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Ma Y, He X, Yang T, Yang Y, Yang Z, Gao T, Yan F, Yan B, Wang J, Han L. Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta-analysis. J Clin Nurs 2024. [PMID: 39073235 DOI: 10.1111/jocn.17367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 04/13/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024]
Abstract
AIMS AND OBJECTIVES The main aim of this study is to synthesize the prevalent predictive models for pressure injuries in hospitalized patients, with the goal of identifying common predictive factors linked to pressure injuries in hospitalized patients. This endeavour holds the potential to provide clinical nurses with a valuable reference for providing targeted care to high-risk patients. BACKGROUND Pressure injuries (PIs) are a frequently occurring health problem throughout the world. There are mounting studies about risk prediction model of PIs reported and published. However, the prediction performance of the models is still unclear. DESIGN Systematic review and meta-analysis: The Cochrane Library, PubMed, Embase, CINAHL, Web of Science and Chinese databases including CNKI (China National Knowledge Infrastructure), Wanfang Database, Weipu Database and CBM (China Biology Medicine). METHODS This systematic review was conducted following PRISMA recommendations. The databases of Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and CNKI, Weipu Database, Wanfang Database and CBM were searched for all studies published before September 2023. We included studies with cohort, case-control designs, reporting the development of risk model and have been validated externally and internally among the hospitalized patients. Two researchers selected the retrieved studies according to the inclusion and exclusion criteria, and critically evaluated the quality of studies based on the CHARMS checklist. The PRISMA guideline was used to report the systematic review and meta-analysis. RESULTS Sixty-two studies were included, which contained 99 pressure injuries risk prediction models. The AUC (area under ROC curve) of modelling in 32 prediction models were reported ranged from .70 to .99, while the AUC of verification in 38 models were reported ranged from .70 to .98. Gender (OR = 1.41, CI: .99 ~ 1.31), age (WMD = 8.81, CI: 8.11 ~ 9.57), diabetes mellitus (OR = 1.64, CI: 1.36 ~ 1.99), mechanical ventilation (OR = 2.71, CI: 2.05 ~ 3.57), length of hospital stay (WMD = 7.65, CI: 7.24 ~ 8.05) were the most common predictors of pressure injuries. CONCLUSION Studies of PIs risk prediction model in hospitalized patients had high research quality, and the risk prediction models also had good predictive performance. However, some of the included studies lacked of internal or external validation in modelling, which affected the stability and extendibility. The aged, male patient in ICU, albumin, haematocrit, low haemoglobin level, diabetes, mechanical ventilation and length of stay in hospital were high-risk factors for pressure injuries in hospitalized patients. In the future, it is recommended that clinical nurses, in practice, select predictive models with better performance to identify high-risk patients based on the actual situation and provide care targeting the high-risk factors to prevent the occurrence of diseases. RELEVANCE TO CLINICAL PRACTICE The risk prediction model is an effective tool for identifying patients at the risk of developing PIs. With the help of risk prediction tool, nurses can identify the high-risk patients and common predictive factors, predict the probability of developing PIs, then provide specific preventive measures to improve the outcomes of these patients. REGISTRATION NUMBER (PROSPERO) CRD42023445258.
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Affiliation(s)
- Yuxia Ma
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Xiang He
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Tingting Yang
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Yifang Yang
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Ziyan Yang
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Tian Gao
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Fanghong Yan
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Boling Yan
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Juan Wang
- Department of Nursing, Second Hospital of Lanzhou University, Lanzhou, China
| | - Lin Han
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
- The First Hospital of Lanzhou University, Lanzhou, China
- Department of Nursing, Gansu Provincial Hospital, Lanzhou, China
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Vera-Salmerón E, Domínguez-Nogueira C, Sáez JA, Romero-Béjar JL, Mota-Romero E. Differentiating Pressure Ulcer Risk Levels through Interpretable Classification Models Based on Readily Measurable Indicators. Healthcare (Basel) 2024; 12:913. [PMID: 38727470 PMCID: PMC11083727 DOI: 10.3390/healthcare12090913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient's requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level.
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Affiliation(s)
- Eugenio Vera-Salmerón
- Servicio Andaluz de Salud, Distrito Sanitario Granada-Metropolitano, Centro de Salud Dr. Salvador Caballero de Granada, 18012 Granada, Spain; (E.V.-S.); (E.M.-R.)
