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Seidenfeld J, Lee S, Ragsdale L, Nickel CH, Liu SW, Kennedy M. Risk factors and risk stratification approaches for delirium screening: A Geriatric Emergency Department Guidelines 2.0 systematic review. Acad Emerg Med 2024; 31:969-984. [PMID: 38847070 DOI: 10.1111/acem.14939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/08/2024] [Accepted: 04/27/2024] [Indexed: 10/18/2024]
Abstract
OBJECTIVE As part of the Geriatric Emergency Department (ED) Guidelines 2.0 project, we conducted a systematic review to find risk factors or risk stratification approaches that can be used to identify subsets of older adults who may benefit from targeted ED delirium screening. METHODS An electronic search strategy was developed with a medical librarian, conducted in April 2021 and November 2022. Full-text studies of patients ≥65 years assessed for prevalent delirium in the ED were included. Risk of bias was assessed using the McMaster University Clarity Group tool. Outcomes measures pertained to the risk stratification method used. Due to heterogeneity of patient populations, risk stratification methods, and outcomes, a meta-analysis was not conducted. RESULTS Our search yielded 1878 unique citations, of which 13 were included. Six studies developed a novel delirium risk score with or without evaluation of specific risk factors, six studies evaluated specific risk factors only, and one study evaluated an existing nondelirium risk score for association with delirium. The most common risk factor was history of dementia, with odds ratios ranging from 3.3 (95% confidence interval [CI] 1.2-8.9) to 18.33 (95% CI 8.08-43.64). Other risk factors that were consistently associated with increased risk of delirium included older age, use of certain medications (such as antipsychotics, antidepressants, and opioids, among others), and functional impairments. Of the studies that developed novel risk scores, the reported area under the curve ranged from 0.77 to 0.90. Only two studies reported potential impact of the risk stratification tool on screening burden. CONCLUSIONS There is significant heterogeneity, but results suggest that factors such as dementia, age over 75, and functional impairments should be used to identify older adults who are at highest risk for ED delirium. No studies evaluated implementation of a risk stratification method for delirium screening or evaluated patient-oriented outcomes.
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Affiliation(s)
- Justine Seidenfeld
- Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Health Care System, Durham, North Carolina, USA
- Department of Emergency Medicine, Durham VA Health Care System, Durham, North Carolina, USA
- Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sangil Lee
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Luna Ragsdale
- Department of Emergency Medicine, Durham VA Health Care System, Durham, North Carolina, USA
- Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christian H Nickel
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Shan W Liu
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Maura Kennedy
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA
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Prakash R, Dupre ME, Østbye T, Xu H. Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study. JMIR Aging 2024; 7:e57926. [PMID: 39316421 PMCID: PMC11462099 DOI: 10.2196/57926] [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: 02/29/2024] [Revised: 07/08/2024] [Accepted: 07/24/2024] [Indexed: 09/25/2024] Open
Abstract
BACKGROUND The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or "hidden" in unstructured text fields and not readily available for clinicians to act upon. OBJECTIVE We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. METHODS We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians' notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, "mild dementia" and "advanced Alzheimer disease"). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. RESULTS We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. CONCLUSIONS Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.
