Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Abstract

Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient’s well-being and reduce the burden on emergency health system services. Methods: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. Results: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. Conclusions: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

“…Dataset Specification. The most retrieved ML diagnosis models are based on signs and symptoms [19][20][21][22][23][24], followed by models based on laboratory tests [25,26]. Moreover, we found ML models based on clinical and electronic health records (EHRs) [27,28], ML models based on clinical reports [29], ML models based on image processing with other techniques [30,31], and ML models based on a combination of predictors including abnormal lab test results, the incidence rates, and signs and symptoms, as well as epidemiological features [32].…”

“…Computational and Mathematical Methods in Medicine Reference Pros Cons [19] (i) The proposed method provides efficient hospital resources (ii) Simple and more generic features are used to encode the waveform dynamics in time and frequency domains (iii) Low-cost wearable sensors are used to collect data (i) The manual encoding of features used to encode the waveform dynamics in time and frequency domains is time-consuming and may have errors (ii) The dataset is small [20] (i) The study shows an accessible, easy to use, flexible, ubiquitous, and cost-effective eHealth system for diagnosing infectious diseases from vital signs.…”

“…The authors in [20] introduced a combination of ML technology, cloud services, and mobile communications and performed pattern recognition analysis. Three ML In addition, the NB classifier was implemented in [24,32] to classify several infectious diseases.…”

“…Moreover, physiological waveforms collected from sensors are susceptible to noise and should be filtered using a pass filter followed by a Gaussian filter [19]. In contrast, the biosensors in [20] collected five vital signs directly from patients to a hub that quantifies and mul-tiplexes the signals and sends them to a mobile application through Bluetooth technology.…”

New Method to Implement and Analysis of Medical System in Real Time

“…The course emphasized that artificial intelligence leads to better diagnosis and prediction of clinical schedules and incurable diseases. Garces et al [ 25 ] used cloud services and machine learning technology to provide effective tools for doctors to monitor and diagnose diseases in remote areas with low cost and high accuracy. In their study, vital signs were taken from residents of a specific area daily for seven days and transferred to a database for analysis.…”