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
| | - Carmen Domínguez-Nogueira
- Inspección Provincial de Servicios Sanitarios, Delegación Territorial de Granada, Consejería de Salud y Familias de la Junta de Andalucía, 41071 Sevilla, Spain;
| | - José A. Sáez
- Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain;
| | - José L. Romero-Béjar
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain;
- Institute of Mathematics, University of Granada (IMAG), Ventanilla 11, 18001 Granada, Spain
| | - Emilio Mota-Romero
- Servicio Andaluz de Salud, Distrito Sanitario Granada-Metropolitano, Centro de Salud Dr. Salvador Caballero de Granada, 18012 Granada, Spain; (E.V.-S.); (E.M.-R.)
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
- Department of Nursing, University of Granada, Avda. Ilustración 60, 18071 Granada, Spain
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Chuang YC, Miao T, Cheng F, Wang Y, Chien CW, Tao P, Kang L. Exploration of pressure injury risk in adult inpatients: An integrated Braden scale and rough set approach. Intensive Crit Care Nurs 2024; 80:103567. [PMID: 37924783 DOI: 10.1016/j.iccn.2023.103567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE This study aimed to develop an interpretive model with decision rules to assess the risk level of pressure injuries in adult inpatients and identify the critical risk factors associated with these injuries. METHODS The rough set approach was used to identify the critical risk factors associated with pressure injuries and demonstrate their behavioral patterns. The study focused on adult inpatients aged 18 or above who remained in bed for at least 24 hours after admission. The data was extracted from a nursing electronic medical record system of a hospital in Zhejiang Province, China, from 27 October 2019 to 1 November 2020. RESULTS The critical risk factors associated with pressure injuries in adult inpatients were identified as "Sensory perception," "Nutrition," and "Friction and shear." A prediction model with 89 decision rules was established and demonstrated reliable predictive capabilities. Nursing staff should focus more on high-risk and severe-risk rules (Rules 11 to 18) to reduce the likelihood of potential high-risk pressure injuries. CONCLUSIONS The prediction model established by the rough set approach can be used to identify the critical risk factors of pressure injuries and has good explanatory ability, which can complement and improve the predictive accuracy of the Braden Scale. The decision-making rules can help nurses improve work efficiency. IMPLICATIONS FOR CLINICAL PRACTICE Explanatory analysis can explain most inpatients' potential risk patterns and corresponding critical risk factors. Data-driven research models and results can help nurses understand patients' potential risks better. Additionally, these insights can be valuable in nursing education, aiding new nurses in comprehending and addressing the potential risks patients face.
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Affiliation(s)
- Yen-Ching Chuang
- Institute of Public Health & Emergency Management, Taizhou University, Taizhou 318000, Zhejiang, China; Business College, Taizhou University, Taizhou 318000, Zhejiang, China; Key Laboratory of Evidence-based Radiology of Taizhou, Linhai 317000, Zhejiang, China.
| | - Tao Miao
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, Zhejiang Province, China.
| | - Fengmin Cheng
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, Zhejiang Province, China.
| | - Yanjiao Wang
- Xiamen Cardiovascular Hospital, Xiamen University, Xiamen 361008, Fujian, China.
| | - Ching-Wen Chien
- Institute for Hospital Management, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China.
| | - Ping Tao
- Department of Medical Affairs & Planning, Kaohsiung Veterans General Hospital, Kaohsiung City, Taiwan.
| | - Linlin Kang
- Shenzhen Bao'an District Traditional Chinese Medicine Hospital, Shenzhen, China.
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de Marinis R, Marigi EM, Atwan Y, Yang L, Oeding JF, Gupta P, Pareek A, Sanchez-Sotelo J, Sperling JW. Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:447-453. [PMID: 37928999 PMCID: PMC10625013 DOI: 10.1016/j.xrrt.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Background Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications. Methods PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery. Results Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology. Conclusion AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.
