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Ricciardi C, Ponsiglione AM, Scala A, Borrelli A, Misasi M, Romano G, Russo G, Triassi M, Improta G. Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture. Bioengineering (Basel) 2022; 9:bioengineering9040172. [PMID: 35447732 PMCID: PMC9029792 DOI: 10.3390/bioengineering9040172] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 12/27/2022] Open
Abstract
Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R2) was achieved by the support vector machine (R2 = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.
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Affiliation(s)
- Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy;
| | - Alfonso Maria Ponsiglione
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy;
- Correspondence:
| | - Arianna Scala
- Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (M.T.); (G.I.)
| | - Anna Borrelli
- Health Department, University Hospital of Salerno “San Giovanni di Dio e Ruggi d′Aragona”, 84126 Salerno, Italy;
| | - Mario Misasi
- Department of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy; (M.M.); (G.R.)
| | - Gaetano Romano
- Department of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy; (M.M.); (G.R.)
| | - Giuseppe Russo
- National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy;
| | - Maria Triassi
- Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (M.T.); (G.I.)
- Interdepartmental Center for Research in Healthcare, Management and Innovation in Healthcare (CIRMIS), University of Study of Naples “Federico II”, 80131 Naples, Italy
| | - Giovanni Improta
- Department of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, Italy; (A.S.); (M.T.); (G.I.)
- Interdepartmental Center for Research in Healthcare, Management and Innovation in Healthcare (CIRMIS), University of Study of Naples “Federico II”, 80131 Naples, Italy
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Hatef E, Rouhizadeh M, Tia I, Lasser E, Hill-Briggs F, Marsteller J, Kharrazi H. Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System. JMIR Med Inform 2019; 7:e13802. [PMID: 31376277 PMCID: PMC6696855 DOI: 10.2196/13802] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/03/2019] [Accepted: 05/30/2019] [Indexed: 02/02/2023] Open
Abstract
Background Most US health care providers have adopted electronic health records (EHRs) that facilitate the uniform collection of clinical information. However, standardized data formats to capture social and behavioral determinants of health (SBDH) in structured EHR fields are still evolving and not adopted widely. Consequently, at the point of care, SBDH data are often documented within unstructured EHR fields that require time-consuming and subjective methods to retrieve. Meanwhile, collecting SBDH data using traditional surveys on a large sample of patients is infeasible for health care providers attempting to rapidly incorporate SBDH data in their population health management efforts. A potential approach to facilitate targeted SBDH data collection is applying information extraction methods to EHR data to prescreen the population for identification of immediate social needs. Objective Our aim was to examine the availability and characteristics of SBDH data captured in the EHR of a multilevel academic health care system that provides both inpatient and outpatient care to patients with varying SBDH across Maryland. Methods We measured the availability of selected patient-level SBDH in both structured and unstructured EHR data. We assessed various SBDH including demographics, preferred language, alcohol use, smoking status, social connection and/or isolation, housing issues, financial resource strains, and availability of a home address. EHR’s structured data were represented by information collected between January 2003 and June 2018 from 5,401,324 patients. EHR’s unstructured data represented information captured for 1,188,202 patients between July 2016 and May 2018 (a shorter time frame because of limited availability of consistent unstructured data). We used text-mining techniques to extract a subset of SBDH factors from EHR’s unstructured data. Results We identified a valid address or zip code for 5.2 million (95.00%) of approximately 5.4 million patients. Ethnicity was captured for 2.7 million (50.00%), whereas race was documented for 4.9 million (90.00%) and a preferred language for 2.7 million (49.00%) patients. Information regarding alcohol use and smoking status was coded for 490,348 (9.08%) and 1,728,749 (32.01%) patients, respectively. Using the International Classification of Diseases–10th Revision diagnoses codes, we identified 35,171 (0.65%) patients with information related to social connection/isolation, 10,433 (0.19%) patients with housing issues, and 3543 (0.07%) patients with income/financial resource strain. Of approximately 1.2 million unique patients with unstructured data, 30,893 (2.60%) had at least one clinical note containing phrases referring to social connection/isolation, 35,646 (3.00%) included housing issues, and 11,882 (1.00%) had mentions of financial resource strain. Conclusions Apart from demographics, SBDH data are not regularly collected for patients. Health care providers should assess the availability and characteristics of SBDH data in EHRs. Evaluating the quality of SBDH data can potentially enable health care providers to modify underlying workflows to improve the documentation, collection, and extraction of SBDH data from EHRs.
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Affiliation(s)
- Elham Hatef
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Johns Hopkins Center for Health Disparities Solutions, Baltimore, MD, United States
| | - Masoud Rouhizadeh
- Center for Clinical Data Analysis, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Iddrisu Tia
- Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Elyse Lasser
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Felicia Hill-Briggs
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Department of Acute and Chronic Care, Johns Hopkins School of Nursing, Baltimore, MD, United States.,Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.,Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Jill Marsteller
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.,Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Center for Health Services and Outcomes Research, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Hadi Kharrazi
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Center for Health Services and Outcomes Research, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, United States
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Mena LJ, Felix VG, Ostos R, Gonzalez JA, Cervantes A, Ochoa A, Ruiz C, Ramos R, Maestre GE. Mobile personal health system for ambulatory blood pressure monitoring. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:598196. [PMID: 23762189 PMCID: PMC3665224 DOI: 10.1155/2013/598196] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2012] [Accepted: 04/12/2013] [Indexed: 01/12/2023]
Abstract
The ARVmobile v1.0 is a multiplatform mobile personal health monitor (PHM) application for ambulatory blood pressure (ABP) monitoring that has the potential to aid in the acquisition and analysis of detailed profile of ABP and heart rate (HR), improve the early detection and intervention of hypertension, and detect potential abnormal BP and HR levels for timely medical feedback. The PHM system consisted of ABP sensor to detect BP and HR signals and smartphone as receiver to collect the transmitted digital data and process them to provide immediate personalized information to the user. Android and Blackberry platforms were developed to detect and alert of potential abnormal values, offer friendly graphical user interface for elderly people, and provide feedback to professional healthcare providers via e-mail. ABP data were obtained from twenty-one healthy individuals (>51 years) to test the utility of the PHM application. The ARVmobile v1.0 was able to reliably receive and process the ABP readings from the volunteers. The preliminary results demonstrate that the ARVmobile 1.0 application could be used to perform a detailed profile of ABP and HR in an ordinary daily life environment, bedsides of estimating potential diagnostic thresholds of abnormal BP variability measured as average real variability.
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Affiliation(s)
- Luis J Mena
- Department of Computer Engineering, Polytechnic University of Sinaloa, 82199 Mazatlan, SIN, Mexico.
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