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Shalom E, Goldstein A, Weiss R, Selivanova M, Cohen NM, Shahar Y. Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital. J Biomed Inform 2024; 156:104686. [PMID: 38977257 DOI: 10.1016/j.jbi.2024.104686] [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: 02/17/2024] [Revised: 06/23/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND The increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency. OBJECTIVES 1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs). 2) Assess the system's technical feasibility and functional capability for senior nurse use in geriatric pressure-ulcer management. METHODS A computational QA system using our Quality Assessment Temporal Patterns (QATP) methodology was designed and implemented. Our methodology transforms the GL's procedural-knowledge into declarative-knowledge temporal-abstraction patterns representing the expected execution trace in the patient's data for correct therapy application. Fuzzy temporal logic allows for partial compliance, reflecting individual and grouped action performance considering their values and temporal aspects. The system was tested using a pressure ulcer treatment GL and data from 100 geriatric patients' Electronic Medical Records (EMR). After technical evaluation for accuracy and feasibility, an extensive functional evaluation was conducted by an experienced nurse, comparing QA scores with and without system support, and versus automated system scores. Time efficiency was also measured. RESULTS QA scores from the geriatric nurse, with and without system's support, did not significantly differ from those provided by the automated system (p < 0.05), demonstrating the effectiveness and reliability of both manual and automated methods. The system-supported manual QA process reduced scoring time by approximately two-thirds, from an average of 17.3 min per patient manually to about 5.9 min with the system's assistance, highlighting the system's efficiency potential in clinical practice. CONCLUSION The QA system based on QATP, produces scores consistent with an experienced nurse's assessment for complex care over extended periods. It enables quick and accurate quality care evaluation for multiple patients after brief training. Such automated QA systems may empower nursing staff, enabling them to manage more patients, accurately and consistently, while reducing costs due to saved time and effort, and enhanced compliance with evidence-based guidelines.
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Affiliation(s)
- Erez Shalom
- The Medical Informatics Research Center, Department of Software and Information System Engineering, Ben Gurion University of the Negev, Israel
| | | | - Rony Weiss
- Herzfeld Geriatric Rehabilitation Medical Center, Clalit Health Services, Israel
| | - Maya Selivanova
- Herzfeld Geriatric Rehabilitation Medical Center, Clalit Health Services, Israel
| | - Nogah Melamed Cohen
- The Medical Informatics Research Center, Department of Software and Information System Engineering, Ben Gurion University of the Negev, Israel
| | - Yuval Shahar
- The Medical Informatics Research Center, Department of Software and Information System Engineering, Ben Gurion University of the Negev, Israel
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2
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Tan KR, Seng JJB, Kwan YH, Chen YJ, Zainudin SB, Loh DHF, Liu N, Low LL. Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review. J Diabetes Sci Technol 2023; 17:474-489. [PMID: 34727783 PMCID: PMC10012374 DOI: 10.1177/19322968211056917] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
BACKGROUND With the rising prevalence of diabetes, machine learning (ML) models have been increasingly used for prediction of diabetes and its complications, due to their ability to handle large complex data sets. This study aims to evaluate the quality and performance of ML models developed to predict microvascular and macrovascular diabetes complications in an adult Type 2 diabetes population. METHODS A systematic review was conducted in MEDLINE®, Embase®, the Cochrane® Library, Web of Science®, and DBLP Computer Science Bibliography databases according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Studies that developed or validated ML prediction models for microvascular or macrovascular complications in people with Type 2 diabetes were included. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC). An AUC >0.75 indicates clearly useful discrimination performance, while a positive mean relative AUC difference indicates better comparative model performance. RESULTS Of 13 606 articles screened, 32 studies comprising 87 ML models were included. Neural networks (n = 15) were the most frequently utilized. Age, duration of diabetes, and body mass index were common predictors in ML models. Across predicted outcomes, 36% of the models demonstrated clearly useful discrimination. Most ML models reported positive mean relative AUC compared with non-ML methods, with random forest showing the best overall performance for microvascular and macrovascular outcomes. Majority (n = 31) of studies had high risk of bias. CONCLUSIONS Random forest was found to have the overall best prediction performance. Current ML prediction models remain largely exploratory, and external validation studies are required before their clinical implementation. PROTOCOL REGISTRATION Open Science Framework (registration number: 10.17605/OSF.IO/UP49X).
