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González-Castro A, Leirós-Rodríguez R, Prada-García C, Benítez-Andrades JA. The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review. J Med Internet Res 2024; 26:e54934. [PMID: 38684088 DOI: 10.2196/54934] [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: 11/28/2023] [Revised: 01/30/2024] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. OBJECTIVE The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. METHODS A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. RESULTS We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. CONCLUSIONS The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. TRIAL REGISTRATION PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.
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Affiliation(s)
- Ana González-Castro
- Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain
| | - Raquel Leirós-Rodríguez
- SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain
| | - Camino Prada-García
- Department of Preventive Medicine and Public Health, Universidad de Valladolid, Valladolid, Spain
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Wabe N, Meulenbroeks I, Huang G, Silva SM, Gray LC, Close JCT, Lord S, Westbrook JI. Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: a landmarking approach. J Am Med Inform Assoc 2024; 31:1113-1125. [PMID: 38531675 PMCID: PMC11031240 DOI: 10.1093/jamia/ocae058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 02/28/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
OBJECTIVES Falls pose a significant challenge in residential aged care facilities (RACFs). Existing falls prediction tools perform poorly and fail to capture evolving risk factors. We aimed to develop and internally validate dynamic fall risk prediction models and create point-based scoring systems for residents with and without dementia. MATERIALS AND METHODS A longitudinal cohort study using electronic data from 27 RACFs in Sydney, Australia. The study included 5492 permanent residents, with a 70%-30% split for training and validation. The outcome measure was the incidence of falls. We tracked residents for 60 months, using monthly landmarks with 1-month prediction windows. We employed landmarking dynamic prediction for model development, a time-dependent area under receiver operating characteristics curve (AUROCC) for model evaluations, and a regression coefficient approach to create point-based scoring systems. RESULTS The model identified 15 independent predictors of falls in dementia and 12 in nondementia cohorts. Falls history was the key predictor of subsequent falls in both dementia (HR 4.75, 95% CI, 4.45-5.06) and nondementia cohorts (HR 4.20, 95% CI, 3.87-4.57). The AUROCC across landmarks ranged from 0.67 to 0.87 for dementia and from 0.66 to 0.86 for nondementia cohorts but generally remained between 0.75 and 0.85 in both cohorts. The total point risk score ranged from -2 to 57 for dementia and 0 to 52 for nondementia cohorts. DISCUSSION Our novel risk prediction models and scoring systems provide timely person-centered information for continuous monitoring of fall risk in RACFs. CONCLUSION Embedding these tools within electronic health records could facilitate the implementation of targeted proactive interventions to prevent falls.
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Affiliation(s)
- Nasir Wabe
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW 2109, Australia
| | - Isabelle Meulenbroeks
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW 2109, Australia
| | - Guogui Huang
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW 2109, Australia
| | - Sandun Malpriya Silva
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW 2109, Australia
| | - Leonard C Gray
- Centre for Health Service Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jacqueline C T Close
- Neuroscience Research Australia, University of New South Wales, Sydney, NSW 2052, Australia
- School of Clinical Medicine, University of New South Wales, Sydney, NSW 2052, Australia
| | - Stephen Lord
- Neuroscience Research Australia, University of New South Wales, Sydney, NSW 2052, Australia
- School of Population Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Johanna I Westbrook
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW 2109, Australia
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Song W, Latham NK, Liu L, Rice HE, Sainlaire M, Min L, Zhang L, Thai T, Kang MJ, Li S, Tejeda C, Lipsitz S, Samal L, Carroll DL, Adkison L, Herlihy L, Ryan V, Bates DW, Dykes PC. Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach. J Am Geriatr Soc 2024; 72:1145-1154. [PMID: 38217355 PMCID: PMC11018490 DOI: 10.1111/jgs.18776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/21/2023] [Accepted: 12/22/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires. METHODS Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors. RESULTS Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models. CONCLUSIONS The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.
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Affiliation(s)
- Wenyu Song
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Nancy K Latham
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Luwei Liu
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Hannah E Rice
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Michael Sainlaire
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Lillian Min
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Linying Zhang
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Tien Thai
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Min-Jeoung Kang
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Siyun Li
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Christian Tejeda
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Stuart Lipsitz
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Lipika Samal
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Diane L Carroll
- Yvonne L. Munn Center for Nursing Research, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lesley Adkison
- Department of Nursing and Patient Care Services, Newton Wellesley Hospital, Newton, Massachusetts, USA
| | - Lisa Herlihy
- Division of Nursing, Salem Hospital, Salem, Massachusetts, USA
| | - Virginia Ryan
- Division of Nursing, Brigham and Women's Faulkner Hospital, Jamaica Plain, Massachusetts, USA
| | - David W Bates
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Patricia C Dykes
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Rein DB, Hackney ME, Haddad YK, Sublett FA, Moreland B, Imhof L, Peterson C, Legha JK, Mark J, Vaughan CP, Johnson Ii TM, Bergen G. Telemedicine-Based Risk Program to Prevent Falls Among Older Adults: Protocol for a Randomized Quality Improvement Trial. JMIR Res Protoc 2024; 13:e54395. [PMID: 38346180 PMCID: PMC11005432 DOI: 10.2196/54395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND The Center for Disease Control and Prevention's Stopping Elderly Accidents, Deaths, and Injuries (STEADI) initiative offers health care providers tools and resources to assist with fall risk screening and multifactorial fall risk assessment and interventions. Its effectiveness has never been evaluated in a randomized trial. OBJECTIVE This study aims to describe the protocol for the STEADI Options Randomized Quality Improvement Trial (RQIT), which was designed to evaluate the impact on falls and all-cause health expenditures of a telemedicine-based form of STEADI implemented among older adults aged 65 years and older, within a primary care setting. METHODS STEADI Options was a pragmatic RQIT implemented within a health system comparing a telemedicine version of the STEADI fall risk assessment to the standard of care (SOC). Before screening, we randomized all eligible patients in participating clinics into the STEADI arm or SOC arm based on their scheduled provider. All received the Stay Independent screener (SIS) to determine fall risk. Patients were considered at risk for falls if they scored 4 or more on the SIS or answered affirmatively to any 1 of the 3 key questions within the SIS. Patients screened at risk for falls and randomized to the STEADI arm were offered a registered nurse (RN)-led STEADI assessment through telemedicine; the RN provided assessment results and recommendations to the providers, who were advised to discuss fall-prevention strategies with their patients. Patients screened at risk for falls and randomized to the SOC arm were asked to participate in study data collection only. Data on recruitment, STEADI assessments, use of recommended prevention services, medications, and fall occurrences were collected using electronic health records and patient surveys. Using staff time diaries and administrative records, the study prospectively collected data on STEADI implementation costs and all-cause outpatient and inpatient charges incurred over the year following enrollment. RESULTS The study enrolled 720 patients (n=307, 42.6% STEADI arm; n=353, 49% SOC arm; and n=60, 8.3% discontinued arm) from September 2020 to December 2021. Follow-up data collection was completed in January 2023. As of February 2024, data analysis is complete, and results are expected to be published by the end of 2025. CONCLUSIONS The STEADI RQIT evaluates the impact of a telemedicine-based, STEADI-based fall risk assessment on falls and all-cause health expenditures and can provide information on the intervention's effectiveness and cost-effectiveness. TRIAL REGISTRATION ClinicalTrials.gov NCT05390736, http://clinicaltrials.gov/ct2/show/NCT05390736. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR1-10.2196/54395.