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Affiliation(s)
- Ravi Prakash
- Thomas Lord Department of Mechanical Engineering and Materials Science, Pratt School of Engineering, Duke University, Durham, NC, United States
| | - Matthew E Dupre
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, United States
- Department of Sociology, Trinity College of Arts & Sciences, Duke University, Durham, NC, United States
| | - Truls Østbye
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, United States
- Department of Family Medicine and Community Health, School of Medicine, Duke Univeristy, Durham, NC, United States
| | - Hanzhang Xu
- Department of Family Medicine and Community Health, School of Medicine, Duke Univeristy, Durham, NC, United States
- School of Nursing, Duke University, Durham, NC, United States
- Center for the Study of Aging and Human Development, Duke University, Durham, NC, United States
- Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore
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Brain J, Kafadar AH, Errington L, Kirkley R, Tang EY, Akyea RK, Bains M, Brayne C, Figueredo G, Greene L, Louise J, Morgan C, Pakpahan E, Reeves D, Robinson L, Salter A, Siervo M, Tully PJ, Turnbull D, Qureshi N, Stephan BC. What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review. Dement Geriatr Cogn Dis Extra 2024; 14:49-74. [PMID: 39015518 PMCID: PMC11250535 DOI: 10.1159/000539744] [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: 02/25/2024] [Accepted: 06/07/2024] [Indexed: 07/18/2024] Open
Abstract
Introduction Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
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Affiliation(s)
- Jacob Brain
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Aysegul Humeyra Kafadar
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
| | - Linda Errington
- Walton Library, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael Kirkley
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Eugene Y.H. Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ralph K. Akyea
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Manpreet Bains
- Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | | | - Leanne Greene
- Exeter Clinical Trials Unit, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennie Louise
- Women’s and Children’s Hospital Research Centre and South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Catharine Morgan
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK
| | - Eduwin Pakpahan
- Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK
| | - David Reeves
- School for Health Sciences, University of Manchester, Manchester, UK
| | - Louise Robinson
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Amy Salter
- School of Public Health, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Mario Siervo
- School of Population Health, Curtin University, Perth, WA, Australia
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
| | - Phillip J. Tully
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Medicine and Health, School of Psychology, University of New England, Armidale, NSW, Australia
| | - Deborah Turnbull
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Nadeem Qureshi
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Blossom C.M. Stephan
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
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Sim JA, Huang X, Horan MR, Stewart CM, Robison LL, Hudson MM, Baker JN, Huang IC. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artif Intell Med 2023; 146:102701. [PMID: 38042599 PMCID: PMC10693655 DOI: 10.1016/j.artmed.2023.102701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/30/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
OBJECTIVE Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care. METHODS We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs. RESULTS Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP. CONCLUSIONS This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.
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Affiliation(s)
- Jin-Ah Sim
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; School of AI Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, TN, United States
| | - Madeline R Horan
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Christopher M Stewart
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States
| | - Leslie L Robison
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Justin N Baker
- Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - I-Chan Huang
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.
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Taylor B, Barboi C, Boustani M. Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review. J Am Geriatr Soc 2023; 71:2966-2974. [PMID: 37249252 DOI: 10.1111/jgs.18426] [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: 07/29/2022] [Revised: 04/12/2023] [Accepted: 04/30/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND The timely detection of Alzheimer's disease and other related dementias (ADRD) is suboptimal. Digital data already stored in electronic health records (EHR) offer opportunities for enhancing the timely detection of ADRD by facilitating the development of passive digital markers (PDMs). We conducted a systematic evidence review to identify studies that describe the development, performance, and validity of EHR-based PDMs for ADRD. METHODS We searched the literature published from January 2000 to August 2022 and reviewed cross-sectional, retrospective, or prospective observational studies with a patient population of 18 years or older, published in English that collected and interpreted original data, included EHR as a source of digital data, and had the primary purpose of supporting ADRD care. We extracted relevant data from the included studies with guidance from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and used the US Preventive Services Task Force criteria to appraise each study. RESULTS We included and appraised 19 studies. Four studies were considered to have a fair quality, and none was considered to have a good quality. The functionality of the PDMs varied from detecting mild cognitive impairment, Alzheimer's disease or ADRD, to forecasting stages of ADRD. Only seven studies used a valid reference diagnostic method. Nine PDMs used only structured EHR data, and five studies provided complete information on the race and ethnicity of its population. The number of features included in the PDMs ranges from 10 to 853, and the PMDs used a variety of statistical and machine learning algorithms with various time-at-risk windows. The area under the curve (AUC) for the PDMs varied from 0.67 to 0.97. CONCLUSION Although we noted heterogeneity in the PDMs development and performance, there is evidence that these PDMs have the potential to detect ADRD at earlier stages.
