1
|
Roock ED, Martin N. Process mining in healthcare – an updated perspective on the state of the art. J Biomed Inform 2022; 127:103995. [DOI: 10.1016/j.jbi.2022.103995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/29/2021] [Accepted: 01/10/2022] [Indexed: 10/19/2022]
|
2
|
Alonso SG, de la Torre-Díez I, Hamrioui S, López-Coronado M, Barreno DC, Nozaleda LM, Franco M. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst 2018; 42:161. [PMID: 30030644 DOI: 10.1007/s10916-018-1018-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 07/16/2018] [Indexed: 12/12/2022]
Abstract
Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as 'techniques' AND 'Data Mining' AND 'Mental Health', 'algorithms' AND 'Data Mining' AND 'dementia' AND 'schizophrenia' AND 'depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.
Collapse
Affiliation(s)
- Susel Góngora Alonso
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
| | - Sofiane Hamrioui
- Bretagne Loire and Nantes Universities, UMR 6164, IETR Polytech Nantes, Nantes, France
| | - Miguel López-Coronado
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Diego Calvo Barreno
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Lola Morón Nozaleda
- Nozaleda and Lafora Mental Health Clinic, C/ José Ortega Y Gasset, 44, 28006, Madrid, Spain
| | - Manuel Franco
- Psiquiatry Service, Hospital Zamora, Hernán Cortés, Zamora, Spain
| |
Collapse
|
3
|
Meneu T, Traver V, Guillen S, Valdivieso B, Benedi J, Fernandez-Llatas C. Heart Cycle: facilitating the deployment of advanced care processes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:6996-9. [PMID: 24111355 DOI: 10.1109/embc.2013.6611168] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make their deployment and dissemination extremely difficult in real practice environments. Furthermore, the intrinsic difficulties for the design of formal Clinical Pathways requires new specific design tools to help making them relly useful and cost-effective. Process Mining techniques can help to automatically infer processes definition from execution samples and, thus, support the automatization of the standardization and continuous control of healthcare processes. This way, they can become a relevant helping tool for clinical experts and healthcare systems for reducing variability in clinical practice and better understand the performance of the system.
Collapse
|
4
|
Fernández-Llatas C, Benedi JM, García-Gómez JM, Traver V. Process mining for individualized behavior modeling using wireless tracking in nursing homes. SENSORS (BASEL, SWITZERLAND) 2013; 13:15434-51. [PMID: 24225907 PMCID: PMC3871075 DOI: 10.3390/s131115434] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Revised: 10/30/2013] [Accepted: 11/04/2013] [Indexed: 11/18/2022]
Abstract
The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.
Collapse
Affiliation(s)
- Carlos Fernández-Llatas
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mails: (J.M.G.-G.); (V.T.)
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain
| | - José-Miguel Benedi
- Instituto Tecnológico de Informática (ITI), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mail:
| | - Juan M. García-Gómez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mails: (J.M.G.-G.); (V.T.)
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA). Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain; E-Mails: (J.M.G.-G.); (V.T.)
- Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politécnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain
| |
Collapse
|