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Affiliation(s)
| | | | - Yu Heng Kwan
- MOH Holdings Private Ltd.,
Singapore
- Health Services & Systems Research,
Duke-NUS Medical School, Singapore
- Department of Pharmacy, Faculty of
Science, National University of Singapore, Singapore
| | | | | | | | - Nan Liu
- Health Services & Systems Research,
Duke-NUS Medical School, Singapore
- Health Services Research Centre,
Singapore Health Services, Singapore
- Institute of Data Science, National
University of Singapore, Singapore
| | - Lian Leng Low
- SingHealth Regional Health System,
Singapore Health Services, Singapore
- Department of Family Medicine and
Continuing Care, Singapore General Hospital, Singapore
- SingHealth Duke-NUS Family Medicine
Academic Clinical Program, SingHealth Duke-NUS Academic Medical Centre,
Singapore
- Lian Leng Low, MMed(FM), FCFP(Singapore),
MCI, Department of Family Medicine and Continuing Care, Singapore General
Hospital, Outram Road, Singapore 169608.
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3
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Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results. INFORMATICS 2022. [DOI: 10.3390/informatics9010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada.
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4
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Khasnabish S, Burns Z, Couch M, Mullin M, Newmark R, Dykes PC. Best practices for data visualization: creating and evaluating a report for an evidence-based fall prevention program. J Am Med Inform Assoc 2021; 27:308-314. [PMID: 31697326 DOI: 10.1093/jamia/ocz190] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 09/08/2019] [Accepted: 10/06/2019] [Indexed: 11/14/2022] Open
Abstract
This case report applied principles from the data visualization (DV) literature and feedback from nurses to develop an effective report to display adherence with an evidence-based fall prevention program. We tested the usability of the original and revised reports using a Health Information Technology Usability Evaluation Scale (Health-ITUES) customized for this project. Items were rated on a 5-point Likert scale, strongly disagree (1) to strongly agree (5). The literature emphasized that the ideal display maximizes the information communicated, minimizes the cognitive efforts involved with interpretation, and selects the correct type of display (eg, bar versus line graph). Semi-structured nurse interviews emphasized the value of simplified reports and meaningful data. The mean (standard deviation [SD]) Health-ITUES score for the original report was 3.86 (0.19) and increased to 4.29 (0.11) in the revised report (Mann Whitney U Test, z = -12.25, P < 0.001). Lessons learned from this study can inform report development for clinicians in implementation science.
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Affiliation(s)
- Srijesa Khasnabish
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Zoe Burns
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Madeline Couch
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Mary Mullin
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Randall Newmark
- Research Computing, Partners Healthcare, Boston, Massachusetts, USA
| | - Patricia C Dykes
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Center for Patient Safety Research and Practice, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
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5
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Abstract
The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.
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6
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Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records. INFORMATICS 2020. [DOI: 10.3390/informatics7020017] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs.
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7
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Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3. DATA 2020. [DOI: 10.3390/data5020033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.
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8
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Feller DJ, Burgermaster M, Levine ME, Smaldone A, Davidson PG, Albers DJ, Mamykina L. A visual analytics approach for pattern-recognition in patient-generated data. J Am Med Inform Assoc 2019; 25:1366-1374. [PMID: 29905826 PMCID: PMC6188507 DOI: 10.1093/jamia/ocy054] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 04/18/2018] [Indexed: 11/30/2022] Open
Abstract
Objective To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.
