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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Mi Y, Shi Y, Li J, Liu W, Yan M. Fuzzy-Based Concept Learning Method: Exploiting Data With Fuzzy Conceptual Clustering. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:582-593. [PMID: 32275634 DOI: 10.1109/tcyb.2020.2980794] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering for concept classification and concept discovery. However, the standard CCL algorithms are incapable of tackling continuous data directly, and some standard conceptual clustering methods mainly focus on the attribute information, ignoring the object information that is also important to improve clustering analysis and concept classification ability. Therefore, in this article, we present a novel concept learning method, called the fuzzy-based concept learning model (FCLM), to address these two issues by exploiting concept hierarchical relations in concept lattices. More specifically, we first show some new related notions for FCLM based on a regular fuzzy formal decision context; among these notions, the object-oriented and attribute-oriented fuzzy concept similarities are used to achieve the concept similarity measure in concept lattices. Moreover, a novel fuzzy concept learning framework is designed, and its corresponding learning algorithms are developed. Finally, we conduct some experiments on various real-world datasets to demonstrate that the proposed method can achieve the state-of-the-art classification performance among similarity-based learning methods. In addition, we further verify the effectiveness of our method in concept discovery on the MNIST dataset.
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Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2020. [DOI: 10.3390/jmse8090689] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model.
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Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16091511. [PMID: 31035655 PMCID: PMC6539188 DOI: 10.3390/ijerph16091511] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 04/18/2019] [Accepted: 04/25/2019] [Indexed: 11/17/2022]
Abstract
Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people's unsafe behavior and objective's unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet's value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.
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Zhang P, Essaid A, Zanni-Merk C, Cavallucci D, Ghabri S. Experience capitalization to support decision making in inventive problem solving. COMPUT IND 2018. [DOI: 10.1016/j.compind.2018.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Dale M. Compression and knowledge discovery in ecology. COMMUNITY ECOL 2013. [DOI: 10.1556/comec.14.2013.2.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Bichindaritz I. PROTOTYPICAL CASES FOR RETRIEVAL, REUSE, AND KNOWLEDGE MAINTENANCE IN BIOMEDICAL CASE-BASED REASONING. Comput Intell 2009. [DOI: 10.1111/j.1467-8640.2009.00339.x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhang L, Coenen F, Leng P. Formalising optimal feature weight setting in case based diagnosis as linear programming problems. Knowl Based Syst 2002. [DOI: 10.1016/s0950-7051(02)00023-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Faucher C. Approximate knowledge modeling and classification in a frame-based language: The system CAIN. INT J INTELL SYST 2001. [DOI: 10.1002/int.1033] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Abstract
Representation has always been a central part of models in cognitive science, but this idea has come under attack. Researchers advocating the alternative approaches of perceptual symbol systems, situated action, embodied cognition, and dynamical systems have argued against central assumptions of the classical representational approach to mind. We review the core assumptions of the representational view and these four suggested alternatives. We argue that representation should remain a core part of cognitive science, but that the insights from these alternative approaches must be incorporated into models of cognitive processing.
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Hahn U, Romacker M, Schulz S. How knowledge drives understanding--matching medical ontologies with the needs of medical language processing. Artif Intell Med 1999; 15:25-51. [PMID: 9930615 DOI: 10.1016/s0933-3657(98)00044-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In this article, we introduce a knowledge-based approach to medical text understanding. From an in-depth consideration of deep sentence and text understanding we distill basic requirements for an adequate knowledge representation framework. These requirements are then matched with currently available medical ontologies (thesauri, terminologies, etc.). A fundamental trade-off is recognized between large-scale conceptual coverage on the one hand, and formal mechanisms for integrity preservation and conceptual expressiveness on the other hand. We discuss various shortcomings of the most wide-spread ontologies to capture medical knowledge in-the-large. As a result, we argue for the need of a formally sound and expressive model along the lines of KL-ONE-style terminological representation systems in the format of description logics. These provide an adequate methodology for designing more sophisticated, flexible medical ontologies serving the needs of 'deep' knowledge applications which are by no means restricted to medical language processing.
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Affiliation(s)
- U Hahn
- Computational Linguistics Division-Text Knowledge Engineering Lab, Freiburg University, Germany.
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Smyth B, Keane MT. Adaptation-guided retrieval: questioning the similarity assumption in reasoning. ARTIF INTELL 1998. [DOI: 10.1016/s0004-3702(98)00059-9] [Citation(s) in RCA: 109] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Gonzalez A, Lingli Xu, Gupta U. Validation techniques for case-based reasoning systems. ACTA ACUST UNITED AC 1998. [DOI: 10.1109/3468.686707] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Jurisica I, Mylopoulos J, Glasgow J, Shapiro H, Casper RF. Case-based reasoning in IVF: prediction and knowledge mining. Artif Intell Med 1998; 12:1-24. [PMID: 9475949 DOI: 10.1016/s0933-3657(97)00037-7] [Citation(s) in RCA: 70] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
In vitro fertilization (IVF) is a medically-assisted reproduction technique, enabling infertile couples to achieve successful pregnancy. Given the unpredictability of the task, we propose to use a case-based reasoning system that exploits past experiences to suggest possible modifications to an IVF treatment plan in order to improve overall success rates. Once the system's knowledge base is populated with a sufficient number of past cases, it can be used to explore and discover interesting relationships among data, thereby achieving a form of knowledge mining. The article describes the TA3IVF system--a case-based reasoning system which relies on context-based relevance assessment to assist in knowledge visualization, interactive data exploration and discovery in this domain. The system can be used as an advisor to the physician during clinical work and during research to help determine what knowledge sources are relevant for a treatment plan.
