Journal of Personalized Medicine

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14 pages, 693 KiB

Open AccessFeature PaperArticle

Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network

by Nina R. Sperber, Olivia M. Dong, Megan C. Roberts, Paul Dexter, Amanda R. Elsey, Geoffrey S. Ginsburg, Carol R. Horowitz, Julie A. Johnson, Kenneth D. Levy, Henry Ong, Josh F. Peterson, Toni I. Pollin, Tejinder Rakhra-Burris, Michelle A. Ramos, Todd Skaar and Lori A. Orlando

J. Pers. Med. 2021, 11(7), 647; https://doi.org/10.3390/jpm11070647 - 8 Jul 2021

Cited by 17 | Viewed by 4615

Abstract

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In [...] Read more.

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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10 pages, 410 KiB

Open AccessArticle

Machine Learning Approaches for Predicting Bisphosphonate-Related Osteonecrosis in Women with Osteoporosis Using VEGFA Gene Polymorphisms

by Jin-Woo Kim, Jeong Yee, Sang-Hyeon Oh, Sun-Hyun Kim, Sun-Jong Kim, Jee-Eun Chung and Hye-Sun Gwak

J. Pers. Med. 2021, 11(6), 541; https://doi.org/10.3390/jpm11060541 - 10 Jun 2021

Cited by 13 | Viewed by 2712

Abstract

Objective: This nested case–control study aimed to investigate the effects of VEGFA polymorphisms on the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ) in women with osteoporosis. Methods: Eleven single nucleotide polymorphisms (SNPs) of the VEGFA were assessed in a total of 125 [...] Read more.

Objective: This nested case–control study aimed to investigate the effects of VEGFA polymorphisms on the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ) in women with osteoporosis. Methods: Eleven single nucleotide polymorphisms (SNPs) of the VEGFA were assessed in a total of 125 patients. Logistic regression was performed for multivariable analysis. Machine learning algorithms, namely, fivefold cross-validated multivariate logistic regression, elastic net, random forest, and support vector machine, were developed to predict risk factors for BRONJ occurrence. Area under the receiver-operating curve (AUROC) analysis was conducted to assess clinical performance. Results: The VEGFA rs881858 was significantly associated with BRONJ development. The odds of BRONJ development were 6.45 times (95% CI, 1.69–24.65) higher among carriers of the wild-type rs881858 allele compared with variant homozygote carriers after adjusting for covariates. Additionally, variant homozygote (GG) carriers of rs10434 had higher odds than those with wild-type allele (OR, 3.16). Age ≥ 65 years (OR, 16.05) and bisphosphonate exposure ≥ 36 months (OR, 3.67) were also significant risk factors for BRONJ occurrence. AUROC values were higher than 0.78 for all machine learning methods employed in this study. Conclusion: Our study showed that the BRONJ occurrence was associated with VEGFA polymorphisms in osteoporotic women. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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13 pages, 2715 KiB

Open AccessArticle

Multi-Institutional Implementation of Clinical Decision Support for APOL1, NAT2, and YEATS4 Genotyping in Antihypertensive Management

by Thomas M. Schneider, Michael T. Eadon, Rhonda M. Cooper-DeHoff, Kerri L. Cavanaugh, Khoa A. Nguyen, Meghan J. Arwood, Emma M. Tillman, Victoria M. Pratt, Paul R. Dexter, Allison B. McCoy, Lori A. Orlando, Stuart A. Scott, Girish N. Nadkarni, Carol R. Horowitz and Joseph L. Kannry

J. Pers. Med. 2021, 11(6), 480; https://doi.org/10.3390/jpm11060480 - 27 May 2021

Cited by 8 | Viewed by 2982

Abstract

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the [...] Read more.

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus; 2. actionability; 3. context-sensitive triggers; 4. workflow integration; 5. feasibility; 6. interpretability; 7. portability; and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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15 pages, 4406 KiB

Open AccessArticle

Structured Integration and Alignment Algorithm: A Tool for Personalized Surgical Treatment of Tibial Plateau Fractures

by Flaviu Moldovan, Adrian Gligor and Tiberiu Bataga

J. Pers. Med. 2021, 11(3), 190; https://doi.org/10.3390/jpm11030190 - 10 Mar 2021

Cited by 26 | Viewed by 2455

Abstract

The planning of the surgical treatment in orthopedics, with the help of three-dimensional (3D) technologies, arouses an increasing scientific interest. Scientific literature describes some semi-automatic reconstructive attempts at fragmented bone fractures, but the matching algorithms presented are likely to improve. The aim of [...] Read more.