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Affiliation(s)
- Rodrigo de Marinis
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Pontificia Universidad Católica de Chile, Santiago, Chile
- Shoulder and Elbow Unit, Hospital Dr. Sótero del Rio, Santiago, Chile
| | - Erick M. Marigi
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Yousif Atwan
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Linjun Yang
- Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, MN, USA
| | - Jacob F. Oeding
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | | | - John W. Sperling
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
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Toffaha KM, Simsekler MCE, Omar MA. Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review. Artif Intell Med 2023; 141:102560. [PMID: 37295900 DOI: 10.1016/j.artmed.2023.102560] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. OBJECTIVE This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. METHODS A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. RESULTS The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. CONCLUSIONS There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.
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Affiliation(s)
- Khaled M Toffaha
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mecit Can Emre Simsekler
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Mohammed Atif Omar
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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Dabas M, Schwartz D, Beeckman D, Gefen A. Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review. Adv Wound Care (New Rochelle) 2023; 12:205-240. [PMID: 35438547 DOI: 10.1089/wound.2021.0144] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Significance: As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at providing an in-depth overview of the relevant methodologies of artificial intelligence (AI) and their potential implementation to support these growing needs of wound care and management. Recent Advances: MEDLINE, Compendex, Scopus, Web of Science, and IEEE databases were all searched for new AI methods or novel uses of existing AI methods for the diagnosis or management of hard-to-heal wounds. We only included English peer-reviewed original articles, conference proceedings, published patent applications, or granted patents (not older than 2010) where the performance of the utilized AI algorithms was reported. Based on these criteria, a total of 75 studies were eligible for inclusion. These varied by the type of the utilized AI methodology, the wound type, the medical record/database configuration, and the research goal. Critical Issues: AI methodologies appear to have a strong positive impact and prospects in the wound care and management arena. Another important development that emerged from the findings is AI-based remote consultation systems utilizing smartphones and tablets for data collection and connectivity. Future Directions: The implementation of machine-learning algorithms in the diagnosis and managements of hard-to-heal wounds is a promising approach for improving the wound care delivered to hospitalized patients, while allowing health care professionals to manage their working time more efficiently.
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Affiliation(s)
- Mai Dabas
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Dafna Schwartz
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Dimitri Beeckman
- Skin Integrity Research Group (SKINT), University Centre for Nursing and Midwifery, Department of Public Health, Ghent University, Ghent, Belgium.,Swedish Centre for Skin and Wound Research, School of Health Sciences, Örebro University, Örebro, Sweden
| | - Amit Gefen
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,The Herbert J. Berman Chair in Vascular Bioengineering, Tel Aviv University, Tel Aviv, Israel
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Shi HY, Yeh SCJ, Chou HC, Wang WC. Long-term care insurance purchase decisions of registered nurses: Deep learning versus logistic regression models. Health Policy 2023; 129:104709. [PMID: 36725380 DOI: 10.1016/j.healthpol.2023.104709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/03/2022] [Accepted: 01/16/2023] [Indexed: 01/19/2023]
Abstract
OBJECTIVE The purpose of this study was to use a deep learning model and a traditional statistical regression model to predict the long-term care insurance decisions of registered nurses. METHODS We Prospectively surveyed 1,373 registered nurses with a minimum of 2 years of full-time working experience at a large medical center in Taiwan: 615 who already owned long-term care insurance (LTCI), 332 who had no intention to purchase LTCI (group 1), and 426 who intended to purchase LTCI (group 2). RESULTS After inverse probability of treatment weighting (IPTW), no statistically significant differences were identified in the study characteristics of the two groups. All the performance indices for the deep neural network (DNN) model were significantly higher than those of the multiple logistic regression (MLR) model (P<0.001). The strongest predictor of an individual's long-term care insurance decision was their risk propensity score, followed by their caregiving responsibilities, whether they live with older adult relatives, their experiences of catastrophic illness, and their openness to experience. CONCLUSIONS The DNN model is useful for predicting long-term care insurance decisions. Its prediction accuracy can be increased through training with temporal data collected from registered nurses. Future research can explore designs for two-level or multilevel models that explain the contextual effects of the risk factors on long-term care insurance decisions.
