Machine Learning for Healthcare: Introduction

Gupta, Shiwani; Sedamkar, R. R.

doi:10.1007/978-3-030-40850-3_1

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…These methods can process large quantities of data without entirely depending on a priori knowledge of predictive factors like age and gender for the detection of distinct movement patterns [ 20 ]. Patients having similar pathologies can be grouped based on regularities in the data, using so-called unsupervised machine learning methods, such as the popular k-means clustering technique [ 21 , 22 ]. Cluster analysis has been used to identify functional groups in patients walking with crouch gait [ 23 ], patients with flexible flatfeet [ 24 ], swimming [ 25 ] and running [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis

Drongelen

Stetter

Böhm

et al. 2021

JCM

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Despite good clinical functional outcome, deficits in gait biomechanics exist 2 years after total hip replacement surgery. The aims of this research were (1) to group patients showing similar gait adaptations to hip osteoarthritis and (2) to investigate the effect of the surgical treatment on gait kinematics and external joint moments. In a secondary analysis, gait data of 51 patients with unilateral hip osteoarthritis were analyzed. A k-means cluster analysis was performed on scores derived via a principal component analysis of the gait kinematics. Preoperative and postoperative datasets were statistically tested between clusters and 46 healthy controls. The first three principal components incorporated hip flexion/extension, pelvic tilt, foot progression angle and thorax tilt. Two clusters were discriminated best by the peak hip extension during terminal stance. Both clusters deviated from healthy controls in spatio-temporal, kinematic and kinetic parameters. The cluster with less hip extension deviated significantly more. The clusters improved postoperatively but differences to healthy controls were still present one year after surgery. A poor preoperative gait pattern in patients with unilateral hip osteoarthritis is associated with worse gait kinematics after total hip replacement. Further research should focus on the identification of patients who can benefit from an adapted or individualized rehabilitation program.

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Section: Introductionmentioning

confidence: 99%

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis

Drongelen

Stetter

Böhm

et al. 2021

JCM

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“…The authors of [41] presented numerous flaws in healthcare's current evidence-based approaches. However, they showed how insufficient biased evidence lead to ineffective care.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability

Kaddoura

2022

Sustainability

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Water has become intricately linked to the United Nations’ sixteen sustainable development goals. Access to clean drinking water is crucial for health, a fundamental human right, and a component of successful health protection policies. Clean water is a significant health and development issue on a national, regional, and local level. Investments in water supply and sanitation have been shown to produce a net economic advantage in some areas because they reduce adverse health effects and medical expenses more than they cost to implement. However, numerous pollutants are affecting the quality of drinking water. This study evaluates the efficiency of using machine learning (ML) techniques in order to predict the quality of water. Thus, in this paper, a machine learning classifier model is built to predict the quality of water using a real dataset. First, significant features are selected. In the case of the used dataset, all measured characteristics are chosen. Data are split into training and testing subsets. A set of existing ML algorithms is applied, and the results are compared in terms of precision, recall, F1 score, and ROC curve. The results show that support vector machine and k-nearest neighbor are better according to F1-score and ROC AUC values. However, The LASSO LARS and stochastic gradient descent are better based on recall values.

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“…In the case of service industry, machine-learning algorithms have also been successfully applied. applied machine learning in the healthcare industry to identify ethical considerations, while Gupta and Sedamkar (2020) deployed machine learning to correctly identify the percentage of sick versus healthy people. For the tourism and hospitality industry, machine learning has shown significant potential.…”

Section: Application Of Machine Learning In Various Industrial Segmentsmentioning

confidence: 99%

“…There are various applications of machine learning methods that have been applied in different industrial sectors in recent years. These methods have been applied in various industrial segments such as healthcare, banking, manufacturing, transportation, etc (Li, Wang, and Wang 2019;Xie 2020;Gupta et al 2020;Danton et al 2020). Studies have shown that the adoption of machine learning algorithms can contribute to reducing costs by between 20%-25% across banking, IT operations, infrastructure, and maintenance, improve customer retention and acquisition, etc.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Shamsuzzoha

Kankaanpaa

Nguyen

et al. 2021

International Journal of Computer Integrated Manufacturing

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This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.

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Machine Learning for Healthcare: Introduction

Cited by 17 publications

References 16 publications

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis

Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis

Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

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