scholarly journals Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Shijun Yang ◽  
Bin Wang ◽  
Xiong Han
Keyword(s):  
Antiepileptic Drugs ◽  
Statistical Models ◽  
Prediction Models ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Treatment Withdrawal ◽  
Patient Treatment ◽  
Recent Developments ◽  
Progression Of Disease ◽  
Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

External validation and comparison of two prediction models for seizure recurrence after the withdrawal of antiepileptic drugs in adult patients

Epilepsia ◽  
2019 ◽  
Vol 61 (1) ◽  
pp. 115-124 ◽  
Author(s):  
Jiahe Lin ◽  
Siqi Ding ◽  
Xueying Li ◽  
Yingjie Hua ◽  
Xinshi Wang ◽  
...  
Keyword(s):  
Antiepileptic Drugs ◽  
Prediction Models ◽  
External Validation ◽  
Adult Patients ◽  
Seizure Recurrence

scholarly journals A machine learning tool for interpreting differences in cognition using brain features

2019 ◽  
Author(s):  
Tiago Azevedo ◽  
Luca Passamonti ◽  
Pietro Lió ◽  
Nicola Toschi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Public Repository ◽  
Cognitive Differences ◽  
Health Measures ◽  
Human Connectome Project ◽  
Neuroimaging Data ◽  
Recent Developments ◽  
Surface Based Morphometry

AbstractPredicting variability in cognition traits is an attractive and challenging area of research, where different approaches and datasets have been implemented with mixed results. Some powerful Machine Learning algorithms employed before are difficult to interpret, while other algorithms are easy to interpret but might not be as powerful. To improve understanding of individual cognitive differences in humans, we make use of the most recent developments in Machine Learning in which powerful prediction models can be interpreted with confidence. We used neuroimaging data and a variety of behavioural, cognitive, affective and health measures from 905 people obtained from the Human Connectome Project (HCP). As a main contribution of this paper, we show how one could interpret the neuroanatomical basis of cognition, with recent methods which we believe are not yet fully explored in the field. By reducing neuroimages to a well characterised set of features generated from surface-based morphometry and cortical myelin estimates, we make the interpretation of such models easier as each feature is self-explanatory. The code used in this tool is available in a public repository: https://github.com/tjiagoM/interpreting-cognition-paper-2019

Predicting Non-Routine Discharge After Elective Spine Surgery: External Validation of Machine Learning Algorithms Using Institutional Data

Neurosurgery ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E270-E270
Author(s):  
Brittany M Stopa ◽  
Faith C Robertson ◽  
Aditya Karhade ◽  
Melissa Chua ◽  
Marike L D Broekman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spine Surgery ◽  
Prediction Models ◽  
Lumbar Disc ◽  
Tertiary Care ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Multidisciplinary Teams ◽  
Institutional Data ◽  
Home Discharge

Abstract INTRODUCTION Non-routine discharge after elective spine surgery negatively impacts patient outcomes and increases healthcare costs. Preoperative prediction in this population could improve discharge planning. We previously developed an algorithm that predicts non-home discharge after elective spine surgery. Here, we validate our algorithm in an institutional population of neurosurgical spine patients from a Transitional Care Program (TCP) at an academic, tertiary care center. METHODS Medical records from elective inpatient surgery for lumbar disc herniation or degeneration in the TCP program (2013–2015) were retrospectively reviewed. Variables included age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels and discharge disposition. The discrimination and calibration of the previous algorithm was assessed in the independent sample, using Python 3.0 software. RESULTS A total of 144 patients underwent elective inpatient surgery for lumbar disc disorders with a non-home discharge rate of 10 (7%). The algorithm generalized well to the institutional data with c-statistic 0.89, calibration slope 1.09 and calibration intercept −0.08. This was comparable to performance in the derivation cohort and substantiates initial use of this algorithm in clinical practice. Prospective validation of this algorithm and comparison to other existing discharge prediction models is ongoing. CONCLUSION This institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying lumbar disc disorder patients at risk for non-routine discharge. This tool can be used by multidisciplinary teams of case management and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.