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Affiliation(s)
- I Jurisica
- Department of Computer Science, University of Toronto, Ontario, Canada.
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Abstract
The distinction between rule-based and similarity-based processes in cognition is of fundamental importance for cognitive science, and has been the focus of a large body of empirical research. However, intuitive uses of the distinction are subject to theoretical difficulties and their relation to empirical evidence is not clear. We propose a 'core' distinction between rule- and similarity-based processes, in terms of the way representations of stored information are 'matched' with the representation of a novel item. This explication captures the intuitively clear-cut cases of processes of each type, and resolves apparent problems with the rule/similarity distinction. Moreover, it provides a clear target for assessing the psychological and AI literatures. We show that many lines of psychological evidence are less conclusive than sometimes assumed, but suggest that converging lines of evidence may be persuasive. We then argue that the AI literature suggests that approaches which combine rules and similarity are an important new focus for empirical work.
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Affiliation(s)
- U Hahn
- Department of Psychology, University of Warwick, Coventry, UK.
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Greiner R, Grove AJ, Kogan A. Knowing what doesn't matter: exploiting the omission of irrelevant data. ARTIF INTELL 1997. [DOI: 10.1016/s0004-3702(97)00048-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Gupta K, Montezemi A. Empirical evaluation of retrieval in case-based reasoning systems using modified cosine matching function. ACTA ACUST UNITED AC 1997. [DOI: 10.1109/3468.618259] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Diamantidis N, Giakoumakis EA. Don't care values in induction. Artif Intell Med 1996; 8:505-14. [PMID: 8955859 DOI: 10.1016/s0933-3657(96)00357-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Inductive learning algorithms are powerful tools for the extraction of knowledge from data. Their success in medical domains is well-known. In medical diagnosis domains and generally in real-world applications among other problems, inductive learning algorithms have to deal with unknown values. In most cases unknown values are treated as missing ones, i.e. unknown values which are related to the class of training examples, but are missing due to lack of measurements. In this paper we address the problem of don't care values, which are unknown, because they are irrelevant to the class of the examples. The distinction of don't care values and missing ones is important in medical domains. With this distinction the experts are able to relate each diagnosis to the appropriate subset of attributes. We present techniques for dealing efficiently with don't care values in the induction of decision trees. Furthermore, we examine the importance of the distinction between missing and don't care values and we investigate the existence of don't care values instead of missing ones, in medical and non-medical real-world datasets.
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Affiliation(s)
- N Diamantidis
- Informatics Department, Athens University of Economics and Business, Greece.
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Holte R, Mkadmi T, Zimmer R, MacDonald A. Speeding up problem solving by abstraction: a graph oriented approach. ARTIF INTELL 1996. [DOI: 10.1016/0004-3702(95)00111-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration. DATA KNOWL ENG 1995. [DOI: 10.1016/0169-023x(95)00017-m] [Citation(s) in RCA: 94] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Evans CD. A case-based assistant for diagnosis and analysis of dysmorphic syndromes. MEDICAL INFORMATICS = MEDECINE ET INFORMATIQUE 1995; 20:121-31. [PMID: 8569305 DOI: 10.3109/14639239509025350] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
This report describes the development of a case-based reasoning (CBR) system designed to provide assistance to specialists within the dysmorphology field. An interactive CBR model is described which aids medical experts in arriving at potential diagnoses, and has an explicit learning goal in order to provide further analytical assessment of syndrome categories. The complexity of this real world domain has highlighted a number of important empirical issues with respect to CBR techniques, which are discussed along with the system design.
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Affiliation(s)
- C D Evans
- Department of Computer Science, University College London, UK
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Categorization, Concept Learning, and Problem-Solving: A Unifying View. PSYCHOLOGY OF LEARNING AND MOTIVATION 1993. [DOI: 10.1016/s0079-7421(08)60141-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register]
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Mooney RJ. Induction over the unexplained: Using overly-general domain theories to aid concept learning. Mach Learn 1993. [DOI: 10.1007/bf00993482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Bareiss R, Slator BM. The Evolution of a Case-Based Computational Approach to Knowledge Representation, Classification, and Learning. PSYCHOLOGY OF LEARNING AND MOTIVATION 1993. [DOI: 10.1016/s0079-7421(08)60139-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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A reply to Reich's book review of Exemplar-based knowledge acquisition. Mach Learn 1991. [DOI: 10.1007/bf00153764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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