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Lee DY, Kim C, Lee S, Son SJ, Cho SM, Cho YH, Lim J, Park RW. Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods. Front Psychiatry 2022; 13:844442. [PMID: 35479497 PMCID: PMC9037331 DOI: 10.3389/fpsyt.2022.844442] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/09/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Identifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data. METHODS Clinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database. RESULTS A total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616. CONCLUSIONS We developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.
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Affiliation(s)
- Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Chungsoo Kim
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
| | - Seongwon Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, South Korea
| | - Sun-Mi Cho
- Department of Psychiatry, Ajou University School of Medicine, Suwon, South Korea
| | - Yong Hyuk Cho
- Department of Psychiatry, Ajou University School of Medicine, Suwon, South Korea
| | - Jaegyun Lim
- Department of Laboratory Medicine, Myongji Hospital, Hanyang University College of Medicine, Goyang, South Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
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Abbasgholizadeh Rahimi S, Légaré F, Sharma G, Archambault P, Zomahoun HTV, Chandavong S, Rheault N, T Wong S, Langlois L, Couturier Y, Salmeron JL, Gagnon MP, Légaré J. Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal. J Med Internet Res 2021; 23:e29839. [PMID: 34477556 PMCID: PMC8449300 DOI: 10.2196/29839] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. OBJECTIVE We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. METHODS We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. RESULTS We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). CONCLUSIONS We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.
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Affiliation(s)
- Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.,Mila-Quebec AI Institute, Montreal, QC, Canada
| | - France Légaré
- Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, QC, Canada.,VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada
| | - Gauri Sharma
- Faculty of Engineering, Dayalbagh Educational Institute, Agra, India
| | - Patrick Archambault
- Department of Family Medicine and Emergency Medicine, Université Laval, Quebec City, QC, Canada.,VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada
| | - Herve Tchala Vignon Zomahoun
- VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada.,Quebec SPOR-Support Unit, Quebec City, QC, Canada
| | - Sam Chandavong
- Faculty of Science and Engineering, Université Laval, Quebec City, QC, Canada
| | - Nathalie Rheault
- VITAM - Centre de recherche en santé durable, Université Laval, Quebec City, QC, Canada.,Quebec SPOR-Support Unit, Quebec City, QC, Canada
| | - Sabrina T Wong
- School of Nursing, University of British Columbia, Vancouver, BC, Canada.,Center for Health Services and Policy Research, University of British Columbia, Vancouver, BC, Canada
| | - Lyse Langlois
- Department of Industrial Relations, Université Laval, Quebec City, QC, Canada.,OBVIA - Quebec International Observatory on the social impacts of AI and digital technology, Quebec City, QC, Canada
| | - Yves Couturier
- School of Social Work, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Jose L Salmeron
- Department of Data Science, University Pablo de Olavide, Seville, Spain
| | | | - Jean Légaré
- Arthritis Alliance of Canada, Montreal, QC, Canada
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3
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Lineback CM, Garg R, Oh E, Naidech AM, Holl JL, Prabhakaran S. Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing. Front Neurol 2021; 12:649521. [PMID: 34326805 PMCID: PMC8315788 DOI: 10.3389/fneur.2021.649521] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 06/04/2021] [Indexed: 01/04/2023] Open
Abstract
Background and Purpose: This study aims to determine whether machine learning (ML) and natural language processing (NLP) from electronic health records (EHR) improve the prediction of 30-day readmission after stroke. Methods: Among index stroke admissions between 2011 and 2016 at an academic medical center, we abstracted discrete data from the EHR on demographics, risk factors, medications, hospital complications, and discharge destination and unstructured textual data from clinician notes. Readmission was defined as any unplanned hospital admission within 30 days of discharge. We developed models to predict two separate outcomes, as follows: (1) 30-day all-cause readmission and (2) 30-day stroke readmission. We compared the performance of logistic regression with advanced ML algorithms. We used several NLP methods to generate additional features from unstructured textual reports. We evaluated the performance of prediction models using a five-fold validation and tested the best model in a held-out test dataset. Areas under the curve (AUCs) were used to compare discrimination of each model. Results: In a held-out test dataset, advanced ML methods along with NLP features out performed logistic regression for all-cause readmission (AUC, 0.64 vs. 0.58; p < 0.001) and stroke readmission prediction (AUC, 0.62 vs. 0.52; p < 0.001). Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
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Affiliation(s)
- Christina M Lineback
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Ravi Garg
- Department of Neurology, Biological Sciences, Division and Center for Healthcare Delivery Science and Innovation, University of Chicago, Chicago, IL, United States
| | - Elissa Oh
- Department of Neurology, Biological Sciences, Division and Center for Healthcare Delivery Science and Innovation, University of Chicago, Chicago, IL, United States
| | - Andrew M Naidech
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.,Department of Neurology, Biological Sciences, Division and Center for Healthcare Delivery Science and Innovation, University of Chicago, Chicago, IL, United States
| | - Jane L Holl
- Department of Neurology, Biological Sciences, Division and Center for Healthcare Delivery Science and Innovation, University of Chicago, Chicago, IL, United States
| | - Shyam Prabhakaran
- Department of Neurology, University of Chicago, Chicago, IL, United States
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Vandenberg O, Durand G, Hallin M, Diefenbach A, Gant V, Murray P, Kozlakidis Z, van Belkum A. Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies. Clin Microbiol Rev 2020; 33:e00057-19. [PMID: 32102900 PMCID: PMC7048017 DOI: 10.1128/cmr.00057-19] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.
