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Wang W, Feng Y, Zhao H, Wang X, Cai R, Cai W, Zhang X. Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs. Health Inf Sci Syst 2024; 12:15. [PMID: 38440103 PMCID: PMC10908733 DOI: 10.1007/s13755-024-00278-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/23/2024] [Indexed: 03/06/2024] Open
Abstract
Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.
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Affiliation(s)
- Weiguang Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819 Liaoning China
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110167 Liaoning China
| | - Yingying Feng
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819 Liaoning China
| | - Haiyan Zhao
- School of Computer Science, Peking University, Beijing, 100871 China
- Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, 100871 China
| | - Xin Wang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300354 China
| | - Ruikai Cai
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, 110004 Liaoning China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110167 Liaoning China
| | - Xia Zhang
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819 Liaoning China
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110167 Liaoning China
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Bottrighi A, Pennisi M, Roveta A, Massarino C, Cassinari A, Betti M, Bolgeo T, Bertolotti M, Rava E, Maconi A. A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2. BMC Med Inform Decis Mak 2022; 22:340. [PMID: 36578017 PMCID: PMC9795955 DOI: 10.1186/s12911-022-02076-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 12/06/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data. METHODS This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models. RESULTS We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation. CONCLUSIONS The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.
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Affiliation(s)
- Alessio Bottrighi
- grid.16563.370000000121663741DISIT, Computer Science Institute, Università del Piemonte Orientale, Viale T. Michel, 11, 15121 Alessandria, Italy ,grid.16563.370000000121663741AI@UPO, Università del Piemonte Orientale, Vercelli, Italy
| | - Marzio Pennisi
- grid.16563.370000000121663741DISIT, Computer Science Institute, Università del Piemonte Orientale, Viale T. Michel, 11, 15121 Alessandria, Italy ,grid.16563.370000000121663741AI@UPO, Università del Piemonte Orientale, Vercelli, Italy
| | - Annalisa Roveta
- Research Laboratory Facility, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
| | - Costanza Massarino
- Research Training Innovation Infrastructure, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
| | - Antonella Cassinari
- Research Training Innovation Infrastructure, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
| | - Marta Betti
- Research Training Innovation Infrastructure, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
| | - Tatiana Bolgeo
- Research Training Innovation Infrastructure, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
| | - Marinella Bertolotti
- Research Training Innovation Infrastructure, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
| | - Emanuele Rava
- grid.16563.370000000121663741DISIT, Università del Piemonte Orientale, Viale T. Michel, 11, 15121 Alessandria, Italy
| | - Antonio Maconi
- Research Laboratory Facility, Research and Innovation Department, Azienda Ospedaliera “SS Antonio e Biagio e Cesare Arrigo”, Alessandria, Italy
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