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Affiliation(s)
- Hon-Yi Shi
- Institute of Health Care Management and Department of Business Management, National Sun Yat-sen University, No.70 Lian Hai Road, Kaohsiung 80424, Taiwan; Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan; Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung, Taiwan; Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Shu-Chuan Jennifer Yeh
- Institute of Health Care Management and Department of Business Management, National Sun Yat-sen University, No.70 Lian Hai Road, Kaohsiung 80424, Taiwan; Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan.
| | - Hsueh-Chih Chou
- Institute of Health Care Management and Department of Business Management, National Sun Yat-sen University, No.70 Lian Hai Road, Kaohsiung 80424, Taiwan; Department of Nursing, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Wen Chun Wang
- Institute of Health Care Management and Department of Business Management, National Sun Yat-sen University, No.70 Lian Hai Road, Kaohsiung 80424, Taiwan
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Dweekat OY, Lam SS, McGrath L. Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:796. [PMID: 36613118 PMCID: PMC9819814 DOI: 10.3390/ijerph20010796] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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Qu C, Luo W, Zeng Z, Lin X, Gong X, Wang X, Zhang Y, Li Y. The predictive effect of different machine learning algorithms for pressure injuries in hospitalized patients: A network meta-analyses. Heliyon 2022; 8:e11361. [DOI: 10.1016/j.heliyon.2022.e11361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
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Vera-Salmerón E, Domínguez-Nogueira C, Romero-Béjar JL, Sáez JA, Mota-Romero E. Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191811161. [PMID: 36141434 PMCID: PMC9517564 DOI: 10.3390/ijerph191811161] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 06/02/2023]
Abstract
Applications where data mining tools are used in the fields of medicine and nursing are becoming more and more frequent. Among them, decision trees have been applied to different health data, such as those associated with pressure ulcers. Pressure ulcers represent a health problem with a significant impact on the morbidity and mortality of immobilized patients and on the quality of life of affected people and their families. Nurses provide comprehensive care to immobilized patients. This fact results in an increased workload that can be a risk factor for the development of serious health problems. Healthcare work with evidence-based practice with an objective criterion for a nursing professional is an essential addition for the application of preventive measures. In this work, two ways for conducting a pressure ulcer risk assessment based on a decision tree approach are provided. The first way is based on the activity and mobility characteristics of the Braden scale, whilst the second way is based on the activity, mobility and skin moisture characteristics. The results provided in this study endow nursing professionals with a foundation in relation to the use of their experience and objective criteria for quick decision making regarding the risk of a patient to develop a pressure ulcer.
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Affiliation(s)
- Eugenio Vera-Salmerón
- Servicio Andaluz de Salud, Distrito Sanitario Granada-Metropolitano, Centro de Salud Dr. Salvador Caballero de Granada, 18012 Granada, Spain
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
| | - Carmen Domínguez-Nogueira
- Inspección Provincial de Servicios Sanitarios, Delegación Territorial de Granada, Consejería de Salud y Familias de la Junta de Andalucía, 41071 Sevilla, Spain
| | - José L. Romero-Béjar
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain
- Institute of Mathematics, University of Granada (IMAG), Ventanilla 11, 18001 Granada, Spain
| | - José A. Sáez
- Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain
| | - Emilio Mota-Romero
- Servicio Andaluz de Salud, Distrito Sanitario Granada-Metropolitano, Centro de Salud Dr. Salvador Caballero de Granada, 18012 Granada, Spain
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
- Department of Nursing, University of Granada, Avda. Ilustración 60, 18071 Granada, Spain
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11
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Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39:120. [PMID: 35704152 PMCID: PMC9198206 DOI: 10.1007/s12032-022-01711-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 10/28/2022]
Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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13
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Kim SY, Kim HJ, An JW, Lee Y, Shin YS. Effects of alternating pressure air mattresses on pressure injury prevention: A systematic review of randomized controlled trials. Worldviews Evid Based Nurs 2022; 19:94-99. [PMID: 35229980 DOI: 10.1111/wvn.12570] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/25/2021] [Accepted: 10/10/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Pressure injury (PI) is a significant health problem among inpatients that affects their health, quality of life, and expenses. AIM This systematic review aimed to compare effects of alternating pressure air mattresses (APMs) with other types of supporting surfaces as a tool for PI prevention. METHODS The literature published between 2009 and 2020 was searched using the databases PubMed, EMBASE, CINAHL, and Cochrane. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses process was followed, including independent study selection and data extraction. Quality appraisal was conducted using the Cochrane Risk of Bias Tool (RoB 2.0). RESULTS A total of six randomized controlled trials (RCTs) were analyzed. The incidence of hospital-acquired PIs at stage 1 or higher was reported in the APM group from 0.3% to 25%. In one study, APMs were found to be less effective than static air mattresses (SAMs); in contrast, two studies found no difference. In one study, the APM was reported to be more effective than the viscoelastic foam mattress (VFM). On the contrary, in a more recent study, the APM was reported to be less effective than the VFM, and there was no difference compared with high-specification foam mattresses in another study. Using the RoB 2.0 tool, one study was evaluated at "low risk of bias," another as "some concern," and four as "high risk." LINKING EVIDENCE TO ACTION There is insufficient evidence to suggest that APM is more effective in preventing PIs than other supporting surfaces. Evidence to date suggests that APM can be used in patients at risk for PIs. It is important to change position regardless of the type of support surface used. Highly controlled RCTs with low risk of bias are needed to provide strong evidence for identifying the most effective PI prevention support surfaces.
