Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study

AbstractObjectivesTo develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.DesignCross-sectionalSettingMulticenterParticipantsA total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. As of February 20, patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in the final analysis.InterventionCT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features extracted from pneumonia lesions in training and inter-validation datasets. The predictive performance was further evaluated in test dataset on lung lobe- and patients-level.Main outcomesShort-term hospital stay (≤10 days) and long-term hospital stay (>10 days).ResultsThe CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset.ConclusionsThe machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.

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Machine Learning Models of Survival Prediction in Trauma Patients

Journal of Clinical Medicine ◽

10.3390/jcm8060799 ◽

2019 ◽

Vol 8 (6) ◽

pp. 799 ◽

Cited By ~ 7

Author(s):

Cheng-Shyuan Rau ◽

Shao-Chun Wu ◽

Jung-Fang Chuang ◽

Chun-Ying Huang ◽

Hang-Tsung Liu ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Predictive Performance ◽

Original Data ◽

High Accuracy ◽

Validation Dataset ◽

Survival Prediction ◽

Trauma Patients ◽

Data Set ◽

Test Dataset

Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.

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QOLP-14. LONG-TERM IMPACT OF ADULT GLIOMA ON HEALTH-RELATED QUALITY OF LIFE: A SYSTEMATIC REVIEW

Neuro-Oncology ◽

10.1093/neuonc/noab196.735 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi185-vi186

Author(s):

Se Maria Frances ◽

Martin Klein ◽

Susan Short ◽

Louise Murray ◽

Galina Velikova ◽

...

Keyword(s):

Quantitative Methods ◽

Social Issues ◽

Final Analysis ◽

Primary Data ◽

Cognitive Changes ◽

Cross Sectional ◽

Who Grade ◽

Pilot Studies ◽

Wide Range

Abstract BACKGROUND Glioma diagnosis can be devastating, and results in a wide range of symptoms. Relatively little is known about the long-term challenges these symptoms pose on HRQOL. The aim of this review is to identify the long-term HRQOL issues reported at least two years following diagnosis of glioma. METHOD Systematic literature searches were carried out using Medline, EMBASE, CINAHL, PsycINFO, and Web of Science Core Collection. Searches were designed to identify a range of reported HRQOL aspects defined as physical, mental or social issues, in adult WHO grade II or III patients. To capture the full extent of patients’ experience, studies of any design reporting on primary data where patients had at least two years follow-up from diagnosis were included. WHO grade I and grade IV tumours were excluded due to their different prognoses and the expected nature of their disease trajectories. Narrative synthesis was used to collate findings. RESULTS The search returned 8438 articles. 477 titles remained after title and abstract screening, with seventeen full text articles included in the final analysis. The majority of studies used quantitative methods, with only two articles reporting qualitative or mixed methodology. Articles were predominantly cross-sectional studies (n = 9), along with cohort studies (n = 3), clinical trials (n = 3) and pilot studies (n = 2). Results indicated that patients reported a variety of issues influencing their HRQOL, with emotional/psychological/cognitive changes the most frequently reported. Physical complaints included problems with fatigue, seizures and maintaining daily activity. Social challenges included strained social relationships and issues managing finances. Patient coping strategies were found to significantly influence wellbeing and subsequent HRQOL. CONCLUSION Glioma patients’ long-term HRQOL and daily functioning can be impacted by their physical, mental and social wellbeing. Findings from this review lay the groundwork for efforts to improve patient long-term HRQOL.

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Prediction of the Side Effects Perceived by Therapists in Psychotherapy (Preprint)

10.2196/preprints.29338 ◽

2021 ◽

Author(s):

Lijun Yao ◽

Zhiwei Xu ◽

Xudong Zhao ◽

Yang Chen ◽

Liang Liu ◽

...

Keyword(s):

Machine Learning ◽

Side Effects ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Cross Sectional ◽

Psychological States ◽

Learning Classifier ◽

Psychological Activity ◽

Client Side ◽

Sectional Analysis

BACKGROUND Side effects in psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. OBJECTIVE Using machine learning techniques to distinguish therapists with and without the perception of consulting side effects, and identify the predictive factors of therapists who could perceive client side effects in psychotherapy. METHODS We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning–based algorithms were selected and trained by our dataset to build classification models. To make the prediction model interpretable, we leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. RESULTS Our study demonstrated the following: 1) Of the therapists, 316 perceived the side effects of the clients in the ongoing psychotherapy sessions, with a 59.6% incidence of side effects. Among all 7 perception types of the side effects, the most common was “make the clients or patients feel bad” (49.8%). 2) A random forest–based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients’ side effects, with an F1 score of 0.722 and an AUC value of 0.717. 3) When “therapists’ psychological activity” was considered a possible cause of the side effects in psychotherapy by the therapists, it was the most relevant feature for distinguishing the therapist category. CONCLUSIONS Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their own psychological states, was the most important factor in predicting the therapist's perception of the side effects of psychotherapy.

