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Int J Med Inform. 2020 May;137:104105. doi: 10.1016/j.ijmedinf.2020.104105. Epub 2020 Mar 3.

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

Author information

1
Department of Health Management, Hangzhou Normal University, Hangzhou, China. Electronic address: yechengyin@hznu.edu.cn.
2
Department of Health Management, Hangzhou Normal University, Hangzhou, China. Electronic address: lijinmei@stu.hznu.edu.cn.
3
Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States; Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States. Electronic address: shiyingh@stanford.edu.
4
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: mliu@hbisolutions.com.
5
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: hjin@hbisolutions.cn.
6
Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States; Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States. Electronic address: zhengl07@stanford.edu.
7
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: cxia@hbisolutions.com.
8
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: ejin@hbisolutions.com.
9
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: czhu@hbisolutions.com.
10
HealthInfoNet, Portland, ME, United States. Electronic address: salfreds@hinfonet.org.
11
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: fstearns@hbisolutions.com.
12
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: lkanov@hbisolutions.com.
13
Department of Surgery, Stanford University, Stanford, CA, United States. Electronic address: karls@stanford.edu.
14
HBI Solutions Inc., Palo Alto, CA, United States. Electronic address: Ewiden@hbisolutions.com.
15
Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States; Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States. Electronic address: Doff@stanford.edu.
16
Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, CA, United States; Department of Surgery, Stanford University, Stanford, CA, United States. Electronic address: bxling@stanford.edu.

Abstract

OBJECTIVE:

Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls.

METHODS:

The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age).

RESULTS:

This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event.

CONCLUSIONS:

By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.

KEYWORDS:

Accidental falls; Aged; Electronic health records; Supervised machine learning

PMID:
32193089
DOI:
10.1016/j.ijmedinf.2020.104105
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Conflict of interest statement

Declaration of Competing Interest The authors certify that they do not have any financial or other conflicts of interest in relation to this manuscript.

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