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Affiliation(s)
- David B Rein
- Department of Public Health, NORC at the University of Chicago, Atlanta, GA, United States
| | - Madeleine E Hackney
- Department of Medicine, Division of Geriatrics and Gerontology, Emory University School of Medicine, Atlanta, GA, United States
- Atlanta Veterans Affairs Center for Visual & Neurocognitive Rehabilitation, Atlanta, GA, United States
- Birmingham/Atlanta VA Geriatric Research Education Clinical Center, Atlanta, GA, United States
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Yara K Haddad
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Farah A Sublett
- Department of Public Health, NORC at the University of Chicago, Atlanta, GA, United States
| | - Briana Moreland
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Laurie Imhof
- Department of Health Sciences, NORC at the University of Chicago, Chicago, IL, United States
| | - Cora Peterson
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Jaswinder K Legha
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Janice Mark
- School of Nursing and Health Professions, University of San Francisco, San Francisco, CA, United States
| | - Camille P Vaughan
- Department of Medicine, Division of Geriatrics and Gerontology, Emory University School of Medicine, Atlanta, GA, United States
- Atlanta Veterans Affairs Center for Visual & Neurocognitive Rehabilitation, Atlanta, GA, United States
- Birmingham/Atlanta VA Geriatric Research Education Clinical Center, Atlanta, GA, United States
| | - Theodore M Johnson Ii
- Birmingham/Atlanta VA Geriatric Research Education Clinical Center, Atlanta, GA, United States
- Department of Family and Preventive Medicine, Emory University School of Medicine, Atlanta, GA, United States
- Department of Medicine, Division of General Internal Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Gwen Bergen
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
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Li C, Li Z, Huang S, Chen X, Zhang T, Zhu J. Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries. Comput Inform Nurs 2024:00024665-990000000-00176. [PMID: 38453534 DOI: 10.1097/cin.0000000000001110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences.
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Affiliation(s)
- Canping Li
- Author Affiliations: Departments of Day Surgery (Mrs C. Mr Li, Dr Huang, Mrs Chen, Mrs Zhang), Medical Information Center (Mr Z. Li), and Nursing (Mrs Zhu), Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Heo KN, Seok JY, Ah YM, Kim KI, Lee SB, Lee JY. Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population. BMC Geriatr 2023; 23:830. [PMID: 38082380 PMCID: PMC10712099 DOI: 10.1186/s12877-023-04523-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors. METHODS Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference. RESULTS In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition. CONCLUSION We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.
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Affiliation(s)
- Kyu-Nam Heo
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Jeong Yeon Seok
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Young-Mi Ah
- College of Pharmacy, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea
| | - Kwang-Il Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Dalgubeol-Daero 1095, Dalseo-Gu, Daegu, 42601, Republic of Korea.
| | - Ju-Yeun Lee
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
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Lanotte F, O’Brien MK, Jayaraman A. AI in Rehabilitation Medicine: Opportunities and Challenges. Ann Rehabil Med 2023; 47:444-458. [PMID: 38093518 PMCID: PMC10767220 DOI: 10.5535/arm.23131] [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: 09/18/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.
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Affiliation(s)
- Francesco Lanotte
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Megan K. O’Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
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Yu Q, Li Z, Yang C, Zhang L, Xing M, Li W. Predicting functional dependency using machine learning among a middle-aged and older Chinese population. Arch Gerontol Geriatr 2023; 115:105124. [PMID: 37454417 DOI: 10.1016/j.archger.2023.105124] [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: 05/21/2023] [Revised: 07/02/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE To develop prediction models for assessing functional dependency in a middle-aged and older Chinese population. METHOD Adults ≥45 years old from the China Health and Retirement Longitudinal Study (CHARLS) and without functional dependency at baseline were included. Functional dependency was defined as needing any help in any basic activities of daily living (ADL) or instrumental activities of daily living (IADL). The outcomes were overall functional dependency, ADL and IADL dependency. Stacked ensemble models were constructed based on five selected machine learning models. Models were trained and tested in the 2011-2015 cohort, and were externally validated in the 2015-2018 cohort. SHapley Additive exPlanations (SHAP) was utilized to quantify the significance of predictors. RESULT In the training cohort, a total of 6,297 participants were included at baseline, 1,893 developed functional dependency during the follow-up period. The stacked ensemble model achieved the best performance in terms of discrimination ability for predicting overall functional dependency, ADL and IADL dependency, with AUCs of 0.750, 0.690 and 0.748, respectively; in external validation cohort, the corresponding AUCs were 0.725, 0.719 and 0.727, respectively. A compact model was further developed and maintained similar predictive performance. CONCLUSION The stacked ensemble approach can serve as a useful tool for identifying the risk of functional dependency in a large Chinese population. For ADL dependency, arthritis, age, self-report health, and waist circumference were identified as highly significant predictors. Conversely, cognitive function, age, living in rural areas, and performance in chair stand test emerged as highly ranked predictors for IADL dependency.
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Affiliation(s)
- Qi Yu
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zihan Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chenyu Yang
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lingzhi Zhang
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Muqi Xing
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Zapata RD, Huang S, Morris E, Wang C, Harle C, Magoc T, Mardini M, Loftus T, Modave F. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records. PLoS One 2023; 18:e0292888. [PMID: 37862334 PMCID: PMC10588875 DOI: 10.1371/journal.pone.0292888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 09/30/2023] [Indexed: 10/22/2023] Open
Abstract
OBJECTIVE This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home. METHODS We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.8%) female patients with a mean (SD) age of 51.86 (18.49) years and 618 (39.2%) male patients with a mean (SD) age of 54.35 (18.48) years. Machine learning (ML) model training involved cross-validation to assess their performance in predicting patient disposition. RESULTS We developed and validated six supervised ML-based prediction models (logistic regression, Gaussian Naïve Bayes, k-nearest neighbors, decision trees, random forest, and support vector machine classifier) to predict patient discharge status. The models were evaluated based on the area under the receiver operating characteristic curve (ROC-AUC), precision, accuracy, F1 score, and Brier score. The random forest classifier exhibited the highest performance, achieving an accuracy of 0.84 and an AUC of 0.72. Logistic regression (accuracy: 0.85, AUC: 0.71), k-nearest neighbor (accuracy: 0.84, AUC: 0.63), decision tree (accuracy: 0.84, AUC: 0.61), Gaussian Naïve Bayes (accuracy: 0.84, AUC: 0.66), and support vector machine classifier (accuracy: 0.84, AUC: 0.67) also demonstrated valuable predictive capabilities. SIGNIFICANCE This study's findings are crucial for efficiently allocating healthcare resources during pandemics like COVID-19. By harnessing ML techniques and EHR data, we can create predictive tools to identify patients at greater risk of severe symptoms based on their medical histories. The models developed here serve as a foundation for expanding the toolkit available to healthcare professionals and organizations. Additionally, explainable ML methods, such as Shapley Additive Explanations, aid in uncovering underlying data features that inform healthcare decision-making processes.