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Affiliation(s)
- Daniel J Feller
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | | | - Matthew E Levine
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Arlene Smaldone
- Columbia University School of Nursing and College of Dental Medicine, Columbia University Medical Center, New York, NY, USA
| | | | - David J Albers
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Lena Mamykina
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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9
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Bellei EA, Biduski D, Lisboa HRK, De Marchi ACB. Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus. Telemed J E Health 2019; 26:205-217. [PMID: 30724717 DOI: 10.1089/tmj.2018.0329] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background: In the daily routine of type 1 diabetes mellitus (T1DM), the patients deal with many data and consider many variables to perform actions, decisions, and regimen adjustments. There is a need to apply filtering techniques to extract relevant information and provide appropriate data visualization methods to assist in clinical tasks and decision making. Objective: To present Soins DM, a mobile health tool, for monitoring the linkage among treatment factors of T1DM with an interactive data visualization approach. Methods: First, we performed a literature review, a commercial search, and ideation. Next, we created a prototype and an online survey for its feedback, with participation of 76 individuals. Afterward, the mobile app and its website version were built. Eventually, we conducted a pilot experiment with 4 patients, an online experiment for satisfaction assessment with 97 patients, and an online assessment by 9 health professionals. Results: Prototyping and feedback facilitated the design refinement. Soins DM enables the recording of data from routines of glycemia, insulin applications, meals, and physical exercises. From these logs, the app builds two different ways of interactive data visualization, a timeline and an integrated chart, providing personalized feedback on bad glycemia with its possible causes. The assessments revealed overall satisfaction with the app's characteristics. Conclusions: Soins DM is a novel application with interactive visualization and personalized feedback for easy identification of the linkage among treatment factors of T1DM. The test scenario with patients and health professionals indicates Soins DM as a useful and reliable tool.
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Affiliation(s)
- Ericles Andrei Bellei
- Graduate Program in Applied Computing, Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, Brazil
| | - Daiana Biduski
- Graduate Program in Applied Computing, Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, Brazil
| | - Hugo Roberto Kurtz Lisboa
- IMED Medical School, Passo Fundo, Brazil.,Teaching Hospital, São Vicente de Paulo's Hospital, Passo Fundo, Brazil
| | - Ana Carolina Bertoletti De Marchi
- Graduate Program in Applied Computing, Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, Brazil.,Graduate Program in Human Aging, College of Physical Education and Physiotherapy, University of Passo Fundo, Passo Fundo, Brazil
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10
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Bhattacharya M, Jurkovitz C, Shatkay H. Chronic Kidney Disease stratification using office visit records: Handling data imbalance via hierarchical meta-classification. BMC Med Inform Decis Mak 2018; 18:125. [PMID: 30537962 PMCID: PMC6290512 DOI: 10.1186/s12911-018-0675-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. In its advanced forms it carries severe outcomes and can lead to death. It is thus important to detect the disease as early as possible, which can help devise effective intervention and treatment plan. Here we investigate ways to utilize information available in electronic health records (EHRs) from regular office visits of more than 13,000 patients, in order to distinguish among several stages of the disease. While clinical data stored in EHRs provide valuable information for risk-stratification, one of the major challenges in using them arises from data imbalance. That is, records associated with a more severe condition are typically under-represented compared to those associated with a milder manifestation of the disease. To address imbalance, we propose and develop a sampling-based ensemble approach, hierarchical meta-classification, aiming to stratify CKD patients into severity stages, using simple quantitative non-text features gathered from standard office visit records. Methods The proposed hierarchical meta-classification method frames the multiclass classification task as a hierarchy of two subtasks. The first is binary classification, separating records associated with the majority class from those associated with all minority classes combined, using meta-classification. The second subtask separates the records assigned to the combined minority classes into the individual constituent classes. Results The proposed method identifies a significant proportion of patients suffering from the more advanced stages of the condition, while also correctly identifying most of the less severe cases, maintaining high sensitivity, specificity and F-measure (≥ 93%). Our results show that the high level of performance attained by our method is preserved even when the size of the training set is significantly reduced, demonstrating the stability and generalizability of our approach. Conclusion We present a new approach to perform classification while addressing data imbalance, which is inherent in the biomedical domain. Our model effectively identifies severity stages of CKD patients, using information readily available in office visit records within the realistic context of high data imbalance.
