Validation of Machine Learning-Based Individualized Treatment for Depressive Disorder Using Target Trial Emulation

This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan’s National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate.

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Machine Learning-Based Personalized Pharmacological Treatment for Depressive Disorder: A Target Trial Emulation Study (Preprint)

10.2196/preprints.29652 ◽

2021 ◽

Author(s):

Chi-Shin Wu ◽

Albert C. Yang ◽

Shu-Sen Chang ◽

Chia-Ming Chang ◽

Yi-Hung Liu ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Depressive Disorder ◽

Treatment Failure ◽

Pharmacological Treatment ◽

Initial Treatment ◽

Prediction Models ◽

Research Database ◽

Super Learner ◽

Personalised Treatment

BACKGROUND Developing personalized treatment is one way to improve treatment effectiveness for depressive disorder. OBJECTIVE This study was aimed to develop and validate the use of machine learning-based prediction models to select personalized pharmacological treatment for patients with depressive disorder. METHODS This study used Taiwan's National Health Insurance Research Database. Patients with diagnoses of depressive disorder between 2003 and 2012 were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalisation, self-harm hospitalisation, emergency visits, or treatment change. Predictors included the patients’ demographic variables, clinical characteristics of depression, and medical and psychiatric comorbid conditions. Prediction models based on Super Learner algorithms were trained for the initial and the next-step treatment separately. The personalised treatment strategy was developed for choosing the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effect of personalised treatment. RESULTS The areas under the curve of the prediction model using Super Learner was 0·627 for the initial treatment and 0·751 for the next-step treatment. Patients treated with model-selected regimens had reduced treatment failure rates, with a 0·84-fold (95% confidence interval [CI] 0·82-0·86) decrease for the initial treatment and a 0·82-fold (95% CI 0·80-0·83) decrease for the next-step treatment. CONCLUSIONS Machine learning-based prediction models for depression outcomes had fair prediction accuracies. In emulating clinical trials, we found the model-selected regimen to be associated with a reduced treatment failure rate. Future randomised controlled trials should be conducted to investigate the effectiveness of the use of machine-learning algorithms in clinical practice.

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Prediction of recurrence-associated death from localized prostate cancer with a charlson comorbidity index–reinforced machine learning model

Open Medicine ◽

10.1515/med-2019-0067 ◽

2019 ◽

Vol 14 (1) ◽

pp. 593-606 ◽

Cited By ~ 4

Author(s):

Yi-Ting Lin ◽

Michael Tian-Shyug Lee ◽

Yen-Chun Huang ◽

Chih-Kuang Liu ◽

Yi-Tien Li ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Charlson Comorbidity Index ◽

Prediction Models ◽

External Beam Radiotherapy ◽

Hazard Model ◽

Localized Prostate Cancer ◽

Support Vector ◽

Research Database ◽

Comorbidity Index

AbstractResearch has failed to resolve the dilemma experienced by localized prostate cancer patients who must choose between radical prostatectomy (RP) and external beam radiotherapy (RT). Because the Charlson Comorbidity Index (CCI) is a measurable factor that affects survival events, this research seeks to validate the potential of the CCI to improve the accuracy of various prediction models. Thus, we employed the Cox proportional hazard model and machine learning methods, including random forest (RF) and support vector machine (SVM), to model the data of medical records in the National Health Insurance Research Database (NHIRD). In total, 8581 individuals were enrolled, of whom 4879 had received RP and 3702 had received RT. Patients in the RT group were older and exhibited higher CCI scores and higher incidences of some CCI items. Moderate-to-severe liver disease, dementia, congestive heart failure, chronic pulmonary disease, and cerebrovascular disease all increase the risk of overall death in the Cox hazard model. The CCI-reinforced SVM and RF models are 85.18% and 81.76% accurate, respectively, whereas the SVM and RF models without the use of the CCI are relatively less accurate, at 75.81% and 74.83%, respectively. Therefore, CCI and some of its items are useful predictors of overall and prostate-cancer-specific survival and could constitute valuable features for machine-learning modeling.

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Machine Learning Prediction Models for Chronic Kidney Disease using National Health Insurance Claim Data in Taiwan

10.1101/2020.06.25.20139147 ◽

2020 ◽

Author(s):

Surya Krishnamurthy ◽

Kapeleshh KS ◽

Erik Dovgan ◽

Mitja Luštrek ◽

Barbara Gradišek Piletič ◽

...