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Affiliation(s)
- Soo-Yeon Kim
- Department of Nursing, Daegu Haany University, Gyeongsangbuk-do, Korea
| | - Hyun-Jung Kim
- Department of Nursing, Asan Medical Center, Seoul, Korea
| | - Ji-Won An
- Department of Nursing Science, Far East University, Chungcheongbuk-do, Korea
| | - Yoonyoung Lee
- Department of Nursing Science, Sunchon National University, Jeollanam-do, Korea
| | - Yong-Soon Shin
- School of Nursing, Research Institute of Nursing Science, Hanyang University, Seoul, Korea
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14
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Luo J, Zhang W, Tan S, Liu L, Bai Y, Zhang G. Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning. Front Cardiovasc Med 2022; 8:777757. [PMID: 35004892 PMCID: PMC8733407 DOI: 10.3389/fcvm.2021.777757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can hardly offer practical help to patients. Meanwhile, the artificial intelligence technology may provide a cheap but effective approach to building an auxiliary diagnosis model for improving the early AD diagnosis rate by taking advantage of the data of the general conditions of AD patients, such as the data about the basic inspection information. Therefore, this study proposes to hybrid five types of machine learning operators into an integrated diagnosis model, as an auxiliary diagnostic approach, to cooperate with the AD-clinical analysis. To improve the diagnose accuracy, the participating rate of each operator in the proposed model may adjust adaptively according to the result of the data learning. After a set of experimental evaluations, the proposed model, acting as the preliminary AD-discriminant, has reached an accuracy of over 80%, which provides a promising instance for medical colleagues.
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Affiliation(s)
- Jingmin Luo
- Xiangya Hospital of Central South University, Changsha, China
| | - Wei Zhang
- Xiangya Hospital of Central South University, Changsha, China
| | - Shiyang Tan
- Information Science and Engineering School of Central South University, Changsha, China
| | - Lijue Liu
- Information Science and Engineering School of Central South University, Changsha, China
| | - Yongping Bai
- Xiangya Hospital of Central South University, Changsha, China
| | - Guogang Zhang
- Third Xiangya Hospital of Central South University, Changsha, China
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15
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The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework. SUSTAINABILITY 2021. [DOI: 10.3390/su132111587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Big data has been prominent in studying aging and older people’s health. It has promoted modeling and analyses in biological and geriatric research (like cellular senescence), developed health management platforms, and supported decision-making in public healthcare and social security. However, current studies are still limited within a single subject, rather than flourished as interdisciplinary research in the context of big data. The research perspectives have not changed, nor has big data brought itself out of the role as a modeling tool. When embedding big data as a data product, analysis tool, and resolution service into different spatial, temporal, and organizational scales of aging processes, it would present as a connection, integration, and interaction simultaneously in conducting interdisciplinary research. Therefore, this paper attempts to propose an ecological framework for big data based on aging and older people’s health research. Following the scoping process of PRISMA, 35 studies were reviewed to validate our ecological framework. Although restricted by issues like digital divides and privacy security, we encourage researchers to capture various elements and their interactions in the human-environment system from a macro and dynamic perspective rather than simply pursuing accuracy.
