Patients being admitted to a hospital will most often be associated with a
certain clinical development during their stay. However, there is always a risk
of patients being subject to the wrong diagnosis or to a certain treatment not
pertaining to the desired effect, potentially leading to adverse events. Our
research aims to develop an anomaly detection system for identifying deviations
from expected clinical trajectories. To address this goal we analyzed 16 months
of vital sign recordings obtained from the Nordland Hospital Trust (NHT). We
employed an self-supervised framework based on the STraTS transformer
architecture to represent the time series data in a latent space. These
representations were then subjected to various clustering techniques to explore
potential patient phenotypes based on their clinical progress. While our
preliminary results from this ongoing research are promising, they underscore
the importance of enhancing the dataset with additional demographic information
from patients. This additional data will be crucial for a more comprehensive
evaluation of the method's performance.

我们的研究旨在开发一种异常检测系统，以识别与预期临床发展不符的偏差。通过分析挪威北部医院信托公司（NHT）获得的 16 个月的生命体征记录，我们采用基于 STraTS 变压器架构的自监督框架来表示时间序列数据。然后，利用各种聚类技术对这些表示进行处理，以探索基于患者临床进展的潜在表型。尽管我们正在进行的研究的初步结果是令人鼓舞的，但它们强调了从患者获得的额外人口统计学信息对方法性能更全面的评估的重要性。

基于临床时间序列的转换器方法接近不良事件检测

Approaching adverse event detection utilizing transformers on clinical  time-series

Mobile phones and other electronic gadgets or devices have aided in
collecting data without the need for data entry. This paper will specifically
focus on Mobile health data. Mobile health data use mobile devices to gather
clinical health data and track patient vitals in real-time. Our study is aimed
to give decisions for small or big sports teams on whether one athlete good fit
or not for a particular game with the compare several machine learning
algorithms to predict human behavior and health using the data collected from
mobile devices and sensors placed on patients. In this study, we have obtained
the dataset from a similar study done on mhealth. The dataset contains vital
signs recordings of ten volunteers from different backgrounds. They had to
perform several physical activities with a sensor placed on their bodies. Our
study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree,
Random Forest, and Logistic Regression) to analyze and predict human health
behavior. XGBoost performed better compared to the other machine learning
algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in
specificity, and 99.66% in F1 score. Our research indicated a promising future
in mhealth being used to predict human behavior and further research and
exploration need to be done for it to be available for commercial use
specifically in the sports industry.

本研究对 Mhealth 数据进行了分析，并使用了几种机器学习算法来预测人类的运动行为和健康状态，最终结果表明 XGBoost 相对其他算法表现更好。这项研究对于使用 Mhealth 预测一个人对特定运动比赛是否适合有着很大的前景和潜力。