Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Yeh, Marvin Chia-Han; Wang, Yu-Hsiang; Bai, Kuan‐Jen; Wang, Hsiao-Han; Li, Yu-Chuan

doi:10.2196/26256

Cited by 30 publications

(18 citation statements)

References 41 publications

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“…In addition, no published lung cancer risk prediction by a synthetic data ML model is available for comparison. With regard to real EMR data studies, the AUC of 0.91–0.96 for lung cancer risk prediction in this synthetic LHS study is higher than the AUC of 0.88 for predicting 1-year risk obtained in the XGBoost study by Wang et al 21 and AUC of 0.90 for predicting 1-year risk obtained in the deep learning study by Yeh et al 30 .…”

Section: Resultscontrasting

confidence: 73%

“…Because our hospital collaborators have been building LHS to personalize lung cancer and stroke screening, we chose lung cancer as an example for the development and the simulation of ML-enabled LHS and stroke for verifying the new ML-enabled LHS process. Many ML models for lung cancer 21 , 29 , 30 and stroke 31 , 32 exist and are available for comparison. A study by Kaiser Permanente used routine clinical and lab data to predict lung cancer risk 33 .…”

Section: Introductionmentioning

confidence: 99%

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Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data

Chen¹,

Chen²

2022

Sci Rep

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When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.

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Section: Resultscontrasting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data

Chen¹,

Chen²

2022

Sci Rep

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“…To amend these problems, we proposed a "Temporal Phenomic Map (TPM)" model that will inclusively take into consideration all the possible variables and their change over time [4,5,6]. Using data elements from the EHRs as examples, we can model all the diagnoses and medications that happened in a specific period of time before the initial diagnostic date of the target health threat into a two-dimensional map for each patient.…”

Section: The Temporal Phenomic Map Modelmentioning

confidence: 99%

“…Now the trained CNN can be readily used to spot the target health threat out of any patient. We have used the TPM to accurately predict the risk of several major cancers including non-melanoma skin cancer, lung cancer and liver cancer using 36 months of EHRs data [4,5,6].…”

Section: The Temporal Phenomic Map Modelmentioning

confidence: 99%

Temporal Phenomics – A Powerful Approach Using AI to Achieve “Earlier Medicine”

2022

Studies in Health Technology and Informatics

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The resurgence of machine learning AI has triggered the importance of collecting “personal big data” over a long period of time from wearable devices and EHRs. Collecting data from this large number of variables over a significant period of time has further induced the study on “Temporal Phenomics”, which can be a powerful approach to achieve pre-emptive and “earlier medicine”. The paper presents a methodology to make studying “Temporal Phenomics” more feasible and convenient without limitations on the number of variables and the length of time periods.

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“…Compared to the progress of deep-phenotyping English EHRs, the method for deep-phenotyping Chinese EHRs is still in its infancy. Regarding the existence of linguistic differences, the established strategies [ 14 , 17 , 19 , 20 ] for deep-phenotyping English EHRs cannot be directly used for Chinese EHRs. Moreover, developing a deep-phenotyping algorithm requires fine-grained annotation data.…”

Section: Introductionmentioning

confidence: 99%

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation

Li¹,

Zhang²,

Chen³

et al. 2022

J Med Internet Res

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Background Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. Objective In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. Methods The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction. Results In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method. Conclusions We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.

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Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Cited by 30 publications

References 41 publications

Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data

Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data

Temporal Phenomics – A Powerful Approach Using AI to Achieve “Earlier Medicine”

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation

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