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PredictMed: A logistic regression–based model to predict health conditions in cerebral palsy

Health Informatics Journal ◽

10.1177/1460458219898568 ◽

2020 ◽

Vol 26 (3) ◽

pp. 2105-2118 ◽

Cited By ~ 1

Author(s):

Carlo M Bertoncelli ◽

Paola Altamura ◽

Edgar Ramos Vieira ◽

Sundaraja Sitharama Iyengar ◽

Federico Solla ◽

...

Keyword(s):

Logistic Regression ◽

Cerebral Palsy ◽

Sensitivity And Specificity ◽

Predictive Performance ◽

Health Condition ◽

Study Data ◽

Health Conditions ◽

Cross Sectional ◽

Independent Variables ◽

Children With Cerebral Palsy

Logistic regression–based predictive models are widely used in the healthcare field but just recently are used to predict comorbidities in children with cerebral palsy. This article presents a logistic regression approach to predict health conditions in children with cerebral palsy and a few examples from recent research. The model named PredictMed was trained, tested, and validated for predicting the development of scoliosis, intellectual disabilities, autistic features, and in the present study, feeding disorders needing gastrostomy. This was a multinational, cross-sectional descriptive study. Data of 130 children (aged 12–18 years) with cerebral palsy were collected between June 2005 and June 2015. The logistic regression–based model uses an algorithm implemented in R programming language. After splitting the patients in training and testing sets, logistic regressions are performed on every possible subset (tuple) of independent variables. The tuple that shows the best predictive performance in terms of accuracy, sensitivity, and specificity is chosen as a set of independent variables in another logistic regression to calculate the probability to develop the specific health condition (e.g. the need for gastrostomy). The average of accuracy, sensitivity, and specificity score was 90%. Our model represents a novelty in the field of some cerebral palsy–related health outcomes treatment, and it should significantly help doctors’ decision-making process regarding patient prognosis.

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Developing and validating a primary care EMR-based frailty definition using machine learning

International Journal for Population Data Science ◽

10.23889/ijpds.v5i1.1344 ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Tyler Williamson ◽

Sylvia Aponte-Hao ◽

Bria Mele ◽

Brendan Cord Lethebe ◽

Charles Leduc ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Sensitivity And Specificity ◽

Health Spending ◽

Case Definition ◽

Complex Nature ◽

Case Definitions ◽

Surveillance Network ◽

Clinical Frailty Scale ◽

Cross Sectional

Introduction. Individuals who have been identified as frail have an increased state of vulnerability, often leading to adverse health events, increased health spending, and potentially detrimental outcomes. Objective. The objective of this work is to develop and validate a case definition for frailty that can be used in a primary care electronic medical record database. Methods. This is a cross-sectional validation study using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Southern Alberta. 52 CPCSSN sentinels assessed a random sample of their own patients using the Rockwood Clinical Frailty scale, resulting in a total of 875 patients to be used as reference standard. Patients must be over the age of 65 and have had a clinic visit within the last 24 months. The case definition for frailty was developed using machine learning methods using CPCSSN records for the 875 patients. Results. Of the 875 patients, 155 (17.7%) were frail and 720 (84.2%) were not frail. Validation metrics of the case definition were: sensitivity and specificity of 0.28, 95% CI (0.21 to 0.36) and 0.94, 95% CI (0.93 to 0.96), respectively; PPV and NPV of 0.53, 95% CI (0.42 to 0.64) and 0.86, 95% CI (0.83 to 0.88), respectively. Conclusion. The low sensitivity and specificity results could be because frailty as a construct remains under-developed and relatively poorly understood due to its complex nature. These results contribute to the literature by demonstrating that case definitions for frailty require expert consensus and potentially more sophisticated algorithms to be successful

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A Machine Learning-based Approach to Identify Therapists who can Perceive Client-Side Effects in Psychotherapy

10.21203/rs.3.rs-1150073/v1 ◽

2021 ◽

Author(s):

Lijun Yao ◽

Zhiwei Xu ◽

Xudong Zhao ◽

Yang Chen ◽

Liang Liu ◽

...