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Affiliation(s)
- Britain Taylor
- Department of Intelligent Systems Engineering, School of Informatics, Computing, and Engineering. Indiana University, Bloomington, Indiana, USA
| | - Cristina Barboi
- Department of Epidemiology, School of Public Health. Indiana University, Indianapolis, Indiana, USA
| | - Malaz Boustani
- Center for Health Innovation and Implementation Science, Department of Medicine, School of Medicine, Indiana University, Indianapolis, Indiana, USA
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Vogelgsang J, Dan S, Lally AP, Chatigny M, Vempati S, Abston J, Durning PT, Oakley DH, McCoy TH, Klengel T, Berretta S. Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12464. [PMID: 37745891 PMCID: PMC10517223 DOI: 10.1002/dad2.12464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/30/2023] [Accepted: 07/07/2023] [Indexed: 09/26/2023]
Abstract
Introduction Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records. Methods We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures. Results Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes. Discussion This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.
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Affiliation(s)
- Jonathan Vogelgsang
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Shu Dan
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Anna P. Lally
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Michael Chatigny
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Sangeetha Vempati
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Joshua Abston
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Peter T. Durning
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Derek H. Oakley
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Department of Pathology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Thomas H. McCoy
- Department of Psychiatry and Medicine, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Torsten Klengel
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Sabina Berretta
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
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Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1492. [PMID: 38439952 PMCID: PMC10909482 DOI: 10.1002/widm.1492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 03/06/2024]
Abstract
Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Richard Milne
- Kavli Centre for Ethics, Science and the PublicUniversity of CambridgeCambridgeUK
- Engagement and SocietyWellcome Connecting ScienceCambridgeUK
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Maclagan LC, Abdalla M, Harris DA, Stukel TA, Chen B, Candido E, Swartz RH, Iaboni A, Jaakkimainen RL, Bronskill SE. Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing? JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:42-58. [PMID: 36910911 PMCID: PMC9995630 DOI: 10.1007/s41666-023-00125-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 12/23/2022] [Accepted: 01/07/2023] [Indexed: 01/24/2023]
Abstract
Dementia and mild cognitive impairment can be underrecognized in primary care practice and research. Free-text fields in electronic medical records (EMRs) are a rich source of information which might support increased detection and enable a better understanding of populations at risk of dementia. We used natural language processing (NLP) to identify dementia-related features in EMRs and compared the performance of supervised machine learning models to classify patients with dementia. We assembled a cohort of primary care patients aged 66 + years in Ontario, Canada, from EMR notes collected until December 2016: 526 with dementia and 44,148 without dementia. We identified dementia-related features by applying published lists, clinician input, and NLP with word embeddings to free-text progress and consult notes and organized features into thematic groups. Using machine learning models, we compared the performance of features to detect dementia, overall and during time periods relative to dementia case ascertainment in health administrative databases. Over 900 dementia-related features were identified and grouped into eight themes (including symptoms, social, function, cognition). Using notes from all time periods, LASSO had the best performance (F1 score: 77.2%, sensitivity: 71.5%, specificity: 99.8%). Model performance was poor when notes written before case ascertainment were included (F1 score: 14.4%, sensitivity: 8.3%, specificity 99.9%) but improved as later notes were added. While similar models may eventually improve recognition of cognitive issues and dementia in primary care EMRs, our findings suggest that further research is needed to identify which additional EMR components might be useful to promote early detection of dementia. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00125-6.
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Affiliation(s)
| | - Mohamed Abdalla
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Daniel A. Harris
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Therese A. Stukel
- ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5 Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Branson Chen
- ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5 Canada
| | - Elisa Candido
- ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5 Canada
| | - Richard H. Swartz
- ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5 Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Andrea Iaboni
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - R. Liisa Jaakkimainen
- ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5 Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, Canada
| | - Susan E. Bronskill
- ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5 Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
- Women’s College Research Institute, Women’s College Hospital, Toronto, Canada
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Fu S, Wang L, Moon S, Zong N, He H, Pejaver V, Relevo R, Walden A, Haendel M, Chute CG, Liu H. Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review. Clin Transl Sci 2023; 16:398-411. [PMID: 36478394 PMCID: PMC10014687 DOI: 10.1111/cts.13463] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.
