Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will ...
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ISBN:
(数字)9783031177217
ISBN:
(纸本)9783031177217;9783031177200
Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machinelearning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.
The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion...
ISBN:
(纸本)9783030871000
The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired data-Sampling Prototype for Time-Series Analysis;membership-Mappings for data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series pattern Discovery by Deep learning and Graph mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the machinelearning Algorithm for Gene Expression data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case study;Privacy Preserving machinelearning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction strategies for Node Embeddings with RDF2Vec in Knowledge Graphs;bridging Semantic Web and machinelearning: First Results of a Systematic Mapping study.
The utilization of machinelearning (ML) and patternrecognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical d...
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ISBN:
(数字)9798350367720
ISBN:
(纸本)9798350367737
The utilization of machinelearning (ML) and patternrecognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machinelearning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding.
We propose the k-subspace method, a clustering method based on the subspace method, and the k-subspace classification method, a patternrecognition method that utilizes multiple subspaces for each class. Conventional ...
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ISBN:
(数字)9798331504120
ISBN:
(纸本)9798331504137
We propose the k-subspace method, a clustering method based on the subspace method, and the k-subspace classification method, a patternrecognition method that utilizes multiple subspaces for each class. Conventional clustering algorithms, such as the k-means method, operate based on centroids. However, in image recognition, subspaces provide a more essential representation. Existing techniques, such as CLAFIC, define a single subspace per class. However, real-world data often exhibit significant intra-class variations, such as diverse character styles within the same class. We hypothesize that partitioning subspaces into subsets, which account for intra-class variations, will enhance classification accuracy. In this study, we examine the feasibility of partitioning a sub-space into $k$ subclasses. We apply the proposed method to image recognition and experimentally demonstrate its effectiveness.
The proceedings contain 12 papers. The special focus in this conference is on Explainable Artificial Intelligence in Healthcare. The topics include: Interpreting machinelearning Models for Survival Analysis: A S...
ISBN:
(纸本)9783031543029
The proceedings contain 12 papers. The special focus in this conference is on Explainable Artificial Intelligence in Healthcare. The topics include: Interpreting machinelearning Models for Survival Analysis: A study of Cutaneous Melanoma Using the SEER database;probExplainer: A Library for Unified Explainability of Probabilistic Models and an Application in Interneuron Classification;An Explainable AI Framework for Treatment Failure Model for Oncology Patients;explanations of Symbolic Reasoning to Effect Patient Persuasion and Education;explainable Artificial Intelligence in Response to the Failures of Musculoskeletal Disorder Rehabilitation;phenotypes vs Processes: Understanding the Progression of Complications in Type 2 Diabetes. A Case study;A data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Path Discovery, Model Generation to Validation;preface;understanding Prostate Cancer Care Process Using Process mining: A Case study;From Script to Application. A bupaR Integration into PMApp for Interactive Process mining Research.
Once ranked last in Europe for public access to judgment data, the United Kingdom has taken large strides in recent years to improve the accessibility of judgments. This paper discusses how the new platform from The N...
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This comprehensive study delves into the revolutionary possibilities of AI and ML in predictive analytics for gauging private insurance industry employees' stress levels. Recognizing the root causes and anticipati...
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Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links with...
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In response to the prevalent symptoms of depression, the complexity of diagnostic processes, and individuals’ resistance to psychological testing in contemporary society, this study proposes an automated depression d...
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ISBN:
(数字)9798331539757
ISBN:
(纸本)9798331539764
In response to the prevalent symptoms of depression, the complexity of diagnostic processes, and individuals’ resistance to psychological testing in contemporary society, this study proposes an automated depression detection method based on audio analysis. This method aims to enhance the accuracy of depression state recognition by comparing the efficiency of different models, including machinelearning and deep learning. Initially, the study collected audio sample data from 50 individuals, including both healthy subjects and patients with depression. During the audio data processing phase, features related to depression recognition, such as Mel-frequency cepstral coefficients (MFCC) and fundamental frequency, were extracted from these samples. Subsequently, a series of recognition comparison experiments were conducted based on the extracted features, involving various algorithmic models such as Support Vector machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN). Experimental results demonstrated that the Convolutional Neural Network exhibited higher accuracy in recognizing depression states from audio data, with an average recognition accuracy rate of 95.82%. This finding indicates that deep learning models, especially Convolutional Neural Networks, have significant advantages in addressing such issues, providing robust technical support for the future automatic detection of depression.
We present a retrospective analysis of Czech anti-covid governmental measures' effectiveness for an unusually long three years of observation. Numerous Czech government restrictive measures illustrate this analysi...
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