scholarly journals Calibration: the Achilles heel of predictive analytics

BMC Medicine ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Ben Van Calster ◽  
◽  
David J. McLernon ◽  
Maarten van Smeden ◽  
Laure Wynants ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Predictive Analytics ◽  
External Validation ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Model Complexity ◽  
Patient Counseling ◽  
Flexible Machine

Abstract Background The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Conclusion Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.

scholarly journals Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5864
Author(s):  
Qiang Wang ◽  
Changfeng Li ◽  
Jiaxing Zhang ◽  
Xiaojun Hu ◽  
Yingfang Fan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Methodological Quality ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Quality Score ◽  
Risk Of Bias ◽  
Microvascular Invasion ◽  
Patient Treatment ◽  
Preoperative Prediction

Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.

scholarly journals Using the OHDSI network to develop and externally validate a patient-level prediction model for Heart Failure in Type II Diabetes Mellitus

2021 ◽  
Author(s):  
Ross D. Williams ◽  
Jenna M. Reps ◽  
Jan A Kors ◽  
Patrick B Ryan ◽  
Ewout Steyerberg ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
High Performance ◽  
Prediction Models ◽  
External Validation ◽  
The United States ◽  
The Other ◽  
Patient Treatment ◽  
Administrative Claims ◽  
Second Line

AbstractIntroductionHeart Failure (HF) and Type 2 Diabetes Mellitus (T2DM) frequently coexist and exacerbate symptoms of each other. Treatments are available for T2DM that also provide beneficial treatment effects for HF. Guidelines recommend that patients with HF should be given Sodium-glucose co-transporter-2 inhibitors in preference to other second-line treatments for T2DM. Increasing personalization of treatment means that patients who have or are at risk of HF receive a customised treatment. We aimed to develop and externally validate prediction models to predict the 1-year risk of incident HF in T2DM patients starting second-line treatment.MethodsWe analysed a federated network of electronic medical records and administrative claims data from five databases (CCAE, MDCD, MDCR, Optum Clinformatics and Optum EHR) in the United States. We used each database to develop a model to predict 1-year risk of incident HF in patients initialising a second pharmaceutical intervention, following initial treatment with metformin for T2DM. We then performed internal validation for each model as well as external validation using the other databases.ResultsA total of 403,187 patients were included in the study. We developed 5 models with discrimination ranging from 0.72 to 0.80 at external validation in the other databases. Consistent high performance was noted for models developed in CCAE, Optum Clinformatics and Optum EHR with AUCs ranging from 0.74 to 0.81. For these models, calibration was acceptable.ConclusionThree high-performing prediction models were developed for this problem. The CCAE developed model was selected for recommendation as it achieved the same discrimination and better calibration performance than the Optum Clinformatics and Optum EHR models, whilst selecting fewer covariates and as such was selected as the best developed model. The models could be useful in stratifying patient treatment, planning healthcare utilization and reducing cost by aiding in increasing preparedness of healthcare providers.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Retrospective Study ◽  
Random Forest ◽  
Precocious Puberty ◽  
Prediction Models ◽  
Central Precocious Puberty ◽  
Machine Learning Algorithms ◽  
Stimulation Test ◽  
Gnrh Analogue ◽  
Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

scholarly journals Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Ming Chu ◽  
Huan-Ming Hsu ◽  
Chi-Wen Chang ◽  
Yuan-Kuei Li ◽  
Yu-Jia Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Models ◽  
Reference Model ◽  
External Validation ◽  
Risk Scores ◽  
Consistent Finding ◽  
Hazard Ratios ◽  
Genetic Predictors ◽  
Novel Biomarkers ◽  
Cox Proportional Hazard Regression

AbstractGenetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

scholarly journals Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e045572
Author(s):  
Andreas Daniel Meid ◽  
Ana Isabel Gonzalez-Gonzalez ◽  
Truc Sophia Dinh ◽  
Jeanet Blom ◽  
Marjan van den Akker ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Absolute Risk ◽  
External Validation ◽  
Baseline Risk ◽  
Study Heterogeneity ◽  
Related Quality ◽  
Individual Participant ◽  
Risk Predictor

ObjectiveTo explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients.Study design and settingUsing individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV).ResultsPrior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions.ConclusionsPredictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully.Trial registration numberPROSPERO id: CRD42018088129.

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