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Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Sungrim Moon
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Nansu Zong
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Huan He
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rose Relevo
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Anita Walden
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Hongfang Liu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
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10
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Castro VM, Hart KL, Sacks CA, Murphy SN, Perlis RH, McCoy TH. Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients. Gen Hosp Psychiatry 2022; 74:9-17. [PMID: 34798580 PMCID: PMC8562039 DOI: 10.1016/j.genhosppsych.2021.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). METHOD Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. RESULTS Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. CONCLUSION This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.
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Affiliation(s)
- Victor M. Castro
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Research Information Science and Computing, Mass General Brigham, 399 Revolution Drive, Somerville, MA 02145, USA
| | - Kamber L. Hart
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Chana A. Sacks
- Department of Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA
| | - Shawn N. Murphy
- Research Information Science and Computing, Mass General Brigham, 399 Revolution Drive, Somerville, MA 02145, USA,Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Thomas H. McCoy
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Corresponding author at: Simches Research Building, Massachusetts General Hospital, 185 Cambridge St, 6th Floor, Boston, MA 02114, USA
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11
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Crema C, Attardi G, Sartiano D, Redolfi A. Natural language processing in clinical neuroscience and psychiatry: A review. Front Psychiatry 2022; 13:946387. [PMID: 36186874 PMCID: PMC9515453 DOI: 10.3389/fpsyt.2022.946387] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.
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Affiliation(s)
- Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Daniele Sartiano
- Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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12
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Lee DY, Park J, Noh JS, Roh HW, Ha JH, Lee EY, Son SJ, Park RW. Characteristics of Dimensional Psychopathology in Suicidal Patients With Major Psychiatric Disorders and Its Association With the Length of Hospital Stay: Algorithm Validation Study. JMIR Ment Health 2021; 8:e30827. [PMID: 34477555 PMCID: PMC8449292 DOI: 10.2196/30827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/28/2021] [Accepted: 08/02/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Suicide has emerged as a serious concern for public health; however, only few studies have revealed the differences between major psychiatric disorders and suicide. Recent studies have attempted to quantify research domain criteria (RDoC) into numeric scores to systematically use them in computerized methods. The RDoC scores were used to reveal the characteristics of suicide and its association with major psychiatric disorders. OBJECTIVE We intended to investigate the differences in the dimensional psychopathology among hospitalized suicidal patients and the association between the dimensional psychopathology of psychiatric disorders and length of hospital stay. METHODS This retrospective study enrolled hospitalized suicidal patients diagnosed with major psychiatric disorders (depression, schizophrenia, and bipolar disorder) between January 2010 and December 2020 at a tertiary hospital in South Korea. The RDoC scores were calculated using the patients' admission notes. To measure the differences between psychiatric disorder cohorts, analysis of variance and the Cochran Q test were conducted and post hoc analysis for RDoC domains was performed with the independent two-sample t test. A linear regression model was used to analyze the association between the RDoC scores and sociodemographic features and comorbidity index. To estimate the association between the RDoC scores and length of hospital stay, multiple logistic regression models were applied to each psychiatric disorder group. RESULTS We retrieved 732 admissions for 571 patients (465 with depression, 73 with schizophrenia, and 33 with bipolar disorder). We found significant differences in the dimensional psychopathology according to the psychiatric disorders. The patient group with depression showed the highest negative RDoC domain scores. In the cognitive and social RDoC domains, the groups with schizophrenia and bipolar disorder scored higher than the group with depression. In the arousal RDoC domain, the depression and bipolar disorder groups scored higher than the group with schizophrenia. We identified significant associations between the RDoC scores and length of stay for the depression and bipolar disorder groups. The odds ratios (ORs) of the length of stay were increased because of the higher negative RDoC domain scores in the group with depression (OR 1.058, 95% CI 1.006-1.114) and decreased by higher arousal RDoC domain scores in the group with bipolar disorder (OR 0.537, 95% CI 0.285-0.815). CONCLUSIONS This study showed the association between the dimensional psychopathology of major psychiatric disorders related to suicide and the length of hospital stay and identified differences in the dimensional psychopathology of major psychiatric disorders. This may provide new perspectives for understanding suicidal patients.
