Machine Learning Prediction Models for Recovery After Colorectal Cancer Surgery Using Wearable Device Data, Air Quality Data, and Clinical Evaluation Data (Preprint)

Author:

Wu Chia-Tung,Su Kai-Zheng,Hsieh Tsung-Ting,Jhao Lian-Yin,Liao Yu-Tso

Abstract

BACKGROUND

With the advancement of medical technology, minimally invasive surgery has gradually replaced traditional surgery as the primary treatment for colorectal cancer because of its more minor wound and shortened length of hospitalization. The subject of our research is how to investigate the postoperative recovery status further on this basis.

OBJECTIVE

The study hopes to use the physiological and clinical data combined with the patient's lifestyle indicators and use machine learning models to predict the patient's postoperative recovery.

METHODS

A total of 85 patients, data had been collected from National Taiwan University Hospital and National Taiwan University Hospital Hsinchu Branch, undergoing minimally invasive surgery for colorectal cancer. We use wearable devices to collect the participant's step count data, select the nearest station according to their location to collect the PM2.5 in the air quality index of the day as environmental data and merge the two lifestyle indicators above with physiological and clinical data to predict. We use postoperative complications, defined by the Clavien-Dindo classification, as the basis for judging the situation of postoperative recovery. Four data pre-processing methods and six machine learning classification models were used to predict, and each feature was discussed in an interpretable model SHAP.

RESULTS

In each data set and machine learning model, the performance of the third data set trained with Random Forest and the performance of the fourth data set trained with XgBoost are better. Based on the results, we found that the model tended to predict a good recovery on the participants with more steps/day than 2,000 and low sub-index of PM2.5 in their living environment.

CONCLUSIONS

Combined with the patient's physiological and clinical data, step counts, and air quality data, through the data pre-processing, machine learning prediction, and interpretable model analysis, the merged data are used to classify the participants' postoperative recovery status. We found that if the patient had a daily essential exercise (based on the number of steps, more than 2,000 steps per day) in an environment where PM2.5 is low (PM2.5 sub-index is less than 36), it would help Postoperative recovery.

Publisher

JMIR Publications Inc.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3