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Bibi I, Schaffert D, Blauth M, Lull C, von Ahnen JA, Gross G, Weigandt WA, Knitza J, Kuhn S, Benecke J, Leipe J, Schmieder A, Olsavszky V. Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study. J Med Internet Res 2023; 25:e50886. [PMID: 38015608 PMCID: PMC10716771 DOI: 10.2196/50886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. OBJECTIVE We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. METHODS After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. RESULTS Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. CONCLUSIONS This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.
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Affiliation(s)
- Igor Bibi
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Daniel Schaffert
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Mara Blauth
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Christian Lull
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Jan Alwin von Ahnen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Georg Gross
- Department of Medicine V, Division of Rheumatology, University Medical Centre and Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wanja Alexander Weigandt
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Johannes Knitza
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Sebastian Kuhn
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Johannes Benecke
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
| | - Jan Leipe
- Department of Medicine V, Division of Rheumatology, University Medical Centre and Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Astrid Schmieder
- Department of Dermatology, Venereology, and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Victor Olsavszky
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Center of Excellence in Dermatology, Heidelberg University, Mannheim, Germany
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Chou A, Torres-Espin A, Kyritsis N, Huie JR, Khatry S, Funk J, Hay J, Lofgreen A, Shah R, McCann C, Pascual LU, Amorim E, Weinstein PR, Manley GT, Dhall SS, Pan JZ, Bresnahan JC, Beattie MS, Whetstone WD, Ferguson AR. Correction: Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. PLoS One 2023; 18:e0294081. [PMID: 37917637 PMCID: PMC10621810 DOI: 10.1371/journal.pone.0294081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0265254.].
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Kapoor D, Xu C. Spinal Cord Injury AIS Predictions Using Machine Learning. eNeuro 2023; 10:ENEURO. [PMID: 36543536 DOI: 10.1523/ENEURO.0149-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 10/21/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
The study used machine learning to predict The American Spinal Injury Association Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital discharge time from hospital admission data. Additionally, machine learning was used to analyze the best model for feature importance to validate the criticality of the AIS score and highlight relevant demographic details. The data used for training machine learning models was from the National Spinal Cord Injury Statistical Center (NSCISC) database of U.S. spinal cord injury patient details. Eighteen real features were used from 417 provided features, which mapped to 53 machine learning features after processing. Eight models were tuned on the dataset to predict AIS scores, and Shapely analysis was performed to extract the most important of the 53 features. Patients within the NSCISC database who sustained injuries were between 1972 and 2016 after data cleaning (n = 20,790). Outcomes were test set multiclass accuracy and aggregated Shapely score magnitudes. Ridge Classifier was the best performer with 73.6% test set accuracy. AIS scores and neurologic category at the time of admission were the best predictors of recovery. Demographically, features were less important, but age, sex, marital status, and race stood out. AIS scores on admission are highly predictive of patient outcomes when combined with patient demographic data. Promising results in terms of predicting recovery were seen, and Shapely analysis allowed for the machine learning model to be probed as a whole, giving insight into overall feature trends.
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Dietz N, Vaitheesh Jaganathan, Alkin V, Mettille J, Boakye M, Drazin D. Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review. J Clin Orthop Trauma 2022; 35:102046. [PMID: 36425281 PMCID: PMC9678757 DOI: 10.1016/j.jcot.2022.102046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/23/2022] [Accepted: 10/18/2022] [Indexed: 11/19/2022] Open
Abstract
Background Machine learning has been applied to improve diagnosis and prognostication of acute traumatic spinal cord injury. We investigate potential for clinical integration of machine learning in this patient population to navigate variability in injury and recovery. Materials and methods We performed a systematic review using PRISMA guidelines through PubMed database to identify studies that use machine learning algorithms for clinical application toward improvements in diagnosis, management, and predictive modeling. Results Of the 132 records identified, a total of 13 articles met inclusion criteria and were included in final analysis. Of the 13 articles, 5 focused on diagnostic accuracy and 8 were related to prognostication or management of traumatic spinal cord injury. Across studies, 1983 patients with spinal cord injury were evaluated with most classifying as ASIA C or D. Retrospective designs were used in 10 of 13 studies and 3 were prospective. Studies focused on MRI evaluation and segmentation for diagnostic accuracy and prognostication, investigation of mean arterial pressure in acute care and intraoperative settings, prediction of ambulatory and functional ability, chronic complication prevention, and psychological quality of life assessments. Decision tree, random forests (RF), support vector machines (SVM), hierarchical cluster tree analysis (HCTA), artificial neural networks (ANN), convolutional neural networks (CNN) machine learning subtypes were used. Conclusions Machine learning represents a platform technology with clinical application in traumatic spinal cord injury diagnosis, prognostication, management, rehabilitation, and risk prevention of chronic complications and mental illness. SVM models showed improved accuracy when compared to other ML subtypes surveyed. Inherent variability across patients with SCI offers unique opportunity for ML and personalized medicine to drive desired outcomes and assess risks in this patient population.
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Affiliation(s)
- Nicholas Dietz
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | - Vaitheesh Jaganathan
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | | | - Jersey Mettille
- Department of Anesthesia, University of Louisville, Louisville, KY, USA
| | - Maxwell Boakye
- Department of Neurosurgery, University of Louisville, 200 Abraham Flexner Hwy, Louisville, KY, 40202, USA
| | - Doniel Drazin
- Department of Neurosurgery, Providence Regional Medical Center Everett, Everett, WA, USA
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