Shang-ming Zhou

Xianghua Xie

Cronfa at Swansea University

 Cronfa -  Swansea University Open Access Repository   _____________________________________________________________   This is an author produced version of a paper published in:The Lancet                                Cronfa URL for this paper:http://cronfa.swan.ac.uk/Record/cronfa45957_____________________________________________________________ Paper:Zhou, S., Tsang, G., Xie, X., Huo, L., Brophy, S. & Lyons, R. (2018).  Mining electronic health records to identifyinfluential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach.The Lancet, 392, S9http://dx.doi.org/10.1016/S0140-6736(18)32166-4      Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND).  _____________________________________________________________  This item is brought to you by Swansea University. Any person downloading material is agreeing to abide by the termsof the repository licence. Copies of full text items may be used or reproduced in any format or medium, without priorpermission for personal research or study, educational or non-commercial purposes only. The copyright for any workremains with the original author unless otherwise specified. The full-text must not be sold in any format or mediumwithout the formal permission of the copyright holder. Permission for multiple reproductions should be obtained from the original author. Authors are personally responsible for adhering to copyright and publisher restrictions when uploading content to therepository. http://www.swansea.ac.uk/library/researchsupport/ris-support/  Mining Electronic Health Records to Identify Influential Predictors Associated with Hospitalisation of Dementia Patients: An Artificial Intelligence Approach Shang-Ming Zhou1, Gavin Tsang2, Xianghua Xie2, Lin Huo3, Sinead Brophy1, and Ronan A Lyons1 1The Health Data Research UK Wales and Northern Ireland Site, Swansea University Medical School, Swansea, SA2 8PP, UK. 2Department of Computer Science, Swansea University, Swansea, SA2 8PP, UK. 3China-ASEAN Research Institute, Guangxi University, Nanning, China.  Background: Early prediction of the outcomes of dementia is significant and challenging. This study aimed to identify influential predictors from primary care electronic health records that can accurately and reliably predict the outcomes of dementia: being admitted to hospital or remaining with the society.  Methods: The dementia patients were collected from general practice (GP) and hospital data in Wales between 1908 and 2015. These records were linked at the individual patient level via the Secure Anonymised Information Linkage databank.  The GP records of each patient were selected one year before diagnosis up to hospital admission. An artificial intelligence technique, neural network with entropy regularisation, was used to automatically identify the most influential predictors from initial GP Read codes, gender and age. K-fold cross validation was used to assess the predictive performance of the identified signals. Findings: 52.5 million individual records about 59,298 dementia patients were utilised. Among them, there were 30,178 patients admitted to hospital and 29,120 patients remaining within GP. The male patients had more cases of admissions than those of staying with GP (11233/9441), while the female patients have more cases of staying with GP than those of admissions (19679/18945). From the 54,649 initial event codes, 10 most important signals were identified, including 2 diagnostic events (“Nightmares”, “Essential hypertension”), 5 medication events (“Serc-16 tablet”, “Ibugel gel 100g”, “Simvastatin 40mg tablet”, “Influvac sub-unit prefilled syringe 0.5mL”, “Adcal-D3 1.5g/10ug chewable tablet”) and 3 procedural events (“Third party encounter”, “Social group 3 - skilled”, “Blood glucose raised”). They performed significantly above chance to predict the outcomes of dementia with the sensitivity of 0.758±0.014, specificity of 0.759±0.025, precision of 0.766±0.016, and negative predictive value of 0.751±0.005. Interpretation: Our findings suggest that machine learning models provide an effective means of identifying influential clinical signals to predict the outcomes of dementia significantly above chance.  Funding: National Centre for Population Health and Wellbeing Research (CA02). Contributors: SMZ conceived the study. GT extracted data in the cohort. GT, SMZ and XX conceived the method. GT and SMZ completed the experiments. SMZ, GT, XX and LH analysed the results. SMZ and LH wrote up the abstract. RL, SB and SMZ interpreted the results. All authors reviewed the outcomes. Declaration of interests:  We declare no competing interests. 

Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach

https://cronfa.swan.ac.uk/Record/cronfa45957/Download/0045957-07022019091712.pdf

Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach

Abstract

Similar works

Full text

Available Versions

Cronfa at Swansea University