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Affiliation(s)
- Ruben D. Zapata
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Shu Huang
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, United States of America
| | - Earl Morris
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, United States of America
| | - Chang Wang
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Christopher Harle
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
- Clinical and Translational Science Institute, University of Florida, Gainesville, FL, United States of America
| | - Tanja Magoc
- Clinical and Translational Science Institute, University of Florida, Gainesville, FL, United States of America
| | - Mamoun Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Tyler Loftus
- Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - François Modave
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States of America
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10
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Uematsu T, Kawakami Y, Nojiri S, Saito T, Irie Y, Kasai T, Hiratsuka Y, Ishijima M, Kuroki M, Daida H, Nishizaki Y. Association between number of medications and hip fractures in Japanese elderly using conditional logistic LASSO regression. Sci Rep 2023; 13:16831. [PMID: 37803071 PMCID: PMC10558461 DOI: 10.1038/s41598-023-43876-3] [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: 02/08/2023] [Accepted: 09/29/2023] [Indexed: 10/08/2023] Open
Abstract
To examine the association between hip fracture and associated factors, including polypharmacy, and develop an optimal predictive model, we conducted a population-based matched case-control study using the health insurance claims data on hip fracture among Japanese patients. We included 34,717 hospitalized Japanese patients aged ≥ 65 years with hip fracture and 34,717 age- and sex- matched controls who were matched 1:1. This study included 69,434 participants. Overall, 16 variable comorbidities and 60 variable concomitant medications were used as explanatory variables. The participants were added to early elderly and late elderly categories for further analysis. The odds ratio of hip fracture increased with the number of medications only in the early elderly. AUC was highest for early elderly (AUC, 0.74, 95% CI 0.72-0.76). Use of anti-Parkinson's drugs had the largest coefficient and was the most influential variable in many categories. This study confirmed the association between risk factors, including polypharmacy and hip fracture. The risk of hip fracture increased with an increase in medication number taken by the early elderly and showed good predictive accuracy, whereas there was no such association in the late elderly. Therefore, the early elderly in Japan should be an active target population for hip fracture prevention.
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Affiliation(s)
- Takuya Uematsu
- Clinical Translational Science, Juntendo University School of Medicine Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
- Department of Hospital Pharmacy, Juntendo University Hospital, Tokyo, Japan
| | - Yuta Kawakami
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
- Graduate School of Engineering Science, Yokohama National University, Kanagawa, Japan
| | - Shuko Nojiri
- Clinical Translational Science, Juntendo University School of Medicine Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan.
| | - Tomoyuki Saito
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Yoshiki Irie
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
- Graduate School of Engineering Science, Tokyo University of Science, Tokyo, Japan
| | - Takatoshi Kasai
- Department of Cardiology, Juntendo University School of Medicine Graduate School of Medicine, Tokyo, Japan
| | - Yoshimune Hiratsuka
- Department of Ophthalmology, Juntendo University School of Medicine Graduate School of Medicine, Tokyo, Japan
| | - Muneaki Ishijima
- Department of Medicine for Orthopedics and Motor Organ, Juntendo University School of Medicine Graduate School of Medicine, Tokyo, Japan
| | - Manabu Kuroki
- Graduate School of Engineering Science, Yokohama National University, Kanagawa, Japan
| | - Hiroyuki Daida
- Department of Cardiology, Juntendo University School of Medicine Graduate School of Medicine, Tokyo, Japan
| | - Yuji Nishizaki
- Clinical Translational Science, Juntendo University School of Medicine Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
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11
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Sharma V, Kulkarni V, Joon T, Eurich DT, Simpson SH, Voaklander D, Wright B, Samanani S. Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data. BMJ Open 2023; 13:e071321. [PMID: 37607796 PMCID: PMC10445355 DOI: 10.1136/bmjopen-2022-071321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/26/2023] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions. DESIGN This prognostic study used population-level administrative health data to develop an ML prediction model. SETTING This study took place in Alberta, Canada during 2018-2019. PARTICIPANTS Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded. EXPOSURE Unit of analysis was the individual person. MAIN OUTCOMES/MEASURES We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal validation was done using 2019 data to evaluate model performance. We reported discrimination, calibration and other relevant metrics measured at the end of 2019 on both ranked predictions and predicted probability thresholds. A cost-savings simulation was performed using 2019 data. RESULTS Final number of study participants was 224 445. The validation set had 203 584 participants with 19 389 fall-related events (9.5% pretest probability) and an ML model c-statistic of 0.70. The highest ranked predictions had post-test probabilities ranging from 40% to 50%. Net benefit analysis presented mixed results with some net benefit using the ML model in the 6%-30% range. The top 50 percentile of predicted risks represented nearly $C60 million in health system costs related to falls. Intervening on the top 25 or 50 percentiles of predicted risk could realise substantial (up to $C16 million) savings. CONCLUSION ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.
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Affiliation(s)
- Vishal Sharma
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | | | | | - Dean T Eurich
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Scot H Simpson
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Don Voaklander
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Bruce Wright
- Island Medical Program, University of Victoria, Victoria, British Columbia, Canada
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12
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Mishra AK, Chappell MJ, Emerson S, Skubic M. Fall Risk Prediction in Older Adults Using Free-Text Nursing Notes and Medications in Electronic Health Records. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082830 DOI: 10.1109/embc40787.2023.10341127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Nursing notes in Electronic Health Records (EHR) contain critical health information, including fall risk factors. However, an exploration of fall risk prediction using nursing notes is not well examined. In this study, we explored deep learning architectures to predict fall risk in older adults using text in nursing notes and medications in the EHR. EHR predictor data and fall events outcome data were obtained from 162 older adults living at TigerPlace, a senior living facility located in Columbia, MO. We used pre-trained BioWordVec embeddings to represent the words in the clinical notes and medications and trained multiple recurrent neural network-based natural language processing models to predict future fall events. Our final model predicted falls with an accuracy of 0.81, a sensitivity of 0.75, a specificity of 0.83, and an F1 score of 0.82. This preliminary exploratory analysis provides supporting evidence that fall risk can be predicted from clinical notes and medications. Future studies will utilize additional data modalities available in the EHR to potentially improve fall risk prediction from EHR data.
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13
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Silveira H, Lima J, Plácido J, Ferreira JV, Ferreira R, Laks J, Deslandes A. Dual-Task Performance, Balance and Aerobic Capacity as Predictors of Falls in Older Adults with Cardiovascular Disease: A Comparative Study. Behav Sci (Basel) 2023; 13:488. [PMID: 37366740 DOI: 10.3390/bs13060488] [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: 04/24/2023] [Revised: 06/03/2023] [Accepted: 06/06/2023] [Indexed: 06/28/2023] Open
Abstract
Cardiovascular diseases (CVD) are highly prevalent and strongly associated with the risk of falls in the elderly. Falls are associated with impairments in cognition and functional or gait performance; however, little is known about these associations in the elderly population with CVD. In this study, we aimed to clarify the possible associations of physical capacity and functional and cognitive outcomes with the incidence of falls in older adults with CVD. In this comparative study, 72 elderly patients were divided into fallers (n = 24 cases) and non-fallers (n = 48 controls) according to the occurrence of falls within one year. Machine learning techniques were adopted to formulate a classification model and identify the most important variables associated with the risk of falls. Participants with the worst cardiac health classification, older age, the worst cognitive and functional performance, balance and aerobic capacity were prevalent in the case group. The variables of most importance for the machine learning model were VO2max, dual-task in seconds and the Berg Scale. There was a significant association between cognitive-motor performance and the incidence of falls. Dual-task performance, balance, and aerobic capacity levels were associated with an increased risk of falls, in older adults with CVD, during a year of observation.
