Building classification models from clinical data using machine learning methods often relies on labeling of patient examples by human experts. Standard machine learning framework assumes the labels are assigned by a homogeneous process. However, in reality the labels may come from multiple experts and it may be difficult to obtain a set of class labels everybody agrees on; it is not uncommon that different experts have different subjective opinions on how a specific patient example should be classified. In this work we propose and study a new multi-expert learning framework that assumes the class labels are provided by multiple experts and that these experts may differ in their class label assessments. The framework explicitly models different sources of disagreements and lets us naturally combine labels from different human experts to obtain: (1) a consensus classification model representing the model the group of experts converge to, as well as, and (2) individual expert models. We test the proposed framework by building a model for the problem of detection of the Heparin Induced Thrombocytopenia (HIT) where examples are labeled by three experts. We show that our framework is superior to multiple baselines (including standard machine learning framework in which expert differences are ignored) and that our framework leads to both improved consensus and individual expert models.
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Affiliation(s)
- Hamed Valizadegan
- Department of Computer Science, University of Pittsburgh, United States.
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Park Y, Ho JC, Ghosh J. Multivariate temporal symptomatic characterization of cardiac arrest. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:3222-3225. [PMID: 24110414 DOI: 10.1109/embc.2013.6610227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We model the temporal symptomatic characteristics of 171 cardiac arrest patients in Intensive Care Units. The temporal and feature dependencies in the data are illustrated using a mixture of matrix normal distributions. We found that the cardiac arrest temporal signature is best summarized with six hours data prior to cardiac arrest events, and its statistical descriptions are significantly different from the measurements taken in the past two days. This matrix normal model can classify these patterns better than logistic regressions with lagged features.
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