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16
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Song J, Gao Y, Yin P, Li Y, Li Y, Zhang J, Su Q, Fu X, Pi H. The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms. Risk Manag Healthc Policy 2021; 14:1175-1187. [PMID: 33776495 PMCID: PMC7987326 DOI: 10.2147/rmhp.s297838] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/26/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose Build machine learning models for predicting pressure ulcer nursing adverse event, and find an optimal model that predicts the occurrence of pressure ulcer accurately. Patients and Methods Retrospectively enrolled 5814 patients, of which 1673 suffer from pressure ulcer events. Support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN) models were used to construct the pressure ulcer prediction models, respectively. A total of 19 variables are included, and the importance of screening variables is evaluated. Meanwhile, the performance of the prediction models is evaluated and compared. Results The experimental results show that the four pressure ulcer prediction models all achieve good performance. Also, the AUC values of the four models are all greater than 0.95. Besides, the comparison of the four models indicates that RF model achieves a higher accuracy for the prediction of pressure ulcer. Conclusion This research verifies the feasibility of developing a management system for predicting nursing adverse event based on big data technology and machine learning technology. The random forest and decision tree model are more suitable for constructing a pressure ulcer prediction model. This study provides a reference for future pressure ulcer risk warning based on big data.
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Affiliation(s)
- Jie Song
- Medical School of Chinese PLA, Beijing, People's Republic of China
| | - Yuan Gao
- First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Pengbin Yin
- Fouth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Yi Li
- Medical School of Chinese PLA, Beijing, People's Republic of China
| | - Yang Li
- First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Jie Zhang
- Sixth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Qingqing Su
- Medical School of Chinese PLA, Beijing, People's Republic of China
| | - Xiaojie Fu
- First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Hongying Pi
- Medical Service Training Center, Chinese PLA General Hospital, Beijing, People's Republic of China
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17
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Jiang M, Ma Y, Guo S, Jin L, Lv L, Han L, An N. Using Machine Learning Technologies in Pressure Injury Management: Systematic Review. JMIR Med Inform 2021; 9:e25704. [PMID: 33688846 PMCID: PMC7991995 DOI: 10.2196/25704] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/21/2021] [Accepted: 02/05/2021] [Indexed: 12/24/2022] Open
Abstract
Background Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.
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Affiliation(s)
- Mengyao Jiang
- Evidence-based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Yuxia Ma
- Evidence-based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Siyi Guo
- Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education, School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
| | - Liuqi Jin
- Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education, School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
| | - Lin Lv
- Wound and Ostomy Center, Outpatient Department, Gansu Provincial Hospital, Lanzhou, China
| | - Lin Han
- Evidence-based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China.,Department of Nursing, Gansu Provincial Hospital, Lanzhou, China
| | - Ning An
- Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education, School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
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18
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Caruso R, Arrigoni C, Conte G, Rocco G, Dellafiore F, Ambrogi F, Stievano A. The Byzantine Role of Big Data Application in Nursing Science: A Systematic Review. Comput Inform Nurs 2020; 39:178-186. [PMID: 32868528 DOI: 10.1097/cin.0000000000000673] [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: 11/26/2022]
Abstract
Big data have the potential to determine enhanced decision-making process and to personalize the approach of delivering care when applied in nursing science. So far, the literature on this topic is still not synthesized for the period between 2014 and 2018. Thus, this systematic review aimed to identify and synthesize the most recent evidence on big data application in nursing research. The systematic search was undertaken for the evidence published from January 2014 to May 2018, and the outputs were formatted using the PRISMA Flow Diagram, whereas the quality appraisal was addressed by recommendations consistent with the Critical Appraisal Skills Program. Twelve studies on big data in nursing were included and divided into two themes: the majority of the studies aimed to determine prediction assessment, while only four studies were related to the impact of big data applications to support clinical practice. This review tracks the recent state of knowledge on big data applications in nursing science, revealing the potential for nursing engagement in big data science, even if currently limited to some fields. Big data applications in nursing might have a tremendous potential impact, but are currently underused in research and clinical practice.