Keyword(s):

Machine Learning ◽

Chronic Kidney Disease ◽

Health Insurance ◽

Kidney Disease ◽

National Health Insurance ◽

National Health ◽

Performance Metrics ◽

Prediction Models ◽

Research Database ◽

Number Of Patients

ABSTRACTBackground and ObjectiveChronic kidney disease (CKD) represent a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data, obtained from Taiwan’s National Health Insurance Research Database, to forecast whether an individual will develop CKD within the next 6 or 12 months, and thus forecast the prevalence in the population.MethodsA total of 18,000 people with CKD and 72,000 people without CKD diagnosis along with the past two years of medication and comorbidity data matched by propensity score were used to build a predicting model. A series of approaches were tested, including Convoluted Neural Networks (CNN). 5-fold cross-validation was used to assess the performance metrics of the algorithms.ResultsBoth for the 6 month and 12-month models, the CNN approach performed best, with the AUROC of 0.957 and 0.954, respectively. The most prominent features in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides, angiotensins which had an impact on the progression of CKD.ConclusionsThe model proposed in this study can be a useful tool for the policy-makers helping them in predicting the trends of CKD in the population in the next 6 to 12 months. Information provided by this model can allow closely monitoring the people with risk, early detection of CKD, better allocation of resources, and patient-centric management

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Treatment of Psychiatric Diseases with General Anaesthetics

Oxford Textbook of Neuroscience and Anaesthesiology ◽

10.1093/med/9780198746645.003.0028 ◽

2019 ◽

pp. 315-322

Author(s):

Laszlo Vutskits

Keyword(s):

Clinical Trials ◽

Major Depressive Disorder ◽

Mental Disorders ◽

Depressive Disorder ◽

Depressive Disorders ◽

Psychiatric Diseases ◽

Therapeutic Role ◽

Ordinary Life ◽

Behavioural Patterns ◽

Antidepressant Effects

Psychiatric disorders are defined as mental or behavioural patterns causing either suffering or a poor ability to function in ordinary life. These conditions are generally characterized by a combination of abnormal thoughts, perceptions, emotions, behaviour, and relationships with others. The burden of mental disorders continues to grow and the estimated global lifetime prevalence of these pathological states is estimated to reach over one-third of the population worldwide. Depression, one of the most common psychiatric diseases, is difficult to treat. This chapter on the treatment of psychiatric diseases with general anaesthetics includes discussion of major depressive disorder and its epidemiology and treatments, clinical trials suggesting a therapeutic role for anaesthesia in patients with depressive disorders, rapid antidepressant effects of ketamine, and mechanisms of actions underlying the antidepressant effects of anaesthetics.

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Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

Scientific Reports ◽

10.1038/s41598-021-83911-9 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Guogen Shan ◽

Charles Bernick ◽

Jessica Z. K. Caldwell ◽

Aaron Ritter

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Prediction Models ◽

Demographic Data ◽

Feature Selection Method ◽

Model Performance ◽

Screening Tests ◽

Disease Stage ◽

Preclinical Ad ◽

Domain Scores

AbstractAmyloid-$$\beta$$ β (A$$\beta$$ β ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A$$\beta$$ β status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A$$\beta$$ β status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.

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Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan

Healthcare ◽

10.3390/healthcare9050546 ◽

2021 ◽

Vol 9 (5) ◽

pp. 546

Author(s):

Surya Krishnamurthy ◽

Kapeleshh KS ◽

Erik Dovgan ◽

Mitja Luštrek ◽

Barbara Gradišek Piletič ◽

...

Keyword(s):

Machine Learning ◽

Chronic Kidney Disease ◽

Health Insurance ◽

Kidney Disease ◽

National Health Insurance ◽

National Health ◽

Prediction Models ◽

Research Database ◽

Close Monitoring ◽

Number Of Patients

Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Taiwan’s National Health Insurance Research Database to forecast the occurrence of CKD within the next 6 or 12 months before its onset, and hence its prevalence in the population. A total of 18,000 people with CKD and 72,000 people without CKD diagnosis were selected using propensity score matching. Their demographic, medication and comorbidity data from their respective two-year observation period were used to build a predictive model. Among the approaches investigated, the Convolutional Neural Networks (CNN) model performed best with a test set AUROC of 0.957 and 0.954 for the 6-month and 12-month predictions, respectively. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The models can allow close monitoring of people at risk, early detection of CKD, better allocation of resources, and patient-centric management.

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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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COVID-19 Outbreak Prediction with Machine Learning

10.34055/osf.io/xr4js ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220130232 ◽

2019 ◽

Vol 21 (9) ◽

pp. 662-669 ◽

Cited By ~ 1

Author(s):

Junnan Zhao ◽

Lu Zhu ◽

Weineng Zhou ◽

Lingfeng Yin ◽

Yuchen Wang ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Regression Tree ◽

Large Data ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

Current Diabetes Reviews ◽

10.2174/1573399816666200511003357 ◽

2020 ◽

Vol 16 ◽

Author(s):

Nitigya Sambyal ◽

Poonam Saini ◽

Rupali Syal

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Clinical Medicine ◽

Microvascular Complications ◽

Descriptive Analysis ◽

Machine Learning Techniques ◽

World Health ◽

Public Health Issue ◽

Learning Techniques ◽

Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

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