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Affiliation(s)
- Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Jai Sung Noh
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Hyun Woong Roh
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jae Ho Ha
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Eun Young Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.,Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon, Republic of Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.,Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
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13
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Moura LMVR, Festa N, Price M, Volya M, Benson NM, Zafar S, Weiss M, Blacker D, Normand SL, Newhouse JP, Hsu J. Identifying Medicare beneficiaries with dementia. J Am Geriatr Soc 2021; 69:2240-2251. [PMID: 33901296 PMCID: PMC8373730 DOI: 10.1111/jgs.17183] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/02/2021] [Accepted: 04/03/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND/OBJECTIVES No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims. DESIGN In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard. SETTING Mass-General-Brigham Healthcare (MGB), Massachusetts, USA. PARTICIPANTS From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. INTERVENTION None. MEASUREMENTS We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV). RESULTS Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97). CONCLUSION This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.
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Affiliation(s)
- Lidia M V R Moura
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Natalia Festa
- Department of Internal Medicine, Section of Geriatric Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mary Price
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Margarita Volya
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nicole M Benson
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Sahar Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Max Weiss
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Sharon-Lise Normand
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Joseph P Newhouse
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Division of Health Policy Research and Education, Harvard Kennedy School, Cambridge, Massachusetts, USA
- Programs on Health Care, Health Economics, Productivity, and Children, National Bureau of Economic Research, Cambridge, Massachusetts, USA
| | - John Hsu
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
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14
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Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open 2021; 4:ooab052. [PMID: 34350389 PMCID: PMC8327375 DOI: 10.1093/jamiaopen/ooab052] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
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Affiliation(s)
- Sayantan Kumar
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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15
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Bynum JPW, Dorr DA, Lima J, McCarthy EP, McCreedy E, Platt R, Vydiswaran VGV. Using Healthcare Data in Embedded Pragmatic Clinical Trials among People Living with Dementia and Their Caregivers: State of the Art. J Am Geriatr Soc 2021; 68 Suppl 2:S49-S54. [PMID: 32589274 DOI: 10.1111/jgs.16617] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/25/2020] [Accepted: 04/29/2020] [Indexed: 12/16/2022]
Abstract
Embedded pragmatic clinical trials (ePCTs) are embedded in healthcare systems as well as their data environments. For people living with dementia (PLWD), settings of care can be different from the general population and involve additional people whose information is also important. The ePCT designs have the opportunity to leverage data that becomes available through the normal delivery of care. They may be particularly valuable in Alzheimer's disease and Alzheimer's disease-related dementia (AD/ADRD), given the complexity of case identification and the diversity of care settings. Grounded in the objectives of the Data and Technical Core of the newly established National Institute on Aging Imbedded Pragmatic Alzheimer's Disease and AD-Related Dementias Clinical Trials Collaboratory (IMPACT Collaboratory), this article summarizes the state of the art in using existing data sources (eg, Medicare claims, electronic health records) in AD/ADRD ePCTs and approaches to integrating them in real-world settings. J Am Geriatr Soc 68:S49-S54, 2020.