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Affiliation(s)
- Heitor Silveira
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, Brazil
| | - Juliana Lima
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, Brazil
| | - Jessica Plácido
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, Brazil
| | - José Vinícius Ferreira
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, Brazil
| | - Renan Ferreira
- Instituto Nacional de Tecnologia, Rio de Janeiro 20081-312, Brazil
| | - Jerson Laks
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, Brazil
- Clínica da Gávea, Rio de Janeiro 22451-262, Brazil
| | - Andrea Deslandes
- Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, Brazil
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14
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Prediction of Prednisolone Dose Correction Using Machine Learning. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:84-103. [PMID: 36910914 PMCID: PMC9995628 DOI: 10.1007/s41666-023-00128-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/20/2022] [Accepted: 02/03/2023] [Indexed: 02/17/2023]
Abstract
Wrong dose, a common prescription error, can cause serious patient harm, especially in the case of high-risk drugs like oral corticosteroids. This study aims to build a machine learning model to predict dose-related prescription modifications for oral prednisolone tablets (i.e., highly imbalanced data with very few positive cases). Prescription data were obtained from the electronic medical records at a single institute. Cluster analysis classified the clinical departments into six clusters with similar patterns of prednisolone prescription. Two patterns of training datasets were created with/without preprocessing by the SMOTE method. Five ML models (SVM, KNN, GB, RF, and BRF) and logistic regression (LR) models were constructed by Python. The model was internally validated by five-fold stratified cross-validation and was validated with a 30% holdout test dataset. Eighty-two thousand five hundred fifty-three prescribing data for prednisolone tablets containing 135 dose-corrected positive cases were obtained. In the original dataset (without SMOTE), only the BRF model showed a good performance (in test dataset, ROC-AUC:0.917, recall: 0.951). In the training dataset preprocessed by SMOTE, performance was improved on all models. The highest performance models with SMOTE were SVM (in test dataset, ROC-AUC: 0.820, recall: 0.659) and BRF (ROC-AUC: 0.814, recall: 0.634). Although the prescribing data for dose-related collection are highly imbalanced, various techniques such as the following have allowed us to build high-performance prediction models: data preprocessing by SMOTE, stratified cross-validation, and BRF classifier corresponding to imbalanced data. ML is useful in complicated dose audits such as oral prednisolone. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00128-3.
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15
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Bargiotas I, Wang D, Mantilla J, Quijoux F, Moreau A, Vidal C, Barrois R, Nicolai A, Audiffren J, Labourdette C, Bertin-Hugaul F, Oudre L, Buffat S, Yelnik A, Ricard D, Vayatis N, Vidal PP. Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall. J Neurol 2023; 270:618-631. [PMID: 35817988 PMCID: PMC9886639 DOI: 10.1007/s00415-022-11251-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023]
Abstract
Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.
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Affiliation(s)
- Ioannis Bargiotas
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France. .,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.
| | - Danping Wang
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Juan Mantilla
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Flavien Quijoux
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,ORPEA Group, Puteaux, France
| | - Albane Moreau
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Catherine Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Otorhinolaryngology (ENT), AP-HP, Hôpital Universitaire Pitié Salpêtrière, Paris, 75013, France
| | - Remi Barrois
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Alice Nicolai
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Julien Audiffren
- Department of Neuroscience, University of Fribourg, Fribourg, Switzerland
| | - Christophe Labourdette
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | | | - Laurent Oudre
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Stephane Buffat
- Laboratoire d'accidentologie de biomécanique et du comportement des conducteurs, GIE Psa Renault Groupes, Nanterre, France
| | - Alain Yelnik
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Physical and Rehabilitation Medicine (PRM), AP- HP, GH St Louis, Lariboisière, F. Widal, Paris, 75010, France
| | - Damien Ricard
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Neurology, AP-HP, Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, 92140, France.,École d'application du Val-de-Grâce, Service de Santé des Armée, Paris, France
| | - Nicolas Vayatis
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Pierre-Paul Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
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16
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The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms. Healthcare (Basel) 2022; 11:healthcare11010047. [PMID: 36611508 PMCID: PMC9818612 DOI: 10.3390/healthcare11010047] [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: 11/04/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Falling is an important public health issue, and predicting the fall risk can reduce the incidence of injury events in the elderly. However, most of the existing studies may have additional human and financial costs for community workers and doctors. Therefore, it is socially important to identify elderly people who are at high fall risk through a reasonable and cost-effective method. We evaluated the potential of multifractal, machine learning algorithms to identify the elderly at high fall risk. We developed a 42-point calibration model of the human body and recorded the three-dimensional coordinate datasets. The stability of the motion trajectory is calculated by the multifractal algorithm and used as an input dimension to compare the performance of the six classifiers. The results showed that the instability of the faller group was significantly greater than that of the no-faller group in the male and female cohorts (p < 0.005), and the Gradient Boosting Decision Tree classifier showed the best performance. The findings could help elderly people at high fall risk to identify individualized risk factors and facilitate tailored fall interventions.
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17
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Alsobhi M, Sachdev HS, Chevidikunnan MF, Basuodan R, K U DK, Khan F. Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15919. [PMID: 36497993 PMCID: PMC9737928 DOI: 10.3390/ijerph192315919] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence (AI) has been used in physical therapy diagnosis and management for various impairments. Physical therapists (PTs) need to be able to utilize the latest innovative treatment techniques to improve the quality of care. The study aimed to describe PTs' views on AI and investigate multiple factors as indicators of AI knowledge, attitude, and adoption among PTs. Moreover, the study aimed to identify the barriers to using AI in rehabilitation. Two hundred and thirty-six PTs participated voluntarily in the study. A concurrent mixed-method design was used to document PTs' opinions regarding AI deployment in rehabilitation. A self-administered survey consisting of several aspects, including demographic, knowledge, uses, advantages, impacts, and barriers limiting AI utilization in rehabilitation, was used. A total of 63.3% of PTs reported that they had not experienced any kind of AI applications at work. The major factors predicting a higher level of AI knowledge among PTs were being a non-academic worker (OR = 1.77 [95% CI; 1.01 to 3.12], p = 0.04), being a senior PT (OR = 2.44, [95%CI: 1.40 to 4.22], p = 0.002), and having a Master/Doctorate degree (OR = 1.97, [95%CI: 1.11 to 3.50], p = 0.02). However, the cost and resources of AI were the major reported barriers to adopting AI-based technologies. The study highlighted a remarkable dearth of AI knowledge among PTs. AI and advanced knowledge in technology need to be urgently transferred to PTs.