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Affiliation(s)
- Rosario Caruso
- Author Affiliations: Health Professions Research and Development Unit, IRCCS Policlinico San Donato (Drs Caruso and Dellafiore); Department of Public Health, Experimental and Forensic Medicine, Section of Hygiene, University of Pavia (Ms Arrigoni); Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan (Mr Conte); Center for Excellence in Nursing Scholarship, OPI, Rome (Drs Rocco and Stievano); and Department of Clinical Sciences and Community Health, University of Milan (Dr Ambrogi), Italy; and Nursing and Health Policy, International Council of Nurses, Geneva, Switzerland (Dr Stievano)
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19
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Omissions of Care in Nursing Home Settings: A Narrative Review. J Am Med Dir Assoc 2020; 21:604-614.e6. [DOI: 10.1016/j.jamda.2020.02.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/11/2020] [Accepted: 02/19/2020] [Indexed: 02/06/2023]
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20
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Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020; 2020:baaa010. [PMID: 32185396 PMCID: PMC7078068 DOI: 10.1093/database/baaa010] [Citation(s) in RCA: 167] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.
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Affiliation(s)
- Zeeshan Ahmed
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, 67 North Eagleville Road, Storrs, CT, USA
| | - Khalid Mohamed
- Department of Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT, USA
| | - Saman Zeeshan
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - XinQi Dong
- Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson Street, New Brunswick, NJ, USA
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21
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Woods JS, Saxena M, Nagamine T, Howell RS, Criscitelli T, Gorenstein S, M Gillette B. The Future of Data-Driven Wound Care. AORN J 2019; 107:455-463. [PMID: 29595902 DOI: 10.1002/aorn.12102] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence-based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care.
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22
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Kuo CY, Yu LC, Chen HC, Chan CL. Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms. Healthc Inform Res 2018; 24:29-37. [PMID: 29503750 PMCID: PMC5820083 DOI: 10.4258/hir.2018.24.1.29] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 01/16/2018] [Accepted: 01/22/2018] [Indexed: 12/22/2022] Open
Abstract
Objectives The aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion. Methods A data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1. Results Five hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. Conclusions Our study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.
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Affiliation(s)
- Ching-Yen Kuo
- Institute of Information Management, Yuan-Ze University, Taoyuan, Taiwan.,Department of Medical Administration, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Liang-Chin Yu
- Institute of Information Management, Yuan-Ze University, Taoyuan, Taiwan
| | - Hou-Chaung Chen
- Department of Orthopedics, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Chien-Lung Chan
- Institute of Information Management, Yuan-Ze University, Taoyuan, Taiwan.,Innovation Center for Big Data and Digital Convergence, Yuan-Ze University, Taoyuan, Taiwan
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Kim YM, Kathuria P, Delen D. Machine Learning to Compare Frequent Medical Problems of African American and Caucasian Diabetic Kidney Patients. Healthc Inform Res 2017; 23:241-248. [PMID: 29181232 PMCID: PMC5688022 DOI: 10.4258/hir.2017.23.4.241] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 10/20/2017] [Accepted: 10/20/2017] [Indexed: 12/29/2022] Open
Abstract
Objectives End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an exemplary health disparity between African American and Caucasian patients in the United States. Because diabetic chronic kidney disease (CKD) patients of these two groups show differences in their medical problems, the markers leading to ESRD are also expected to differ. The purpose of this study was, therefore, to compare their medical complications at various levels of kidney function and to identify markers that can be used to predict ESRD. Methods The data of type 2 diabetic patients was obtained from the 2012 Cerner database, which totaled 1,038,499 records. The data was then filtered to include only African American and Caucasian outpatients with estimated glomerular filtration rates (eGFR), leaving 4,623 records. A priori machine learning was used to discover frequently appearing medical problems within the filtered data. CKD is defined as abnormalities of kidney structure, present for >3 months. Results This study found that African Americans have much higher rates of CKD-related medical problems than Caucasians for all five stages, and prominent markers leading to ESRD were discovered only for the African American group. These markers are high glucose, high systolic blood pressure (BP), obesity, alcohol/drug use, and low hematocrit. Additionally, the roles of systolic BP and diastolic BP vary depending on the CKD stage. Conclusions This research discovered frequently appearing medical problems across five stages of CKD and further showed that many of the markers reported in previous studies are more applicable to African American patients than Caucasian patients.
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Affiliation(s)
- Yong-Mi Kim
- School of Library and Information Studies, University of Oklahoma, Tulsa, OK, USA
| | - Pranay Kathuria
- Division of Nephrology and Hypertension, Department of Medicine, School of Community Medicine, University of Oklahoma, Tulsa, OK, USA
| | - Dursun Delen
- Spears School of Business, Oklahoma State University, Tulsa, OK, USA
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