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Affiliation(s)
- Olivier Vandenberg
- Innovation and Business Development Unit, LHUB-ULB, Groupement Hospitalier Universitaire de Bruxelles (GHUB), Université Libre de Bruxelles, Brussels, Belgium
- Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom
| | - Géraldine Durand
- bioMérieux, Microbiology Research and Development, La Balme Les Grottes, France
| | - Marie Hallin
- Department of Microbiology, LHUB-ULB, Groupement Hospitalier Universitaire de Bruxelles (GHUB), Université Libre de Bruxelles, Brussels, Belgium
| | - Andreas Diefenbach
- Department of Microbiology, Infectious Diseases and Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Labor Berlin, Charité-Vivantes GmbH, Berlin, Germany
| | - Vanya Gant
- Department of Clinical Microbiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Patrick Murray
- BD Life Sciences Integrated Diagnostic Solutions, Scientific Affairs, Sparks, Maryland, USA
| | - Zisis Kozlakidis
- Laboratory Services and Biobank Group, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Alex van Belkum
- bioMérieux, Open Innovation and Partnerships, La Balme Les Grottes, France
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Perveen S, Shahbaz M, Ansari MS, Keshavjee K, Guergachi A. A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression. Front Genet 2020; 10:1076. [PMID: 31969896 PMCID: PMC6958689 DOI: 10.3389/fgene.2019.01076] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/09/2019] [Indexed: 12/31/2022] Open
Abstract
Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton’s Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.
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Affiliation(s)
- Sajida Perveen
- Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan
| | - Muhammad Shahbaz
- Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan.,Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, Canada
| | | | - Karim Keshavjee
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Aziz Guergachi
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, Canada.,Ted Rogers School of Information Technology Management, Ryerson University, Toronto, ON, Canada.,Department of Mathematics & Statistics, York University, Toronto, ON, Canada
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6
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Abstract
BACKGROUND With the development of e-Health, it plays a more and more important role in predicting whether a doctor's answer can be accepted by a patient through online healthcare community. Unlike the previous work which focus mainly on the numerical feature, in our framework, we combine both numerical and textual information to predict the acceptance of answers. The textual information is composed of questions posted by the patients and answers posted by the doctors. To extract the textual features from them, we first trained a sentence encoder to encode a pair of question and answer into a co-dependent representation on a held-out dataset. After that,we can use it to predict the acceptance of answers by doctors. RESULTS Our experimental results on the real-world dataset demonstrate that by applying our model additional features from text can be extracted and the prediction can be more accurate. That's to say, the model which take both textual features and numerical features as input performs significantly better than model which takes numerical features only on all the four metrics (Accuracy, AUC, F1-score and Recall). CONCLUSIONS This work proposes a generic framework combining numerical features and textual features for acceptance prediction, where textual features are extracted from text based on deep learning methods firstly and can be used to achieve a better prediction results.
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Affiliation(s)
- Qianlong Liu
- School of Data Science, Fudan University, Handan Road, Shanghai, China
- Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong, Hong Kong, China
| | - Kangenbei Liao
- School of Data Science, Fudan University, Handan Road, Shanghai, China
- Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong, Hong Kong, China
| | - Kelvin Kam-fai Tsoi
- Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong, Hong Kong, China
| | - Zhongyu Wei
- School of Data Science, Fudan University, Handan Road, Shanghai, China
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7
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Lorenzetti DL, Quan H, Lucyk K, Cunningham C, Hennessy D, Jiang J, Beck CA. Strategies for improving physician documentation in the emergency department: a systematic review. BMC Emerg Med 2018; 18:36. [PMID: 30558573 PMCID: PMC6297955 DOI: 10.1186/s12873-018-0188-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 10/12/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Physician chart documentation can facilitate patient care decisions, reduce treatment errors, and inform health system planning and resource allocation activities. Although accurate and complete patient chart data supports quality and continuity of patient care, physician documentation often varies in terms of timeliness, legibility, clarity and completeness. While many educational and other approaches have been implemented in hospital settings, the extent to which these interventions can improve the quality of documentation in emergency departments (EDs) is unknown. METHODS We conducted a systematic review to assess the effectiveness of approaches to improve ED physician documentation. Peer reviewed electronic databases, grey literature sources, and reference lists of included studies were searched to March 2015. Studies were included if they reported on outcomes associated with interventions designed to enhance the quality of physician documentation. RESULTS Nineteen studies were identified that report on the effectiveness of interventions to improve physician documentation in EDs. Interventions included audit/feedback, dictation, education, facilitation, reminders, templates, and multi-interventions. While ten studies found that audit/feedback, dictation, pharmacist facilitation, reminders, templates, and multi-pronged approaches did improve the quality of physician documentation across multiple outcome measures, the remaining nine studies reported mixed results. CONCLUSIONS Promising approaches to improving physician documentation in emergency department settings include audit/feedback, reminders, templates, and multi-pronged education interventions. Future research should focus on exploring the impact of implementing these interventions in EDs with and without emergency medical record systems (EMRs), and investigating the potential of emerging technologies, including EMR-based machine-learning, to promote improvements in the quality of ED documentation.
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Affiliation(s)
- Diane L Lorenzetti
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada.
| | - Hude Quan
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada
| | - Kelsey Lucyk
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada
| | - Ceara Cunningham
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada
| | - Deirdre Hennessy
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada
| | - Jason Jiang
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada
| | - Cynthia A Beck
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada
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