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Affiliation(s)
- Julie P W Bynum
- Department of Internal Medicine, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan, USA
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Julie Lima
- Center for Gerontology and Healthcare Research, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Ellen P McCarthy
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA.,Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ellen McCreedy
- Center for Gerontology and Healthcare Research, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Richard Platt
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - V G Vinod Vydiswaran
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.,School of Information, University of Michigan, Ann Arbor, Michigan, USA
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16
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Abstract
OBJECTIVE The authors sought to characterize the association between prior mood disorder diagnosis and hospital outcomes among individuals admitted with COVID-19 to six Eastern Massachusetts hospitals. METHODS A retrospective cohort was drawn from the electronic health records of two academic medical centers and four community hospitals between February 15 and May 24, 2020. Associations between history of mood disorder and in-hospital mortality and hospital discharge home were examined using regression models among any hospitalized patients with positive tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). RESULTS Among 2,988 admitted individuals, 717 (24.0%) had a prior mood disorder diagnosis. In Cox regression models adjusted for age, sex, and hospital site, presence of a mood disorder prior to admission was associated with greater in-hospital mortality risk beyond hospital day 12 (crude hazard ratio=2.156, 95% CI=1.540, 3.020; fully adjusted hazard ratio=1.540, 95% CI=1.054, 2.250). A mood disorder diagnosis was also associated with greater likelihood of discharge to a skilled nursing facility or other rehabilitation facility rather than home (crude odds ratio=2.035, 95% CI=1.661, 2.493; fully adjusted odds ratio=1.504, 95% CI=1.132, 1.999). CONCLUSIONS Hospitalized individuals with a history of mood disorder may be at risk for greater COVID-19 morbidity and mortality and are at increased risk of need for postacute care. Further studies should investigate the mechanism by which these disorders may confer elevated risk.
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Affiliation(s)
- Victor M Castro
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
| | - Faith M Gunning
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
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17
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Castro VM, Sacks CA, Perlis RH, McCoy TH. Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019. J Acad Consult Liaison Psychiatry 2021; 62:298-308. [PMID: 33688635 PMCID: PMC7933786 DOI: 10.1016/j.jaclp.2020.12.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/27/2020] [Accepted: 12/09/2020] [Indexed: 12/12/2022]
Abstract
Background The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts. Objectives To develop an incident delirium predictive model among coronavirus disease 2019 patients. Methods We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings. Results Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71–0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals. Conclusion Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.
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Affiliation(s)
- Victor M Castro
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Chana A Sacks
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
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18
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Hart KL, Perlis RH, McCoy TH. Mapping of Transdiagnostic Neuropsychiatric Phenotypes Across Patients in Two General Hospitals. J Acad Consult Liaison Psychiatry 2021; 62:430-439. [PMID: 34210402 DOI: 10.1016/j.jaclp.2021.01.002] [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: 11/30/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Multidimensional transdiagnostic phenotyping systems are increasingly important to neuropsychiatric phenotyping, particularly in translational research settings. The relationship the National Institute of Mental Health's Research Domain Criteria multidimensional approach to psychopathology and nonpsychiatric diagnoses has not been studied at scale but is relevant to those caring for neuropsychiatric illness in medical and surgical settings. METHODS We applied the CQH Dimensional Phenotyper natural language processing tool to estimate National Institute of Mental Health's Research Domain Criteria domain-associated symptoms of individuals admitted to nonpsychiatric wards at each of 2 large academic general hospitals over an 8-year period. We compared patterns in individual domain symptom burden, as well as a new pooled unidimensional measure, by primary medical and surgical diagnosis. RESULTS Analysis included 227,243 patients from hospital 1 of whom 68,793 (30.3%) had a prior psychiatric history and 220,213 patients from hospital 2 of whom 50,818 (23.1%) had a prior psychiatric history. The distribution of Research Domain Criteria symptom burdens over primary diagnosis was similar across hospital sites and differed significantly across primary medical or surgical diagnosis. The effect of primary medical or surgical diagnosis was larger than that of prior psychiatric history on Research Domain Criteria symptom burden. CONCLUSION Research Domain Criteria-based neuropsychiatric symptom burden estimated from general hospital patients' clinical documentation is more strongly associated with the primary hospital medical or surgical diagnosis than it is with the presence of a previous psychiatric history. The bidirectional role of psychiatric and somatic illness warrants further study through the lens of transdiagnostic phenotyping.