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Affiliation(s)
- Mashael Alsobhi
- Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
| | - Harpreet Singh Sachdev
- Department of Neurology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Mohamed Faisal Chevidikunnan
- Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
| | - Reem Basuodan
- Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Dhanesh Kumar K U
- Nitte Institute of Physiotherapy, Nitte University, Deralaktte, Mangalore 575022, India
| | - Fayaz Khan
- Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
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18
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Kuo HC, Hao S, Jin B, Chou CJ, Han Z, Chang LS, Huang YH, Hwa K, Whitin JC, Sylvester KG, Reddy CD, Chubb H, Ceresnak SR, Kanegaye JT, Tremoulet AH, Burns JC, McElhinney D, Cohen HJ, Ling XB. Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan. Front Immunol 2022; 13:1031387. [PMID: 36263040 PMCID: PMC9575935 DOI: 10.3389/fimmu.2022.1031387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundKawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.MethodsA single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.FindingsOur diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.InterpretationThis work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.
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Affiliation(s)
- Ho-Chang Kuo
- Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- Department of Pediatrics, Chang Gung University College of Medicine, Kaohsiung, Taiwan
- *Correspondence: Xuefeng B. Ling, ;Ho-Chang Kuo,
| | - Shiying Hao
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Bo Jin
- School of Medicine, Stanford University, Stanford, CA, United States
| | - C. James Chou
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Zhi Han
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Ling-Sai Chang
- Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- Department of Pediatrics, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Ying-Hsien Huang
- Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- Department of Pediatrics, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Kuoyuan Hwa
- Center for Biomedical Industry, Department of Molecular Science and Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - John C. Whitin
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Karl G. Sylvester
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Charitha D. Reddy
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Henry Chubb
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Scott R. Ceresnak
- School of Medicine, Stanford University, Stanford, CA, United States
| | - John T. Kanegaye
- Pediatrics, University of California San Diego, San Diego, CA, United States
| | | | - Jane C. Burns
- Pediatrics, University of California San Diego, San Diego, CA, United States
| | - Doff McElhinney
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Harvey J. Cohen
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Xuefeng B. Ling
- School of Medicine, Stanford University, Stanford, CA, United States
- *Correspondence: Xuefeng B. Ling, ;Ho-Chang Kuo,
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Pérez-Trujillo M, Curcio CL, Duque-Méndez N, Delgado A, Cano L, Gomez F. Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study. Aging Clin Exp Res 2022; 34:2761-2768. [PMID: 36070079 DOI: 10.1007/s40520-022-02227-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis. AIM This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model. METHODS A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment. RESULTS According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance. CONCLUSION The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.
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Affiliation(s)
- Manuel Pérez-Trujillo
- Departamento de Informática y Computación, Facultad de Administración, Grupo GAIA, Universidad Nacional de Colombia, Manizales, Colombia
| | - Carmen-Lucía Curcio
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia.
| | - Néstor Duque-Méndez
- Departamento de Informática y Computación, Facultad de Administración, Grupo GAIA, Universidad Nacional de Colombia, Manizales, Colombia
| | - Alejandra Delgado
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia
| | - Laura Cano
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia
| | - Fernando Gomez
- Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia
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20
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Chu WM, Kristiani E, Wang YC, Lin YR, Lin SY, Chan WC, Yang CT, Tsan YT. A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment. Front Med (Lausanne) 2022; 9:937216. [PMID: 36016999 PMCID: PMC9398203 DOI: 10.3389/fmed.2022.937216] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 12/03/2022] Open
Abstract
Backgrounds Falls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence. Materials and methods Our working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged >65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression. Results From modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy. Conclusion This is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis.
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Affiliation(s)
- Wei-Min Chu
- Department of Family Medicine, Taichung Veterans General Hospital, sTaichung, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Post-baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Institue of Health Policy and Management, National Taiwan University, Taipei, Taiwan
- Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Endah Kristiani
- Department of Computer Science, Tunghai University, Taichung, Taiwan
- Department of Informatics, Krida Wacana Christian University, Jakarta, Indonesia
| | - Yu-Chieh Wang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
| | - Yen-Ru Lin
- Department of Computer Science, Tunghai University, Taichung, Taiwan
| | - Shih-Yi Lin
- Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Wei-Cheng Chan
- Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan
- Chao-Tung Yang
| | - Yu-Tse Tsan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- *Correspondence: Yu-Tse Tsan
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21
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Alsobhi M, Khan F, Chevidikunnan MF, Basuodan R, Shawli L, Neamatallah Z. Physical Therapists' Knowledge and Attitudes Towards Artificial Intelligence Applications in Healthcare and Rehabilitation: A cross-sectional Study (Preprint). J Med Internet Res 2022; 24:e39565. [DOI: 10.2196/39565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/22/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
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22
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Mishra AK, Skubic M, Despins LA, Popescu M, Keller J, Rantz M, Abbott C, Enayati M, Shalini S, Miller S. Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments. Front Digit Health 2022; 4:869812. [PMID: 35601885 PMCID: PMC9120414 DOI: 10.3389/fdgth.2022.869812] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76–0.85), sensitivity of 0.82 (95% CI of 0.74–0.89), specificity of 0.72 (95% CI of 0.67–0.76), F1 score of 0.76 (95% CI of 0.72–0.79), and accuracy of 0.75 (95% CI of 0.72–0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.
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Affiliation(s)
- Anup Kumar Mishra
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
- *Correspondence: Anup Kumar Mishra
| | - Marjorie Skubic
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
| | - Laurel A. Despins
- Sinclair School of Nursing, University of Missouri, Columbia, MO, United States
| | - Mihail Popescu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
- Department of Health Management and Informatics, University of Missouri, Columbia, MO, United States
| | - James Keller
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
| | - Marilyn Rantz
- Sinclair School of Nursing, University of Missouri, Columbia, MO, United States
| | - Carmen Abbott
- School of Health Professions, Physical Therapy, University of Missouri, Columbia, MO, United States
| | - Moein Enayati
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Shradha Shalini
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Steve Miller
- Sinclair School of Nursing, University of Missouri, Columbia, MO, United States
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23
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Kaplan AD, Tipnis U, Beckham JC, Kimbrel NA, Oslin DW, McMahon BH. Continuous-Time Probabilistic Models for Longitudinal Electronic Health Records. J Biomed Inform 2022; 130:104084. [DOI: 10.1016/j.jbi.2022.104084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/18/2022] [Accepted: 04/25/2022] [Indexed: 10/18/2022]
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24
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Chaieb S, Ben Mrad A, Hnich B. From Personal Observations to Recommendation of Tailored Interventions based on Causal Reasoning: a case study of Falls Prevention in Elderly Patients. Int J Med Inform 2022; 163:104765. [PMID: 35461148 DOI: 10.1016/j.ijmedinf.2022.104765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/31/2022] [Accepted: 04/12/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE While the challenge of estimating the efficacy of therapies using observational data has received a lot of attention, little work has been done on estimating the treatment effect from interventions. In this paper, we tackle this problem by proposing an early guidance system based on a causal Bayesian network (CBN) for recommending personalized interventions. We are interested in the elderly fall prevention context. The objective is to develop a practical tool to help doctors estimate the effects of each intervention (or compound interventions) on a given patient and then choose the one that best fits each patient's health situation to reduce the risk of falling. METHODS On a real-world elderly information base, we undertake an empirical investigation for the proposed approach, which is based on a 44-node CBN. Then, we describe what is possible to achieve using state-of-the-art machine learning methods, namely Support Virtual Machine (SVM), Decision Tree (DT), and Bayesian Network (BN), and how well these methods can be used in recommending personalized interventions compared to the proposed approach. RESULTS 1174 elderly patients from Lille University Hospital, between January 2005 and December 2018 are included. The results reveal that none of the classifiers is significantly superior to the others, even if BN delivers somewhat better results (41.6%) and DT most often slightly lower performance (31.2%). Results also show that none of these three classifiers performs comparable to the proposed system (89.7%). The interventions recommended by the system are in agreement with the expert's judgment to a satisfactory level. The reaction of the physicians to the proposed system in its first trial version was very favorable. CONCLUSION The study showed the efficacy and utility of the causality-based strategy in recommending tailored interventions to prevent elderly falls compared to automated learning methods that had failed to infer a solid interventional paradigm for precision medicine.
