Computational healthcare: Present and future perspectives (Review)

Asai, Ayumu; Konno, Masamitsu; Taniguchi, Masateru; Vecchione, Andrea; Ishii, Hideshi

doi:10.3892/etm.2021.10786

Cited by 9 publications

(5 citation statements)

References 62 publications

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“…The modern trends in technology have resulted in the generation of massive high-dimensional datasets [38,39]. The study of high-dimensional statistics has attracted immense interest from scholars and data scientists [40].…”

Section: High-dimensional Datasetsmentioning

confidence: 99%

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

Gikera,

Mwaura,

Muuro

et al. 2023

Journal on Artificial Intelligence

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k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this issue, may be inconsistent. Although various techniques for solving smooth elbow challenges exist, k-hyperparameter tuning in high-dimensional spaces still remains intractable and an open research issue. In this paper, we have first reviewed the existing techniques for solving smooth elbow challenges. The identified research gaps are then utilized in the development of the new technique. The new technique, referred to as the ensemble-based technique of a self-adapting autoencoder and internal validation indexes, is then validated in high-dimensional space clustering. The optimal k-value, tuned by this technique using a voting scheme, is a trade-off between the number of clusters visualized in the autoencoder's latent space, k-value from the ensemble internal validation index score and one that generates a value of 0 or close to 0 on the derivativeat the elbow. Experimental results based on the Cochran's Q test, ANOVA, and McNemar's score indicate a relatively good performance of the newly developed technique in k-hyperparameter tuning. KEYWORDS k-hyperparameter tuning; high-dimensional; smooth elbow K-Means ArchitectureK-means is an iterative algorithm that aims to partition a dataset into a set of k non-overlapping groups of data points [9]. The k-hyperparameter is one of the most important hyperparameters to tune in k-means [10,11]. Tuning a machine learning model's hyperparameters has a significant effect on its performance [12]. In this subsection, we explore k-clusters, k-hyperparameters, and the traditional elbow method used to identify the optimal value for the k-hyperparameter. K-Means ClustersThe k-means clusters are the resulting data sub-groups generated by the popular unsupervised partitioning algorithm, known as the k-means clustering algorithm [13]. Cluster analysis using the k-means algorithm is an example of a k-means-based model that has been successfully applied in cluster analysis [14,15]. The k-clusters generated by these algorithms are usually composed of distinct non-overlapping groups of data points, aggregated together because they share specific similarities [16]. The data points within a particular cluster are similar, while the data points across different clusters are different [17]. Both the intra-cluster and inter-cluster data points are measured using a sum of squared distance-based metric [18,19]. For this reason, the original un-partitioned dataset is stan...

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Section: High-dimensional Datasetsmentioning

confidence: 99%

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

Gikera,

Mwaura,

Muuro

et al. 2023

Journal on Artificial Intelligence

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“…Conducting an MAI social experiment is crucial for exploring the application of MAI and analysing its potential impacts, which helps properly handle the relationship between MAI, humans and society. [11][12][13] Due to the high level of uncertainty and significant ethical risks associated with MAI, implementing ethics in these social experiments is of utmost importance. 14 15 Research institutions in China and other countries have made significant efforts to establish principles, guidelines and norms for the ethical governance of MAI.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children’s hospitals in Shanghai: a cross-sectional study

Wang,

Fu,

et al. 2023

BMJ Open

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ObjectivesImplementing ethics is crucial to prevent harm and promote widespread benefits in social experiments based on medical artificial intelligence (MAI). However, insufficient information is available concerning this within the paediatric healthcare sector. We aimed to conduct a comparative survey among paediatricians, nurses and health information technicians regarding ethics implementation knowledge of and attitude towards MAI social experiments at children’s hospitals in Shanghai.Design and settingA cross-sectional electronic questionnaire was administered from 1 July 2022 to 31 July 2022, at tertiary children’s hospitals in Shanghai.ParticipantsAll the eligible individuals were recruited. The inclusion criteria were as follows: (1) should be a paediatrician, nurse and health information technician, (2) should have been engaged in or currently participating in social experiments based on MAI, and (3) voluntary participation in the survey.Primary outcomeEthics implementation knowledge of and attitude to MAI social experiments among paediatricians, nurses and health information technicians.ResultsThere were 137 paediatricians, 135 nurses and 60 health information technicians who responded to the questionnaire at tertiary children’s hospitals. 2.4–9.6% of participants were familiar with ethics implementation knowledge of MAI social experiments. 31.9–86.1% of participants held an ‘agree’ ethics implementation attitude. Health information technicians accounted for the highest proportion of the participants who were familiar with the knowledge of implementing ethics, and paediatricians or nurses accounted for the highest proportion among those who held ‘agree’ attitudes.ConclusionsThere is a significant knowledge gap and variations in attitudes among paediatricians, nurses and health information technicians, which underscore the urgent need for individualised education and training programmes to enhance MAI ethics implementation in paediatric healthcare.

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“…AI research in healthcare accelerates with applications achieving human-level performance across various fields of medicine. The use of AI can range from organizational help to surgical applications, with image classification for diagnostic support being one of the main areas of interest [ 6 , 7 ]. IB Neuro™ (Imaging Biometrics, Elm Grove, WI, USA) was the first FDA-approved AI application in 2008 for detecting brain tumors on MRI images.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple applications have been approved since then, many in medical imaging domains such as radiology. Some applications go beyond diagnosis and enter therapeutic fields such as radiotherapy [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification

Peeters

Rommes

Elen

et al. 2023

JCM

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Aim: To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis. Methods: The algorithm’s threshold value was fixed at the 90% sensitivity operating point on the receiver operating curve to perform the disease classification. Diagnostic performance was appraised on a private test set and publicly available datasets. Stratification analysis was executed on the private test set considering age, ethnicity, sex, insulin dependency, year of examination, camera type, image quality, and dilatation status. Results: The software displayed an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on the private test set. The specificity and sensitivity for combined DR and DME predictions were 94.24 and 90.91%, respectively. The AUC ranged from 96.91 to 97.99% on the publicly available datasets for DR. AUC values were above 95% in all subgroups, with lower predictive values found for individuals above the age of 65 (82.51% sensitivity) and Caucasians (84.03% sensitivity). Conclusion: We report good overall performance of the MONA.health screening software for DR and DME. The software performance remains stable with no significant deterioration of the deep learning models in any studied strata.

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Computational healthcare: Present and future perspectives (Review)

Cited by 9 publications

References 62 publications

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children’s hospitals in Shanghai: a cross-sectional study

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification

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