Advancing home healthcare through machine learning: Predicting service time for enhanced patient care

Name: Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
Author: Selçuk, Yağmur Selenay, Göktürk, Elvin Çoban

İsim	Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
Yazar	Selçuk, Yağmur Selenay, Göktürk, Elvin Çoban
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	Correlation matrix, Data analysis, Home healthcare service, K-means clustering, Machine learning, Pre-processing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835033752-5
Kayıt Numarası	38492eb0-572d-4b8f-8004-7ab3763e0990
Lokasyon	Industrial Engineering
Tarih	2023
Örnek Metin	Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time.
DOI	10.1109/HORA58378.2023.10156805

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Advancing home healthcare through machine learning: Predicting service time for enhanced patient care

Yazar Selçuk, Yağmur Selenay, Göktürk, Elvin Çoban

Basım Tarihi 2023

Basım Yeri - IEEE

Konu Correlation matrix, Data analysis, Home healthcare service, K-means clustering, Machine learning, Pre-processing

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835033752-5

Kayıt Numarası 38492eb0-572d-4b8f-8004-7ab3763e0990

Lokasyon Industrial Engineering

Tarih 2023

Örnek Metin Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time.

DOI 10.1109/HORA58378.2023.10156805