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Affiliation(s)
- Kamber L Hart
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
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19
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Maserejian N, Krzywy H, Eaton S, Galvin JE. Cognitive measures lacking in EHR prior to dementia or Alzheimer's disease diagnosis. Alzheimers Dement 2021; 17:1231-1243. [PMID: 33656251 PMCID: PMC8359414 DOI: 10.1002/alz.12280] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/02/2020] [Accepted: 11/25/2020] [Indexed: 12/14/2022]
Abstract
Introduction The extent that cognitive measures are documented in electronic health records (EHR) is important for quality care and addressing disparities in timely diagnosis of dementia or Alzheimer's disease (AD). Methods Analysis of U.S. EHR data to describe the frequency and factors associated with cognitive measures prior to diagnosis of dementia (N = 111,125) or AD (N = 30,203). Results Only 11% of dementia patients and 24% of AD patients had a cognitive measure documented in the 5 years prior to diagnosis. Black race, older age, non‐commercial health insurance, lower mean neighborhood income, greater in‐patient stays, and fewer out‐patient visits were associated with lacking cognitive measures. Discussion Extensive missing cognitive data and differences in the availability of cognitive measures by race, age, and socioeconomic factors hinder patient care and limit utility of EHR for dementia research. Structured fields and prompts for cognitive data inputs at the point of care may help address these gaps.
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Affiliation(s)
| | | | | | - James E Galvin
- Department of Neurology, Comprehensive Center for Brain Health, University of Miami Miller School of Medicine, Miami, Florida, USA
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20
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Lee S, Doktorchik C, Martin EA, D'Souza AG, Eastwood C, Shaheen AA, Naugler C, Lee J, Quan H. Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review. JMIR Med Inform 2021; 9:e23934. [PMID: 33522976 PMCID: PMC7884219 DOI: 10.2196/23934] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/20/2020] [Accepted: 12/05/2020] [Indexed: 12/16/2022] Open
Abstract
Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.
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Affiliation(s)
- Seungwon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chelsea Doktorchik
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elliot Asher Martin
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Adam Giles D'Souza
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Cathy Eastwood
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Abdel Aziz Shaheen
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hude Quan
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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21
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Rathod-Mistry T, Marshall M, Campbell P, Bailey J, Chew-Graham CA, Croft P, Frisher M, Hayward R, Negi R, Robinson L, Singh S, Sumathipala A, Thein N, Walters K, Weich S, Jordan KP. Indicators of dementia disease progression in primary care: An electronic health record cohort study. Eur J Neurol 2021; 28:1499-1510. [PMID: 33378599 DOI: 10.1111/ene.14710] [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: 07/23/2020] [Revised: 12/17/2020] [Accepted: 12/20/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND PURPOSE The objectives were to assess the feasibility and validity of using markers of dementia-related health as indicators of dementia progression in primary care, by assessing the frequency with which they are recorded and by testing the hypothesis that they are associated with recognised outcomes of dementia. The markers, in 13 domains, were derived previously through literature review, expert consensus, and analysis of regional primary care records. METHODS The study population consisted of patients with a recorded dementia diagnosis in the Clinical Practice Research Datalink, a UK primary care database linked to secondary care records. Incidence of recorded domains in the 36 months after diagnosis was determined. Associations of recording of domains with future hospital admission, palliative care, and mortality were derived. RESULTS There were 30,463 people with diagnosed dementia. Incidence of domains ranged from 469/1000 person-years (Increased Multimorbidity) to 11/1000 (Home Pressures). An increasing number of domains in which a new marker was recorded in the first year after diagnosis was associated with hospital admission (hazard ratio for ≥4 domains vs. no domains = 1.24; 95% confidence interval = 1.15-1.33), palliative care (1.87; 1.62-2.15), and mortality (1.57; 1.47-1.67). Individual domains were associated with outcomes with varying strengths of association. CONCLUSIONS Feasibility and validity of potential indicators of progression of dementia derived from primary care records are supported by their frequency of recording and associations with recognised outcomes. Further research should assess whether these markers can help identify patients with poorer prognosis to improve outcomes through stratified care and targeted support.
