TY  - JOUR
AU  - Li, Haomin
AU  - Zhou, Mengying
AU  - Sun, Yuhan
AU  - Yang, Jian
AU  - Zeng, Xian
AU  - Qiu, Yunxiang
AU  - Xia, Yuanyuan
AU  - Zheng, Zhijie
AU  - Yu, Jin
AU  - Feng, Yuqing
AU  - Shi, Zhuo
AU  - Huang, Ting
AU  - Tan, Linhua
AU  - Lin, Ru
AU  - Li, Jianhua
AU  - Fan, Xiangming
AU  - Ye, Jingjing
AU  - Duan, Huilong
AU  - Shi, Shanshan
AU  - Shu, Qiang
PY  - 2024
DA  - 2024/1/19
TI  - A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study
JO  - JMIR Med Inform
SP  - e49138
VL  - 12
KW  - medicine-based evidence
KW  - general prediction model
KW  - patient similarity
KW  - congenital heart disease
KW  - echocardiography
KW  - postoperative complication
KW  - similarity network
KW  - heart
KW  - cardiology
KW  - NLP
KW  - natural language processing
KW  - predict
KW  - predictive
KW  - prediction
KW  - complications
KW  - complication
KW  - surgery
KW  - surgical
KW  - postoperative
AB  - Background: Although evidence-based medicine proposes personalized care that considers the best evidence, it still fails to address personal treatment in many real clinical scenarios where the complexity of the situation makes none of the available evidence applicable. “Medicine-based evidence” (MBE), in which big data and machine learning techniques are embraced to derive treatment responses from appropriately matched patients in real-world clinical practice, was proposed. However, many challenges remain in translating this conceptual framework into practice. Objective: This study aimed to technically translate the MBE conceptual framework into practice and evaluate its performance in providing general decision support services for outcomes after congenital heart disease (CHD) surgery. Methods: Data from 4774 CHD surgeries were collected. A total of 66 indicators and all diagnoses were extracted from each echocardiographic report using natural language processing technology. Combined with some basic clinical and surgical information, the distances between each patient were measured by a series of calculation formulas. Inspired by structure-mapping theory, the fusion of distances between different dimensions can be modulated by clinical experts. In addition to supporting direct analogical reasoning, a machine learning model can be constructed based on similar patients to provide personalized prediction. A user-operable patient similarity network (PSN) of CHD called CHDmap was proposed and developed to provide general decision support services based on the MBE approach. Results: Using 256 CHD cases, CHDmap was evaluated on 2 different types of postoperative prognostic prediction tasks: a binary classification task to predict postoperative complications and a multiple classification task to predict mechanical ventilation duration. A simple poll of the k-most similar patients provided by the PSN can achieve better prediction results than the average performance of 3 clinicians. Constructing logistic regression models for prediction using similar patients obtained from the PSN can further improve the performance of the 2 tasks (best area under the receiver operating characteristic curve=0.810 and 0.926, respectively). With the support of CHDmap, clinicians substantially improved their predictive capabilities. Conclusions: Without individual optimization, CHDmap demonstrates competitive performance compared to clinical experts. In addition, CHDmap has the advantage of enabling clinicians to use their superior cognitive abilities in conjunction with it to make decisions that are sometimes even superior to those made using artificial intelligence models. The MBE approach can be embraced in clinical practice, and its full potential can be realized. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2024/1/e49138
UR  - https://doi.org/10.2196/49138
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38297829
DO  - 10.2196/49138
ID  - info:doi/10.2196/49138
ER  -