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4. Personalized Event Prediction for Electronic Health Records

Author

Lee, Jeong Min and Hauskrecht, Milos

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Applications
Abstract

Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. Developing accurate predictive models of such sequences is of a great importance for supporting a variety of models for interpreting/classifying the current patient condition, or predicting adverse clinical events and outcomes, all aimed to improve patient care. One important challenge of learning predictive models of clinical sequences is their patient-specific variability. Based on underlying clinical conditions, each patient's sequence may consist of different sets of clinical events (observations, lab results, medications, procedures). Hence, simple population-wide models learned from event sequences for many different patients may not accurately predict patient-specific dynamics of event sequences and their differences. To address the problem, we propose and investigate multiple new event sequence prediction models and methods that let us better adjust the prediction for individual patients and their specific conditions. The methods developed in this work pursue refinement of population-wide models to subpopulations, self-adaptation, and a meta-level model switching that is able to adaptively select the model with the best chance to support the immediate prediction. We analyze and test the performance of these models on clinical event sequences of patients in MIMIC-III database., Comment: arXiv admin note: text overlap with arXiv:2104.01787

Published
2023
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14. Correction to: Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management

Author

Taouli, Bachir, Ba-Ssalamah, Ahmed, Chapiro, Julius, Chhatwal, Jagpreet, Fowler, Kathryn, Kang, Tae Wook, Knobloch, Gesine, Koh, Dow-Mu, Kudo, Masatoshi, Lee, Jeong Min, Murakami, Takamichi, Pinato, David J., Ringe, Kristina I., Song, Bin, Tabrizian, Parissa, Wang, Jin, Yoon, Jeong Hee, Zeng, Mengsu, Zhou, Jian, and Vilgrain, Valérie

Published
2024
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15. Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management

Author

Taouli, Bachir, Ba-Ssalamah, Ahmed, Chapiro, Julius, Chhatwal, Jagpreet, Fowler, Kathryn, Kang, Tae Wook, Knobloch, Gesine, Koh, Dow-Mu, Kudo, Masatoshi, Lee, Jeong Min, Murakami, Takamichi, Pinato, David J., Ringe, Kristina I., Song, Bin, Tabrizian, Parissa, Wang, Jin, Yoon, Jeong Hee, Zeng, Mengsu, Zhou, Jian, and Vilgrain, Valérie

Published
2023
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16. Consensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion

Author

Taouli, Bachir, Ba-Ssalamah, Ahmed, Chapiro, Julius, Chhatwal, Jagpreet, Fowler, Kathryn, Kang, Tae Wook, Knobloch, Gesine, Koh, Dow-Mu, Kudo, Masatoshi, Lee, Jeong Min, Murakami, Takamichi, Pinato, David J., Ringe, Kristina I., Song, Bin, Tabrizian, Parissa, Wang, Jin, Yoon, Jeong Hee, Zeng, Mengsu, Zhou, Jian, and Vilgrain, Valérie

Published
2023
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18. Learning to Adapt Clinical Sequences with Residual Mixture of Experts

Author

Lee, Jeong Min and Hauskrecht, Milos

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Clinical event sequences in Electronic Health Records (EHRs) record detailed information about the patient condition and patient care as they occur in time. Recent years have witnessed increased interest of machine learning community in developing machine learning models solving different types of problems defined upon information in EHRs. More recently, neural sequential models, such as RNN and LSTM, became popular and widely applied models for representing patient sequence data and for predicting future events or outcomes based on such data. However, a single neural sequential model may not properly represent complex dynamics of all patients and the differences in their behaviors. In this work, we aim to alleviate this limitation by refining a one-fits-all model using a Mixture-of-Experts (MoE) architecture. The architecture consists of multiple (expert) RNN models covering patient sub-populations and refining the predictions of the base model. That is, instead of training expert RNN models from scratch we define them on the residual signal that attempts to model the differences from the population-wide model. The heterogeneity of various patient sequences is modeled through multiple experts that consist of RNN. Particularly, instead of directly training MoE from scratch, we augment MoE based on the prediction signal from pretrained base GRU model. With this way, the mixture of experts can provide flexible adaptation to the (limited) predictive power of the single base RNN model. We experiment with the newly proposed model on real-world EHRs data and the multivariate clinical event prediction task. We implement RNN using Gated Recurrent Units (GRU). We show 4.1% gain on AUPRC statistics compared to a single RNN prediction., Comment: Accepted at 20th International Conference on Artificial Intelligence in Medicine (AIME) 2022

Published
2022

36. Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning

Author

Chung, Hyungjin, Kim, Jaehyun, Yoon, Jeong Hee, Lee, Jeong Min, and Ye, Jong Chul

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Diffusion-weighted MRI is nowadays performed routinely due to its prognostic ability, yet the quality of the scans are often unsatisfactory which can subsequently hamper the clinical utility. To overcome the limitations, here we propose a fully unsupervised quality enhancement scheme, which boosts the resolution and removes the motion artifact simultaneously. This process is done by first training the network using optimal transport driven cycleGAN with stochastic degradation block which learns to remove aliasing artifacts and enhance the resolution, then using the trained network in the test stage by utilizing bootstrap subsampling and aggregation for motion artifact suppression. We further show that we can control the trade-off between the amount of artifact correction and resolution by controlling the bootstrap subsampling ratio at the inference stage. To the best of our knowledge, the proposed method is the first to tackle super-resolution and motion artifact correction simultaneously in the context of MRI using unsupervised learning. We demonstrate the efficiency of our method by applying it to both quantitative evaluation using simulation study, and to in vivo diffusion-weighted MR scans, which shows that our method is superior to the current state-of-the-art methods. The proposed method is flexible in that it can be applied to various quality enhancement schemes in other types of MR scans, and also directly to the quality enhancement of apparent diffusion coefficient maps.