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Affiliation(s)
| | | | - Paul Campbell
- School of Medicine, Keele University, Keele, UK.,Midlands Partnership NHS Foundation Trust, Stafford, UK
| | | | - Carolyn A Chew-Graham
- School of Medicine, Keele University, Keele, UK.,Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Peter Croft
- School of Medicine, Keele University, Keele, UK
| | - Martin Frisher
- School of Pharmacy and Bioengineering, Keele University, Keele, UK
| | | | - Rashi Negi
- Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Louise Robinson
- Institute of Health and Society and Newcastle University Institute for Ageing, Newcastle Upon Tyne, UK
| | - Swaran Singh
- Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, UK
| | - Athula Sumathipala
- School of Medicine, Keele University, Keele, UK.,Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Nwe Thein
- Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Kate Walters
- Research Department of Primary Care & Population Health, University College London, London, UK
| | - Scott Weich
- Mental Health Research Unit, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Kelvin P Jordan
- School of Medicine, Keele University, Keele, UK.,Centre for Prognosis Research, Keele University, Keele, UK
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22
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Hart KL, Pellegrini AM, Forester BP, Berretta S, Murphy SN, Perlis RH, McCoy TH. Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method. Gen Hosp Psychiatry 2021; 68:46-51. [PMID: 33310013 PMCID: PMC7855889 DOI: 10.1016/j.genhosppsych.2020.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 01/29/2023]
Abstract
BACKGROUND Agitation is a common feature of many neuropsychiatric disorders. OBJECTIVE Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases. METHODS We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined. RESULTS Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype. CONCLUSION Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.
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Affiliation(s)
- Kamber L. Hart
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | | | - Brent P. Forester
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA,McLean Hospital, 115 Mill St, Belmont, MA 02478, USA
| | - Sabina Berretta
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; McLean Hospital, 115 Mill St, Belmont, MA 02478, USA.
| | - Shawn N. Murphy
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Roy H. Perlis
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Thomas H. McCoy
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA,Corresponding author at: Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA. (T.H. McCoy)
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23
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Differences among Research Domain Criteria score trajectories by Diagnostic and Statistical Manual categorical diagnosis during inpatient hospitalization. PLoS One 2020; 15:e0237698. [PMID: 32842139 PMCID: PMC7447552 DOI: 10.1371/journal.pone.0237698] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 08/02/2020] [Indexed: 02/07/2023] Open
Abstract
With brief psychiatric hospitalizations, the extent to which symptoms change is rarely characterized. We sought to understand symptomatic changes across Research Domain Criteria (RDoC) dimensions, and the extent to which such improvement might be associated with risk for readmission. We identified 3,634 individuals with 4,713 hospital admissions to the psychiatric inpatient unit of a large academic medical center between 2010 and 2015. We applied a natural language processing tool to extract estimates of the five RDoC domains to the admission note and discharge summary and calculated the change in each domain. We examined the extent to which symptom domains changed during admission, and their relationship to baseline clinical and sociodemographic features, using linear regression. Symptomatic worsening was rare in the negative valence (0.4%) and positive valence (5.1%) domains, but more common in cognition (25.8%). Most diagnoses exhibited improvement in negative valence, which was associated with significant reduction in readmission risk. Despite generally brief hospital stays, we detected reduction across multiple symptom domains, with greatest improvement in negative symptoms, and greatest probability of worsening in cognitive symptoms. This approach should facilitate investigations of other features or interventions which may influence pace of clinical improvement.
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Application of natural language processing algorithms for extracting information from news articles in event-based surveillance. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2020; 46:186-191. [PMID: 33382063 PMCID: PMC7755067 DOI: 10.14745/ccdr.46i06a06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The focus of this article is the application of natural language processing (NLP) for information extraction in event-based surveillance (EBS) systems. We describe common information extraction applications from open-source news articles and media sources in EBS systems, methods, value in public health, challenges and emerging developments.
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