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Affiliation(s)
- Salma Chaieb
- University of Sousse, ISITCom, 4011 Sousse, Tunisia; University of Sfax, CES Lab, 3038 Sfax, Tunisia.
| | - Ali Ben Mrad
- University of Sfax, ISAAS, 1013 Sfax, Tunisia; University of Sfax, CES Lab, 3038 Sfax, Tunisia
| | - Brahim Hnich
- University of Monastir, FSM, 5000 Monastir, Tunisia; University of Sfax, CES Lab, 3038 Sfax, Tunisia.
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25
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van de Loo B, Seppala LJ, van der Velde N, Medlock S, Denkinger M, de Groot LCPGM, Kenny RA, Moriarty F, Rothenbacher D, Stricker B, Uitterlinden A, Abu-Hanna A, Heymans MW, van Schoor N. Development of the AD FICE_IT models for predicting falls and recurrent falls in community-dwelling older adults: pooled analyses of European cohorts with special attention to medication. J Gerontol A Biol Sci Med Sci 2022; 77:1446-1454. [PMID: 35380638 PMCID: PMC9255686 DOI: 10.1093/gerona/glac080] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Indexed: 11/13/2022] Open
Abstract
Background Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications. Methods Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal–external cross-validation. Results Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls. Conclusion Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted.
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Affiliation(s)
- Bob van de Loo
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
- Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Lotta J Seppala
- Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Nathalie van der Velde
- Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Stephanie Medlock
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Michael Denkinger
- Institute for Geriatric Research, Ulm University at Agaplesion Bethesda Clinic, and Geriatric Center Ulm, Ulm, Germany
| | | | - Rose-Anne Kenny
- TILDA, Department of Medical Gerontology, Trinity College, Dublin, Ireland
| | - Frank Moriarty
- TILDA, Department of Medical Gerontology, Trinity College, Dublin, Ireland
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Bruno Stricker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - André Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Natasja van Schoor
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
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Gupta A, Maslen C, Vindlacheruvu M, Abel RL, Bhattacharya P, Bromiley PA, Clark EM, Compston JE, Crabtree N, Gregory JS, Kariki EP, Harvey NC, McCloskey E, Ward KA, Poole KE. Digital health interventions for osteoporosis and post-fragility fracture care. Ther Adv Musculoskelet Dis 2022; 14:1759720X221083523. [PMID: 35368375 PMCID: PMC8966117 DOI: 10.1177/1759720x221083523] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The growing burden from osteoporosis and fragility fractures highlights a need to improve osteoporosis management across healthcare systems. Sub-optimal management of osteoporosis is an area suitable for digital health interventions. While fracture liaison services (FLSs) are proven to greatly improve care for people with osteoporosis, such services might benefit from technologies that enhance automation. The term 'Digital Health' covers a variety of different tools including clinical decision support systems, electronic medical record tools, patient decision aids, patient apps, education tools, and novel artificial intelligence (AI) algorithms. Within the scope of this review are AI solutions that use algorithms within health system registries to target interventions. Clinician-targeted, patient-targeted, or system-targeted digital health interventions could be used to improve management and prevent fragility fractures. This review was commissioned by The Royal Osteoporosis Society and Bone Research Academy during the production of the 2020 Research Roadmap (https://theros.org.uk), with the intention of identifying gaps where targeted research funding could lead to improved patient health. We explore potential uses of digital technology in the general management of osteoporosis. Evidence suggests that digital technologies can support multidisciplinary teams to provide the best possible patient care based on current evidence and to support patients in self-management. However, robust randomised controlled studies are still needed to assess the effectiveness and cost-effectiveness of these technologies.
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Affiliation(s)
- Amit Gupta
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | | | | | | | | | | | | | - Nicola Crabtree
- Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | | | | | | | | | | | - Kenneth E.S. Poole
- University of Cambridge School of Clinical Medicine, CB2 0QQ Cambridge, UK
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27
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Posture Monitoring for Health Care of Bedridden Elderly Patients Using 3D Human Skeleton Analysis via Machine Learning Approach. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For bedridden elderly people, pressure ulcer is the most common and serious complication and could be prevented by regular repositioning. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern health/medical caring technology can potentially solve this problem. We propose a RGB-D camera system to recognize the posture of the bedridden elderly patients based on the analysis of 3D human skeleton which consists of articulated joints. Since practically most bedridden patients were covered with a blanket, only four 3D joints were used in our system. After the recognition of the posture, a warning message will be sent to the caregiver for assistance if the patient stays in the same posture for more than a predetermined period (e.g., two hours). Experimental results indicate that our proposed method is capable of achieving a high accuracy in posture recognition (above 95%). To the best of our knowledge, this application of using human skeleton analysis for patient care is novel. The proposed scheme is promising for clinical applications and will undertake an intensive test in health care facilities in the near future after redesigning a proper RGB-D (Red-Green-Blue-Depth) camera system. In addition, a desktop computer can be used for multi-point monitoring to reduce cost, since real-time processing is not required in this application.
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28
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Seaman K, Ludlow K, Wabe N, Dodds L, Siette J, Nguyen A, Jorgensen M, Lord SR, Close JCT, O'Toole L, Lin C, Eymael A, Westbrook J. The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data: a systematic review. BMC Geriatr 2022; 22:210. [PMID: 35291948 PMCID: PMC8923829 DOI: 10.1186/s12877-022-02901-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/04/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Falls in older adults remain a pressing health concern. With advancements in data analytics and increasing uptake of electronic health records, developing comprehensive predictive models for fall risk is now possible. We aimed to systematically identify studies involving the development and implementation of predictive falls models which used routinely collected electronic health record data in home-based, community and residential aged care settings. METHODS A systematic search of entries in Cochrane Library, CINAHL, MEDLINE, Scopus, and Web of Science was conducted in July 2020 using search terms relevant to aged care, prediction, and falls. Selection criteria included English-language studies, published in peer-reviewed journals, had an outcome of falls, and involved fall risk modelling using routinely collected electronic health record data. Screening, data extraction and quality appraisal using the Critical Appraisal Skills Program for Clinical Prediction Rule Studies were conducted. Study content was synthesised and reported narratively. RESULTS From 7,329 unique entries, four relevant studies were identified. All predictive models were built using different statistical techniques. Predictors across seven categories were used: demographics, assessments of care, fall history, medication use, health conditions, physical abilities, and environmental factors. Only one of the four studies had been validated externally. Three studies reported on the performance of the models. CONCLUSIONS Adopting predictive modelling in aged care services for adverse events, such as falls, is in its infancy. The increased availability of electronic health record data and the potential of predictive modelling to document fall risk and inform appropriate interventions is making use of such models achievable. Having a dynamic prediction model that reflects the changing status of an aged care client is key to this moving forward for fall prevention interventions.