Published
2021

42. Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning

Author

Lee, Jeong Min and Hauskrecht, Milos

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update., Comment: Accepted at 19th International Conference on Artificial Intelligence in Medicine (AIME 2021)

Published
2021

43. English Pronunciation Correction Service for Hearing-Impaired People: BETTer, Focusing on the Personalized Speech Model

Author

Park, Seon Hong, Park, Hyun Jin, Kim, Yoon Jeong, Park, Young Woong, Lee, Jeong Min, Jung, Ye Rin, Jo, Seok Ryu, Oh, Se Jong, Doo, Ill Chul, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Park, Ji Su, editor, Yang, Laurence T., editor, Pan, Yi, editor, and Park, Jong Hyuk, editor

Published
2023
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44. Consensus report from the 9th International Forum for Liver Magnetic Resonance Imaging: applications of gadoxetic acid-enhanced imaging

Author

Koh, Dow-Mu, Ba-Ssalamah, Ahmed, Brancatelli, Giuseppe, Fananapazir, Ghaneh, Fiel, M Isabel, Goshima, Satoshi, Ju, Sheng-Hong, Kartalis, Nikolaos, Kudo, Masatoshi, Lee, Jeong Min, Murakami, Takamichi, Seidensticker, Max, Sirlin, Claude B, Tan, Cher Heng, Wang, Jin, Yoon, Jeong Hee, Zeng, Mengsu, Zhou, Jian, and Taouli, Bachir

Subjects
Rare Diseases, Liver Disease, Digestive Diseases, Biomedical Imaging, Chronic Liver Disease and Cirrhosis, Cancer, Liver Cancer, Orphan Drug, Oral and gastrointestinal, Carcinoma, Hepatocellular, Consensus, Contrast Media, Gadolinium DTPA, Humans, Liver Neoplasms, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Retrospective Studies, Sensitivity and Specificity, Gadoxetic acid, Hepatocellular carcinoma, Magnetic resonance imaging, Metastatic liver disease, Clinical Sciences, Nuclear Medicine & Medical Imaging
Abstract

ObjectivesThe 9th International Forum for Liver Magnetic Resonance Imaging (MRI) was held in Singapore in September 2019, bringing together radiologists and allied specialists to discuss the latest developments in and formulate consensus statements for liver MRI, including the applications of gadoxetic acid-enhanced imaging.MethodsAs at previous Liver Forums, the meeting was held over 2 days. Presentations by the faculty on days 1 and 2 and breakout group discussions on day 1 were followed by delegate voting on consensus statements presented on day 2. Presentations and discussions centered on two main meeting themes relating to the use of gadoxetic acid-enhanced MRI in primary liver cancer and metastatic liver disease.Results and conclusionsGadoxetic acid-enhanced MRI offers the ability to monitor response to systemic therapy and to assist in pre-surgical/pre-interventional planning in liver metastases. In hepatocellular carcinoma, gadoxetic acid-enhanced MRI provides precise staging information for accurate treatment decision-making and follow-up post therapy. Gadoxetic acid-enhanced MRI also has potential, currently investigational, indications for the functional assessment of the liver and the biliary system. Additional voting sessions at the Liver Forum debated the role of multidisciplinary care in the management of patients with liver disease, evidence to support the use of abbreviated imaging protocols, and the importance of standardizing nomenclature in international guidelines in order to increase the sharing of scientific data and improve the communication between centers.Key points• Gadoxetic acid-enhanced MRI is the preferred imaging method for pre-surgical or pre-interventional planning for liver metastases after systemic therapy. • Gadoxetic acid-enhanced MRI provides accurate staging of HCC before and after treatment with locoregional/biologic therapies. • Abbreviated protocols for gadoxetic acid-enhanced MRI offer potential time and cost savings, but more evidence is necessary. The use of gadoxetic acid-enhanced MRI for the assessment of liver and biliary function is under active investigation.

Published
2021

45. Correction: Consensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion

Author

Taouli, Bachir, Ba-Ssalamah, Ahmed, Chapiro, Julius, Chhatwal, Jagpreet, Fowler, Kathryn, Kang, Tae Wook, Knobloch, Gesine, Koh, Dow-Mu, Kudo, Masatoshi, Lee, Jeong Min, Murakami, Takamichi, Pinato, David J., Ringe, Kristina I., Song, Bin, Tabrizian, Parissa, Wang, Jin, Yoon, Jeong Hee, Zeng, Mengsu, Zhou, Jian, and Vilgrain, Valérie

Published
2024
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