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Publications (6 of 6) Show all publications
Tu, R., Zhange, C., Ackermann, P., Mohan, K., Kjellström, H. & Zhang, K. (2019). Causal Discovery in the Presence of Missing Data. In: : . Paper presented at The 22nd International Conference on Artificial Intelligence and Statistics.
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2019 (English)Conference paper, Published paper (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-258069 (URN)
Conference
The 22nd International Conference on Artificial Intelligence and Statistics
Note

QC 20190913

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-13Bibliographically approved
Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellström, H. & Zhang, K. (2019). Causal discovery in the presence of missing data. In: : . Paper presented at International Conference on Artificial Intelligence and Statistics.
Open this publication in new window or tab >>Causal discovery in the presence of missing data
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2019 (English)Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-269111 (URN)
Conference
International Conference on Artificial Intelligence and Statistics
Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-04
Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellström, H. & Zhang, K. (2019). Causal Discovery in the Presence of Missing Data. In: Chaudhuri, K Sugiyama, M (Ed.), 22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89: . Paper presented at 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), APR 16-18, 2019, Naha, JAPAN. MICROTOME PUBLISHING
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2019 (English)In: 22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89 / [ed] Chaudhuri, K Sugiyama, M, MICROTOME PUBLISHING , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Missing data are ubiquitous in many domains such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process. Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs), we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Missing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections. Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR.

Place, publisher, year, edition, pages
MICROTOME PUBLISHING, 2019
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
National Category
Mathematics
Identifiers
urn:nbn:se:kth:diva-269511 (URN)000509687901084 ()
Conference
22nd International Conference on Artificial Intelligence and Statistics (AISTATS), APR 16-18, 2019, Naha, JAPAN
Note

QC 20200309

Available from: 2020-03-09 Created: 2020-03-09 Last updated: 2020-03-09Bibliographically approved
Tu, R., Zhang, K., Bertilson, B. C., Kjellström, H. & Zhang, C. (2019). Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. In: : . Paper presented at Neural Information Processing Systems.
Open this publication in new window or tab >>Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
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2019 (English)Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-269108 (URN)
Conference
Neural Information Processing Systems
Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-04
Hamesse, C., Tu, R., Ackermann, P., Kjellström, H. & Zhang, C. (2019). Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation. In: Proceedings of Machine Learning Research 106: . Paper presented at Machine Learning for Healthcare 2019, University of Michigan, Ann Arbor, MI August 8-10, 2019.
Open this publication in new window or tab >>Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation
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2019 (English)In: Proceedings of Machine Learning Research 106, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the AT Rrehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages.

National Category
Engineering and Technology Computer Sciences
Identifiers
urn:nbn:se:kth:diva-258070 (URN)
Conference
Machine Learning for Healthcare 2019, University of Michigan, Ann Arbor, MI August 8-10, 2019
Note

QC 20190912

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-17Bibliographically approved
Zhan, M., Tu, R. & Yu, Q. (2018). Understanding readers: Conducting sentiment analysis of instagram captions. In: PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018): . Paper presented at 2nd International Conference on Computer Science and Artificial Intelligence, CSAI 2018; Shenzhen; China; 8 December 2018 through 10 December 2018 (pp. 33-40). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Understanding readers: Conducting sentiment analysis of instagram captions
2018 (English)In: PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018), Association for Computing Machinery (ACM), 2018, p. 33-40Conference paper, Published paper (Refereed)
Abstract [en]

The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018
Keywords
Captions, Instagram, Readers, Sentiment Analysis, Data mining, Learning algorithms, Libraries, Machine learning, Multimedia systems, Supervised learning, Media transition, Methods of analysis, Public library, Supervised machine learning, User-generated content
National Category
Information Studies
Identifiers
urn:nbn:se:kth:diva-252245 (URN)10.1145/3297156.3297270 (DOI)000469786300006 ()2-s2.0-85062778865 (Scopus ID)9781450366069 (ISBN)
Conference
2nd International Conference on Computer Science and Artificial Intelligence, CSAI 2018; Shenzhen; China; 8 December 2018 through 10 December 2018
Note

QC20190612

Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-06-26Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1356-9653

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