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Affiliation(s)
- Karla Seaman
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia.
| | - Kristiana Ludlow
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Nasir Wabe
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Laura Dodds
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Joyce Siette
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia.,The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia
| | - Amy Nguyen
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia.,St Vincent's Clinical School, Medicine, University of New South Wales, Sydney, Australia
| | - Mikaela Jorgensen
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Stephen R Lord
- Neuroscience Research Australia, Sydney, Australia.,School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
| | | | - Libby O'Toole
- Aged Care Quality and Safety Commission, Sydney, Australia
| | - Caroline Lin
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Annaliese Eymael
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
| | - Johanna Westbrook
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW, 2109, Australia
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Douthit BJ, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic TA, Lee MA, Pruinelli L, Schultz MA, Wieben A, Jeffery AD. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Appl Clin Inform 2022; 13:161-179. [PMID: 35139564 PMCID: PMC8828453 DOI: 10.1055/s-0041-1742218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.
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Affiliation(s)
- Brian J. Douthit
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Rachel L. Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia P. Coviak
- Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Thompson Forbes
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Grace Gao
- Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States
| | - Theresa A. Kapetanovic
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Mikyoung A. Lee
- College of Nursing, Texas Woman's University, Denton, Texas, United States
| | - Lisiane Pruinelli
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
| | - Mary A. Schultz
- Department of Nursing, California State University, San Bernardino, California, United States
| | - Ann Wieben
- School of Nursing, University of Wisconsin-Madison, Wisconsin, United States
| | - Alvin D. Jeffery
- School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States,Address for correspondence Alvin D. Jeffery, PhD, RN-BC, CCRN-K, FNP-BC 461 21st Avenue South, Nashville, TN 37240United States
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Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Usability of Electronic Health records in Predicting Short-term falls: Machine learning Applications in Senior Care Facilities (Preprint). JMIR Aging 2021; 5:e35373. [PMID: 35363146 PMCID: PMC9015781 DOI: 10.2196/35373] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/16/2022] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.
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Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res 2021; 23:e26522. [PMID: 34847057 PMCID: PMC8669587 DOI: 10.2196/26522] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/21/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022] Open
Abstract
Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.
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Affiliation(s)
- Kathrin Seibert
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| | - Dominik Domhoff
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| | - Dominik Bruch
- Auf- und Umbruch im Gesundheitswesen UG, Bonn, Germany
| | - Matthias Schulte-Althoff
- School of Business and Economics, Department of Information Systems, Freie Universität Berlin, Einstein Center Digital Future, Berlin, Germany
| | - Daniel Fürstenau
- Department of Digitalization, Copenhagen Business School, Frederiksberg, Denmark.,Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Biessmann
- Faculty VI - Informatics and Media, Beuth University of Applied Sciences, Einstein Center Digital Future, Berlin, Germany
| | - Karin Wolf-Ostermann
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
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Makino K, Lee S, Bae S, Chiba I, Harada K, Katayama O, Tomida K, Morikawa M, Shimada H. Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults. J Clin Med 2021; 10:jcm10215184. [PMID: 34768703 PMCID: PMC8585075 DOI: 10.3390/jcm10215184] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/26/2021] [Accepted: 11/03/2021] [Indexed: 11/16/2022] Open
Abstract
The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.
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Affiliation(s)
- Keitaro Makino
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
- Research Fellowship for Young Scientists, Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo 102-0083, Japan
- Correspondence: ; Tel.: +81-562-44-5651
| | - Sangyoon Lee
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Seongryu Bae
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Ippei Chiba
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Kenji Harada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Osamu Katayama
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Kouki Tomida
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Masanori Morikawa
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan; (S.L.); (S.B.); (I.C.); (K.H.); (O.K.); (K.T.); (M.M.)
| | - Hiroyuki Shimada
- Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu City 474-8511, Japan;
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Dormosh N, Schut MC, Heymans MW, van der Velde N, Abu-Hanna A. Development and internal validation of a risk prediction model for falls among older people using primary care electronic health records. J Gerontol A Biol Sci Med Sci 2021; 77:1438-1445. [PMID: 34637510 PMCID: PMC9255681 DOI: 10.1093/gerona/glab311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Indexed: 11/25/2022] Open
Abstract
Background Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance. Methods We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration. Results Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700–0.714). Conclusions Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests.
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Affiliation(s)
- Noman Dormosh
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Martijn C Schut
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Biostatistics, Amsterdam UMC - Location VU, VU University Medical Center, Amsterdam, The Netherlands
| | - Nathalie van der Velde
- Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands
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Kong SH, Shin CS. Applications of Machine Learning in Bone and Mineral Research. Endocrinol Metab (Seoul) 2021; 36:928-937. [PMID: 34674509 PMCID: PMC8566132 DOI: 10.3803/enm.2021.1111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/23/2021] [Accepted: 09/09/2021] [Indexed: 12/26/2022] Open
Abstract
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul,
Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam,
Korea
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul,
Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul,
Korea
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Lin C, Lee YT, Wu FJ, Lin SA, Hsu CJ, Lee CC, Tsai DJ, Fang WH. The Application of Projection Word Embeddings on Medical Records Scoring System. Healthcare (Basel) 2021; 9:healthcare9101298. [PMID: 34682978 PMCID: PMC8544381 DOI: 10.3390/healthcare9101298] [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: 08/24/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 11/16/2022] Open
Abstract
Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician's score.
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Affiliation(s)
- Chin Lin
- School of Medicine, National Defense Medical Center, Taipei 114, Taiwan;
- School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Yung-Tsai Lee
- Division of Cardiovascular Surgery, Cheng Hsin Rehabilitation and Medical Center, Taipei 112, Taiwan;
| | - Feng-Jen Wu
- Department of Informatics, Taoyuan Armed Forces General Hospital, Taoyuan 325, Taiwan;
| | - Shing-An Lin
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (S.-A.L.); (C.-J.H.); (C.-C.L.)
| | - Chia-Jung Hsu
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (S.-A.L.); (C.-J.H.); (C.-C.L.)
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (S.-A.L.); (C.-J.H.); (C.-C.L.)
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Dung-Jang Tsai
- School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- Correspondence: (D.-J.T.); (W.-H.F.); Tel.: +886-2-8792-3100 (ext. #18305) (D.-J.T.); +886-2-8792-3100 (ext. #12322) (W.-H.F.); Fax: +886-2-8792-3147 (D.-J.T. & W.-H.F.)