Keyword(s):

Machine Learning ◽

Side Effects ◽

Predictive Performance ◽

Cross Sectional ◽

Psychological States ◽

Learning Classifier ◽

Auc Value ◽

Psychological Activity ◽

Client Side ◽

Sectional Analysis

Abstract Background: Side effects in psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. Methods: We designed the Psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients’ side effects. A number of features were selected to distinguish the therapists by category. Six machine learning–based algorithms were selected and trained by our dataset to build classification models. To make the prediction model interpretable, we leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories.Results: Our study demonstrated the following: 1) Of the therapists, 316 perceived the side effects of the clients in the ongoing psychotherapy sessions, with a 59.6% incidence of side effects. Among all 7 perception types of the side effects, the most common type was “make the clients or patients feel bad” (49.8%). 2) A random forest–based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients’ side effects, with an F1 score of 0.722 and an AUC value of 0.717. 3) When “therapists’ psychological activity” was considered a possible cause of the side effects in psychotherapy by the therapists, it was the most relevant feature for distinguishing the therapist category.Conclusions: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their own psychological states, was the most important factor in predicting the therapist's perception of the side effects of psychotherapy.

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Theoretical and Methodological Problems of a 10-Year Follow-Up Program Evaluation Study

European Journal of Psychological Assessment ◽

10.1027//1015-5759.18.3.229 ◽

2002 ◽

Vol 18 (3) ◽

pp. 229-241 ◽

Cited By ~ 6

Author(s):

Kurt A. Heller ◽

Ralph Reimann

Keyword(s):

Program Evaluation ◽

Evaluation Studies ◽

Evaluation Study ◽

Control Groups ◽

Cross Sectional ◽

Long Term Study ◽

Partial Inclusion ◽

Methodological Problems

Summary In this paper, conceptual and methodological problems of school program evaluation are discussed. The data were collected in conjunction with a 10 year cross-sectional/longitudinal investigation with partial inclusion of control groups. The experiences and conclusions resulting from this long-term study are revealing not only from the vantage point of the scientific evaluation of new scholastic models, but are also valuable for program evaluation studies in general, particularly in the field of gifted education.

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Long-term metabolic and socio educational outcomes of transient neonatal diabetes: A longitudinal and cross-sectional study

Yearbook of Paediatric Endocrinology ◽

10.1530/ey.17.2.9 ◽

2020 ◽

Author(s):

Le Bourgeois F ◽

B Beltrand J Baz ◽

JP Julla JB Riveline ◽

Simon A ◽

Flechtner I ◽

...

Keyword(s):

Educational Outcomes ◽

Neonatal Diabetes ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Transient Neonatal Diabetes

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Immuno-biological Assessments of Temporomandibular Joint Disease in Patients with Immune-mediated Rheumatic Conditions. A cross sectional study of 273 cases

Revista de Chimie ◽

10.37358/rc.17.12.6023 ◽

2018 ◽

Vol 68 (12) ◽

pp. 2987-2991

Author(s):

Cristina Iordache ◽

Bogdan Vascu ◽

Eugen Ancuta ◽

Rodica Chirieac ◽

Cristina Pomirleanu ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Psoriatic Arthritis ◽

Temporomandibular Joint ◽

Final Analysis ◽

Cross Sectional Study ◽

Joint Disease ◽

Sectional Study ◽

Cross Sectional ◽

Immune Parameters ◽

Immune Mediated

Temporomandibular joint (TMJ) is commonly involved in various immune-mediated rheumatic disorders accounting for significant disability and impaired quality of life. The aim of our study was to assess inflammatory and immune parameters in patients with TMJ arthritis related to rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA) and to identify potential relation with severity and dysfunction of TMJ pathology. We performed a cross-sectional study in a cohort of 433 consecutive RA, 32 JIA, 258 AS, and 103 PsA. Only patients presenting with clinically significant TMJ involvement (273) related to their rheumatic condition were included in the final analysis. TMJ involvement is traditionally described in chronic inflammatory rheumatic disorders, particularly in patients with higher levels of inflammation as detected in rheumatoid arthritis and psoriatic arthritis. Disease activity and severity, as well as biological and positive serological assessments (rheumatoid factor, anti-cyclic citrullinated peptide, IL-1) remain significant determinants of the severity of TMJ arthritis.

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