The planning of the surgical treatment in orthopedics, with the help of three-dimensional (3D) technologies, arouses an increasing scientific interest. Scientific literature describes some semi-automatic reconstructive attempts at fragmented bone fractures, but the matching algorithms presented are likely to improve. The aim of this paper is to develop a new method of aligning fragments of comminutive fractures. We have created a structured integration process and an alignment algorithm integrated in a clinical workflow for personalized surgical treatment of fractures. The provided solution is able to align the surfaces of bone fragments derived from the segmentation process of volumetric tomographic data. Positional uncertainties are eliminated interactively by the user, who selects the corresponding pairs of fracture surfaces. The final matching and the right alignment are performed automatically by the innovative alignment algorithm. The paper solves a challenging problem for the reconstruction of fractured bones, namely the choice of the optimal matching option from the situation in which surface portions of a fracture fragment correspond to multiple high fragments. The method is validated in practice for preoperative planning of a 49-year-old male patient who had a tibial plateau fracture of Schatzker type VI. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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14 pages, 3125 KiB

Open AccessArticle

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data

by Eunchong Huang, Sarah Kim and TaeJin Ahn

J. Pers. Med. 2021, 11(2), 128; https://doi.org/10.3390/jpm11020128 - 15 Feb 2021

Cited by 7 | Viewed by 3304

Abstract

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is [...] Read more.

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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12 pages, 1007 KiB

Open AccessArticle

Breast Cancer Type Classification Using Machine Learning

by Jiande Wu and Chindo Hicks

J. Pers. Med. 2021, 11(2), 61; https://doi.org/10.3390/jpm11020061 - 20 Jan 2021

Cited by 125 | Viewed by 14560

Abstract

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive [...] Read more.

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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14 pages, 501 KiB

Open AccessFeature PaperArticle

Validation of the Italian Version of the Educational Needs Assessment Tool in Rheumatoid Arthritis Patients and Factors Associated with Educational Needs

by Marta Favero, Francesca Ometto, Fausto Salaffi, Elisa Belluzzi, Augusta Ortolan, Mariagrazia Lorenzin, Mara Felicetti, Leonardo Punzi, Mwidimi Ndosi and Roberta Ramonda

J. Pers. Med. 2020, 10(4), 150; https://doi.org/10.3390/jpm10040150 - 1 Oct 2020

Cited by 4 | Viewed by 2277

Abstract

The educational needs assessment tool (ENAT) is a seven-domain questionnaire assessing the educational needs (EN) of patients with rheumatoid arthritis (RA). The aim of this study was to validate the Italian version of the ENAT and to identify factors associated with EN in [...] Read more.

The educational needs assessment tool (ENAT) is a seven-domain questionnaire assessing the educational needs (EN) of patients with rheumatoid arthritis (RA). The aim of this study was to validate the Italian version of the ENAT and to identify factors associated with EN in people with RA. The original English ENAT version was translated into Italian according to Beaton’s method and subjected to Rasch analysis for validity testing. Socio-demographic and clinical variables were tested for associations with the ENAT domain scores using a multivariable linear regression model. The ENAT translated well into Italian and retained its construct validity. Some adjustments were needed when pooling the Italian and English datasets. The overall score of the ENAT had a high median: 82.8 (interquartile range (IQR): 57.5 to 100) i.e., 72.4% of the maximum score. The highest score was observed in the domain “Arthritis process” and the lowest was in “Support systems”. Only gender was independently associated with EN (females having higher EN than males). The Italian ENAT is feasible for the use in the clinical setting and may help the health care practitioners to tailor educational interventions for RA patients. The characteristics of the patients, particularly female gender, may be associated with higher EN. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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11 pages, 504 KiB

Open AccessArticle

A Closer Look at the “Right” Format for Clinical Decision Support: Methods for Evaluating a Storyboard BestPractice Advisory

by Brian J. Douthit, R. Clayton Musser, Kay S. Lytle and Rachel L. Richesson

J. Pers. Med. 2020, 10(4), 142; https://doi.org/10.3390/jpm10040142 - 23 Sep 2020

Cited by 6 | Viewed by 3922

Abstract

(1) Background: The five rights of clinical decision support (CDS) are a well-known framework for planning the nuances of CDS, but recent advancements have given us more options to modify the format of the alert. One-size-fits-all assessments fail to capture the nuance of [...] Read more.