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Affiliation(s)
- Moumita Bhattacharya
- Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA.
| | | | - Hagit Shatkay
- Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA.,Center for Bioinformatics and Computational Biology, Delaware Biotechnology Inst, University of Delaware, Newark, DE, USA
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11
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Litchfield I, Hoye C, Shukla D, Backman R, Turner A, Lee M, Weber P. Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol. BMJ Open 2018; 8:e019947. [PMID: 30518578 PMCID: PMC6286474 DOI: 10.1136/bmjopen-2017-019947] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 10/04/2018] [Accepted: 11/01/2018] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as 'process mining' has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. METHODS AND ANALYSIS The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes. ETHICS AND DISSEMINATION Ethical approval has been provided by East Midlands-Leicester South Regional Ethics Committee (REC reference 18/EM/0284). Having refined the automated production of maps of care processes, we can explore pinch points and bottlenecks, process variants and unexpected behaviour, and make informed recommendations to improve the quality and efficiency of care. The results of this study will be submitted for publication in peer-reviewed journals.
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Affiliation(s)
- Ian Litchfield
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ciaron Hoye
- Digital Transformation, Birmingham Solihull Clinical Commissioning Group, Birmingham, UK
| | - David Shukla
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ruth Backman
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Alice Turner
- University Hospitals Birmingham NHS Foundation Trust and Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Mark Lee
- School of Computer Science, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK
| | - Phil Weber
- School of Engineering and Applied Science, System Analytics for Innovation, Aston University, Birmingham, UK
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12
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Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, De Cata P, Chiovato L, Bellazzi R. Machine Learning Methods to Predict Diabetes Complications. J Diabetes Sci Technol 2018; 12:295-302. [PMID: 28494618 PMCID: PMC5851210 DOI: 10.1177/1932296817706375] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.
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Affiliation(s)
- Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
| | - Simone Marini
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
| | - Lucia Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
| | - Giulia Cogni
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
| | - Marsida Teliti
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
| | | | | | - Luca Chiovato
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Centre for Health Technologies, University of Pavia, Pavia, Italy
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy
- Riccardo Bellazzi, Università’ degli Studi di Pavia, Via Ferrata 1, Pavia, 27100 Italy.
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13
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Shknevsky A, Shahar Y, Moskovitch R. Consistent discovery of frequent interval-based temporal patterns in chronic patients' data. J Biomed Inform 2017; 75:83-95. [PMID: 28987378 DOI: 10.1016/j.jbi.2017.10.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Revised: 08/23/2017] [Accepted: 10/02/2017] [Indexed: 11/24/2022]
Abstract
Increasingly, frequent temporal patterns discovered in longitudinal patient records are proposed as features for classification and prediction, and as means to cluster patient clinical trajectories. However, to justify that, we must demonstrate that most frequent temporal patterns are indeed consistently discoverable within the records of different patient subsets within similar patient populations. We have developed several measures for the consistency of the discovery of temporal patterns. We focus on time-interval relations patterns (TIRPs) that can be discovered within different subsets of the same patient population. We expect the discovered TIRPs (1) to be frequent in each subset, (2) preserve their "local" metrics - the absolute frequency of each pattern, measured by a Proportion Test, and (3) preserve their "global" characteristics - their overall distribution, measured by a Kolmogorov-Smirnov test. We also wanted to examine the effect on consistency, over a variety of settings, of varying the minimal frequency threshold for TIRP discovery, and of using a TIRP-filtering criterion that we previously introduced, the Semantic Adjacency Criterion (SAC). We applied our methodology to three medical domains (oncology, infectious hepatitis, and diabetes). We found that, within the minimal frequency ranges we had examined, 70-95% of the discovered TIRPs were consistently discoverable; 40-48% of them maintained their local frequency. TIRP global distribution similarity varied widely, from 0% to 65%. Increasing the threshold usually increased the percentage of TIRPs that were repeatedly discovered across different patient subsets within the same domain, and the probability of a similar TIRP distribution. Using the SAC principle, enhanced, for most minimal support levels, the percentage of repeating TIRPs, their local consistency and their global consistency. The effect of using the SAC was further strengthened as the minimal frequency threshold was raised.
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Affiliation(s)
- Alexander Shknevsky
- Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva, Israel.
| | - Yuval Shahar
- Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva, Israel.
| | - Robert Moskovitch
- Software and Information Systems Engineering, Ben-Gurion University, Beer Sheva, Israel.