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- Correspondence: (D.-J.T.); (W.-H.F.); Tel.: +886-2-8792-3100 (ext. #18305) (D.-J.T.); +886-2-8792-3100 (ext. #12322) (W.-H.F.); Fax: +886-2-8792-3147 (D.-J.T. & W.-H.F.)
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do Nascimento CF, dos Santos HG, de Moraes Batista AF, Roman Lay AA, Duarte YAO, Chiavegatto Filho ADP. Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach. Age Ageing 2021; 50:1692-1698. [PMID: 33945604 DOI: 10.1093/ageing/afab067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models. METHODS Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data. RESULTS The outcome with highest predictive performance was death by DRS (AUC-ROC = 0.89), followed by the other specific causes (AUC-ROC = 0.87), DCS (AUC-ROC = 0.67) and neoplasms (AUC-ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting. CONCLUSION The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.
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Analyses of used engine oils via atomic spectroscopy - Influence of sample pre-treatment and machine learning for engine type classification and lifetime assessment. Talanta 2021; 232:122431. [PMID: 34074417 DOI: 10.1016/j.talanta.2021.122431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 01/13/2023]
Abstract
The analysis of used engine oils from industrial engines enables the study of engine wear and oil degradation in order to evaluate the necessity of oil changes. As the matrix composition of an engine oil strongly depends on its intended application, meaningful diagnostic oil analyses bear considerable challenges. Owing to the broad spectrum of available oil matrices, we have evaluated the applicability of using an internal standard and/or preceding sample digestion for elemental analysis of used engine oils via inductively coupled plasma optical emission spectroscopy (ICP OES). Elements originating from both wear particles and additives as well as particle size influence could be clearly recognized by their distinct digestion behaviour. While a precise determination of most wear elements can be achieved in oily matrix, the measurement of additives is performed preferably after sample digestion. Considering a dataset of physicochemical parameters and elemental composition for several hundred used engine oils, we have further investigated the feasibility of predicting the identity and overall condition of an unknown combustion engine using the machine learning system XGBoost. A maximum accuracy of 89.6% in predicting the engine type was achieved, a mean error of less than 10% of the observed timeframe in predicting the oil running time and even less than 4% for the total engine running time, based purely on common oil check data. Furthermore, obstacles and possibilities to improve the performance of the machine learning models were analysed and the factors that enabled the prediction were explored with SHapley Additive exPlanation (SHAP). Our results demonstrate that both the identification of an unknown engine as well as a lifetime assessment can be performed for a first estimation of the actual sample without requiring meticulous documentation.
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Arfat Y, Mittone G, Esposito R, Cantalupo B, DE Ferrari GM, Aldinucci M. A review of machine learning for cardiology. Minerva Cardiol Angiol 2021; 70:75-91. [PMID: 34338485 DOI: 10.23736/s2724-5683.21.05709-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.
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Affiliation(s)
- Yasir Arfat
- Computer Science Department, University of Turin, Turin, Italy -
| | | | | | | | - Gaetano M DE Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy.,Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Marco Aldinucci
- Computer Science Department, University of Turin, Turin, Italy
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Delanerolle G, Yang X, Shetty S, Raymont V, Shetty A, Phiri P, Hapangama DK, Tempest N, Majumder K, Shi JQ. Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care. ACTA ACUST UNITED AC 2021; 17:17455065211018111. [PMID: 33990172 PMCID: PMC8127586 DOI: 10.1177/17455065211018111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.
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Affiliation(s)
| | - Xuzhi Yang
- Southern University of Science and Technology, Shenzhen, China
| | | | | | - Ashish Shetty
- University College London, London, UK.,University College London NHS Foundation Trust, London, UK
| | - Peter Phiri
- Southern Health NHS Foundation Trust, Southampton, UK.,Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, UK
| | | | | | - Kingshuk Majumder
- University of Manchester Hospitals NHS Foundation Trust, Manchester, UK
| | - Jian Qing Shi
- Southern University of Science and Technology, Shenzhen, China.,The Alan Turing Institute, London, UK
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Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine Learning Solutions for Osteoporosis-A Review. J Bone Miner Res 2021; 36:833-851. [PMID: 33751686 DOI: 10.1002/jbmr.4292] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/04/2021] [Accepted: 03/16/2021] [Indexed: 12/11/2022]
Abstract
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Julien Smets
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Enisa Shevroja
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Didier Hans
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
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Handley NR, Feng FY, Guise TA, D'Andrea D, Kelly WK, Gomella LG. Preserving Well-being in Patients With Advanced and Late Prostate Cancer. Urology 2020; 155:199-209. [PMID: 33373704 DOI: 10.1016/j.urology.2020.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/23/2020] [Accepted: 12/13/2020] [Indexed: 10/22/2022]
Abstract
Androgen deprivation therapy, alone or in combination with androgen signaling inhibitors, is a treatment option for patients with advanced prostate cancer (PC). When making treatment decisions, health care providers must consider the long-term effects of treatment on the patient's overall health and well-being. Herein, we review the effects of these treatments on the musculoskeletal and cardiovascular systems, cognition, and fall risk, and provide management approaches for each. We also include an algorithm to help health care providers implement best clinical practices and interdisciplinary care for preserving the overall well-being of PC patients.
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Affiliation(s)
- Nathan R Handley
- Department of Medical Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA.
| | - Felix Y Feng
- Departments of Radiation Oncology, Urology, and Medicine, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA
| | - Theresa A Guise
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | | | - William Kevin Kelly
- Department of Medical Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA; Department of Urology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA
| | - Leonard G Gomella
- Department of Urology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA
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Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease. Parkinsonism Relat Disord 2020; 82:77-83. [PMID: 33249293 DOI: 10.1016/j.parkreldis.2020.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 11/11/2020] [Accepted: 11/13/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable high-risk activities to reduce future falls. OBJECTIVES To explore the prediction of falling in PD patients using a machine learning-based approach. METHOD 305 PD patients, with or without a history of falls within the past month, were recruited. Data including clinical demographics, medications, and balance confidence, scaled by the 16-item Activities-Specific Balance Confidence Scale (ABC-16), were entered into the supervised machine learning models using XGBoost to explore the prediction of fallers/recurrent fallers in two separate models. RESULTS 99 (32%) patients were fallers and 58 (19%) were recurrent fallers. The accuracy of the model to predict falls was 72% (p = 0.001). The most important factors were item 7 (sweeping the floor), item 5 (reaching on tiptoes), and item 12 (walking in a crowded mall) in the ABC-16 scale, followed by disease stage and duration. When recurrent falls were analysed, the models had higher accuracy (81%, p = 0.02). The strongest predictors of recurrent falls were item 12, 5, and 10 (walking across parking lot), followed by disease stage and current age. CONCLUSION Our machine learning-based study demonstrated that predictors of falling combined demographics of PD with environmental factors, including high-risk activities that require cognitive attention and changes in vertical and lateral orientations. This enables physicians to focus on modifiable factors and appropriately implement fall prevention strategies for individual patients.
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Alfian G, Syafrudin M, Anshari M, Benes F, Atmaji FTD, Fahrurrozi I, Hidayatullah AF, Rhee J. Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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