(1) Background: The five rights of clinical decision support (CDS) are a well-known framework for planning the nuances of CDS, but recent advancements have given us more options to modify the format of the alert. One-size-fits-all assessments fail to capture the nuance of different BestPractice Advisory (BPA) formats. To demonstrate a tailored evaluation methodology, we assessed a BPA after implementation of Storyboard for changes in alert fatigue, behavior influence, and task completion; (2) Methods: Data from 19 weeks before and after implementation were used to evaluate differences in each domain. Individual clinics were evaluated for task completion and compared for changes pre- and post-redesign; (3) Results: The change in format was correlated with an increase in alert fatigue, a decrease in erroneous free text answers, and worsened task completion at a system level. At a local level, however, 14% of clinics had improved task completion; (4) Conclusions: While the change in BPA format was correlated with decreased performance, the changes may have been driven primarily by the COVID-19 pandemic. The framework and metrics proposed can be used in future studies to assess the impact of new CDS formats. Although the changes in this study seemed undesirable in aggregate, some positive changes were observed at the level of individual clinics. Personalized implementations of CDS tools based on local need should be considered. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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12 pages, 1218 KiB

Open AccessArticle

Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned

by Myung Woo, Brooke Alhanti, Sam Lusk, Felicia Dunston, Stephen Blackwelder, Kay S. Lytle, Benjamin A. Goldstein and Armando Bedoya

J. Pers. Med. 2020, 10(3), 104; https://doi.org/10.3390/jpm10030104 - 27 Aug 2020

Cited by 2 | Viewed by 4038

Abstract

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive [...] Read more.

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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11 pages, 2004 KiB

Open AccessArticle

Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool

by David Gallagher, Congwen Zhao, Amanda Brucker, Jennifer Massengill, Patricia Kramer, Eric G. Poon and Benjamin A. Goldstein

J. Pers. Med. 2020, 10(3), 103; https://doi.org/10.3390/jpm10030103 - 26 Aug 2020

Cited by 19 | Viewed by 5873

Abstract

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. [...] Read more.

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient’s risk for readmission. We report on the implementation and monitoring of the Epic electronic health record—“Unplanned readmission model version 1”—over 2 years from 1/1/2018–12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716–0.760 for all patients and 0.676–0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217–0.248 for all patients. We also present our methods in monitoring the model over time for trend changes, as well as common readmissions reduction strategies triggered by the score. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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22 pages, 1177 KiB

Open AccessArticle

Application of Machine Learning Techniques to Analyze Patient Returns to the Emergency Department

by Antonio Sarasa Cabezuelo

J. Pers. Med. 2020, 10(3), 81; https://doi.org/10.3390/jpm10030081 - 7 Aug 2020

Cited by 10 | Viewed by 3369

Abstract

The study of the quality of hospital emergency services is based on analyzing a set of indicators such as the average time of first medical attention, the average time spent in the emergency department, degree of completion of the medical report and others. [...] Read more.

The study of the quality of hospital emergency services is based on analyzing a set of indicators such as the average time of first medical attention, the average time spent in the emergency department, degree of completion of the medical report and others. In this paper, an analysis is presented of one of the quality indicators: the rate of return of patients to the emergency service less than 72 h from their discharge. The objective of the analysis was to know the variables that influence the rate of return and which prediction model is the best. In order to do this, the data of the activity of the emergency service of a hospital of a reference population of 290,000 inhabitants were analyzed, and prediction models were created for the binary objective variable (rate of return to emergencies) using the logistic regression techniques, neural networks, random forest, gradient boosting and assembly models. Each of the models was analyzed and the result shows that the best model is achieved through a neural network with activation function tanh, algorithm levmar and three nodes in the hidden layer. This model obtains the lowest mean squared error (MSE) and the best area under the curve (AUC) with respect to the rest of the models used. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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24 pages, 1405 KiB

Open AccessArticle

Automatic Labeled Dialogue Generation for Nursing Record Systems

by Tittaya Mairittha, Nattaya Mairittha and Sozo Inoue

J. Pers. Med. 2020, 10(3), 62; https://doi.org/10.3390/jpm10030062 - 16 Jul 2020

Cited by 4 | Viewed by 3983

Abstract

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user [...] Read more.

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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30 pages, 3279 KiB

Open AccessArticle

A Research on the Classification and Applicability of the Mobile Health Applications

by Ivan Miguel Pires, Gonçalo Marques, Nuno M. Garcia, Francisco Flórez-Revuelta, Vasco Ponciano and Salome Oniani

J. Pers. Med. 2020, 10(1), 11; https://doi.org/10.3390/jpm10010011 - 27 Feb 2020

Cited by 76 | Viewed by 15288

Abstract

Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare [...] Read more.

Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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Review

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11 pages, 603 KiB

Open AccessReview

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

by Gopi Battineni, Getu Gamo Sagaro, Nalini Chinatalapudi and Francesco Amenta

J. Pers. Med. 2020, 10(2), 21; https://doi.org/10.3390/jpm10020021 - 31 Mar 2020

Cited by 205 | Viewed by 21258

Abstract

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently [...] Read more.

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future. Full article

(This article belongs to the Special Issue Use of Clinical Decision Support Software within Health Care Systems)

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