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Deja R, Froelich W, Deja G, Wakulicz-Deja A. Hybrid approach to the generation of medical guidelines for insulin therapy for children. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.07.066] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Kostakis O, Papapetrou P. On searching and indexing sequences of temporal intervals. Data Min Knowl Discov 2017. [DOI: 10.1007/s10618-016-0489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Lismont J, Janssens AS, Odnoletkova I, Vanden Broucke S, Caron F, Vanthienen J. A guide for the application of analytics on healthcare processes: A dynamic view on patient pathways. Comput Biol Med 2016; 77:125-34. [PMID: 27544069 DOI: 10.1016/j.compbiomed.2016.08.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 07/27/2016] [Accepted: 08/05/2016] [Indexed: 10/21/2022]
Abstract
OBJECTIVE The aim of this study is to guide healthcare instances in applying process analytics on healthcare processes. Process analytics techniques can offer new insights in patient pathways, workflow processes, adherence to medical guidelines and compliance with clinical pathways, but also bring along specific challenges which will be examined and addressed in this paper. METHODS The following methodology is proposed: log preparation, log inspection, abstraction and selection, clustering, process mining, and validation. It was applied on a case study in the type 2 diabetes mellitus domain. RESULTS Several data pre-processing steps are applied and clarify the usefulness of process analytics in a healthcare setting. Healthcare utilization, such as diabetes education, is analyzed and compared with diabetes guidelines. Furthermore, we take a look at the organizational perspective and the central role of the GP. This research addresses four challenges: healthcare processes are often patient and hospital specific which leads to unique traces and unstructured processes; data is not recorded in the right format, with the right level of abstraction and time granularity; an overflow of medical activities may cloud the analysis; and analysts need to deal with data not recorded for this purpose. These challenges complicate the application of process analytics. It is explained how our methodology takes them into account. CONCLUSION Process analytics offers new insights into the medical services patients follow, how medical resources relate to each other and whether patients and healthcare processes comply with guidelines and regulations.
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Affiliation(s)
- Jasmien Lismont
- KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium.
| | - Anne-Sophie Janssens
- KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium
| | - Irina Odnoletkova
- KU Leuven, Dept. of Public Health, Kapucijnenvoer 35, B-3000 Leuven, Belgium
| | - Seppe Vanden Broucke
- KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium
| | - Filip Caron
- KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium
| | - Jan Vanthienen
- KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium
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Perer A, Wang F, Hu J. Mining and exploring care pathways from electronic medical records with visual analytics. J Biomed Inform 2015; 56:369-78. [PMID: 26146159 DOI: 10.1016/j.jbi.2015.06.020] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 05/13/2015] [Accepted: 06/26/2015] [Indexed: 11/28/2022]
Abstract
OBJECTIVE In order to derive data-driven insights, we develop Care Pathway Explorer, a system that mines and visualizes a set of frequent event sequences from patient EMR data. The goal is to utilize historical EMR data to extract common sequences of medical events such as diagnoses and treatments, and investigate how these sequences correlate with patient outcome. MATERIALS AND METHODS The Care Pathway Explorer uses a frequent sequence mining algorithm adapted to handle the real-world properties of EMR data, including techniques for handling event concurrency, multiple levels-of-detail, temporal context, and outcome. The mined patterns are then visualized in an interactive user interface consisting of novel overview and flow visualizations. RESULTS We use the proposed system to analyze the diagnoses and treatments of a cohort of hyperlipidemic patients with hypertension and diabetes pre-conditions, and demonstrate the clinical relevance of patterns mined from EMR data. The patterns that were identified corresponded to clinical and published knowledge, some of it unknown to the physician at the time of discovery. CONCLUSION Care Pathway Explorer, which combines frequent sequence mining techniques with advanced visualizations supports the integration of data-driven insights into care pathway discovery.
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Affiliation(s)
- Adam Perer
- IBM T.J. Watson Research Center, 1101 Kitchawan Road, P.O. Box 218, Yorktown Heights, NY 10598, USA.
| | - Fei Wang
- University of Connecticut, Storrs, CT, USA
| | - Jianying Hu
- IBM T.J. Watson Research Center, 1101 Kitchawan Road, P.O. Box 218, Yorktown Heights, NY 10598, USA
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Abstract
The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.
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Affiliation(s)
- Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy IRCCS Fondazione S. Maugeri, Pavia, Italy
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Lucia Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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