The Psychedelic Future of Post-Traumatic Stress Disorder Treatment

Current Neuropharmacology, Год журнала: 2024, Номер 22(4), С. 636 - 735

Опубликована: Янв. 5, 2024

Post-traumatic stress disorder (PTSD) is a mental health condition that can occur following exposure to traumatic experience. An estimated 12 million U.S. adults are presently affected by this disorder. Current treatments include psychological therapies (e.g., exposure-based interventions) and pharmacological selective serotonin reuptake inhibitors (SSRIs)). However, significant proportion of patients receiving standard-of-care for PTSD remain symptomatic, new approaches other trauma-related conditions greatly needed. Psychedelic compounds alter cognition, perception, mood currently being examined their efficacy in treating despite current status as Drug Enforcement Administration (DEA)- scheduled substances. Initial clinical trials have demonstrated the potential value psychedelicassisted therapy treat psychiatric disorders. In comprehensive review, we summarize state science care, including shortcomings. We review studies psychedelic interventions PTSD, disorders, common comorbidities. The classic psychedelics psilocybin, lysergic acid diethylamide (LSD), N,N-dimethyltryptamine (DMT) DMT-containing ayahuasca, well entactogen 3,4-methylenedioxymethamphetamine (MDMA) dissociative anesthetic ketamine, reviewed. For each drug, present history use, somatic effects, pharmacology, safety profile. rationale proposed mechanisms use traumarelated disorders discussed. This concludes with an in-depth consideration future directions applications maximize therapeutic benefit minimize risk individuals communities impacted conditions.

Язык: Английский

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IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 11

Опубликована: Май 18, 2022

Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose noninvasive self-care system based on the IoT machine learning (ML) analyses blood sugar other key indicators early. The main purpose this work develop enhanced management which in patient monitoring technology-assisted decision-making. proposed hybrid ensemble ML model predicts mellitus by combining both bagging boosting methods. An online IoT-based application offline questionnaire 15 questions about health, family history, lifestyle were used recruit total 10221 study. For datasets, experimental findings suggest our outperforms state-of-the-art techniques.

Язык: Английский

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An examination of the hybrid meta-heuristic machine learning algorithms for early diagnosis of type II diabetes using big data feature selection

Healthcare Analytics, Год журнала: 2023, Номер 4, С. 100227 - 100227

Опубликована: Июль 14, 2023

People are increasingly getting type II diabetes mellitus (T2DM) due to unhealthy food styles, decreased outdoor activities caused by the COVID-19 pandemic, and unawareness of risk factors. This disease is hidden in early stages causes many comorbidities like fatty liver, heart disease, peripheral artery disease. study presents several hybrid algorithms diagnose T2DM its without requiring expensive time-consuming medical tests. We first apply feature selection using Particle Swarm Optimization (PSO) algorithm reduce required computations. Meta-heuristics used developed hierarchical optimize hyperparameters machine learning for classification. A comparative analysis with performance metrics shows Genetic Algorithm-Support Vector Machine (GA-SVM) has largest area under Receiver Operating Characteristic (ROC) curve (0.934) better most (Accuracy 0.934 F1- Measure 0.945) reasonable metaheuristic computational time. Therefore, GA-SVM recommended clinical decision support systems. diagnoses at responding questions about 93% accuracy, which can help patients survive future complications through lifestyle intervention therapy.

Язык: Английский

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A practical framework for early detection of diabetes using ensemble machine learning models

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, Год журнала: 2023, Номер 31(4), С. 722 - 738

Опубликована: Июль 1, 2023

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been growing effort to develop intelligent diagnostic systems diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such remains significant challenge. Recent advancements in ensemble ML methods offer promising opportunities early detection as they are known be faster and more cost-effective than traditional approaches. Therefore, this study proposes practical framework diagnosing that involves three stages. data preprocessing stage encompasses several tasks, including handling missing values, identifying outliers, balancing the data, normalizing selecting relevant features. Subsequently, hyperparameters algorithms fine-tuned grid search improve their performance. final stage, employs techniques bagging, boosting, stacking combine multiple further enhance predictive capability. Pima Indians Diabetes Database open-access dataset was used test performance proposed models. experimental results indicate superiority compared individual method achieved best among methods, with stacked random forest (RF) support vector (SVM) model attaining an 97.50%. Among bagging RF yielded highest accuracy, while boosting eXtreme Gradient Boosting (XGB) 97.20% 97.10%, respectively. Moreover, our outperforms other models confirmed by comparison. demonstrated accurate diagnosis, enabling through efficient calibrated

Язык: Английский

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What is meant by ‘integrated personalized diabetes management’: A view into the future and what success should look like

Diabetes Obesity and Metabolism, Год журнала: 2024, Номер 26(S1), С. 14 - 29

Опубликована: Фев. 8, 2024

Abstract Integrated personalized diabetes management (IPDM) has emerged as a promising approach to improving outcomes in patients with mellitus (DM). This care emphasizes the integration and coordination of different providers, including physicians, nurses, dietitians, social workers pharmacists. The goal IPDM is provide that tailored their needs. review addresses concept integrated use technology (including data, software applications artificial intelligence) well managerial, regulatory financial aspects. implementation upscaling digitally enabled are discussed, elaboration successful practices related evidence. Finally, recommendations made. It concluded adoption on global level inevitable, considering challenges created by an increasing prevalence DM need for better improvement health system sustainability.

Язык: Английский

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Artificial intelligence model for tumoral clinical decision support systems

Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 253, С. 108228 - 108228

Опубликована: Май 23, 2024

Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center compare similar cases when new patient is evaluated. By leveraging Artificial Intelligence models, proposed system able retrieving most tumors for given query. The primary objective enhance process by generating more accurate representations images, with particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies its ability produce enriched image descriptors solely binary information, eliminating need costly difficult obtain segmentation.

Язык: Английский

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Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights

Information, Год журнала: 2024, Номер 16(1), С. 7 - 7

Опубликована: Дек. 26, 2024

Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability healthcare professionals. Using the Pima Indian dataset, we developed an ensemble 85.01% accuracy leveraging AutoGluon’s AutoML framework. To address “black-box” nature of machine learning, applied XAI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Integrated Gradients (IG), Attention Mechanism (AM), Counterfactual Analysis (CA), providing both patient-specific insights into critical factors such as glucose BMI. These methods enable transparent actionable predictions, supporting clinical decision-making. An interactive Streamlit application was allow clinicians explore feature importance test hypothetical scenarios. Cross-validation confirmed model’s robust performance across diverse datasets. demonstrates integration pathway achieving accurate, interpretable models foster transparency trust while decisions.

Язык: Английский

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Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0307718 - e0307718

Опубликована: Янв. 8, 2025

Diabetes, a chronic metabolic condition characterised by persistently high blood sugar levels, necessitates early detection to mitigate its risks. Inadequate dietary choices can contribute various health complications, emphasising the importance of personalised nutrition interventions. However, real-time selection diets tailored individual nutritional needs is challenging because intricate nature foods and abundance sources. Because diabetes condition, patients with this illness must choose healthy diet. Patients frequently need visit their doctor rely on expensive medications manage condition. It purchase medication for illnesses regular basis in underdeveloped nations. Motivated concept, we suggest hybrid model that, rather than depending solely evade doctor, first anticipate then diet exercise regimen. This research proposes an optimized approach harnessing machine learning classifiers, including Random Forest, Support Vector Machine, XGBoost, develop robust framework accurate prediction. The study addresses difficulties predicting precisely from limited labeled data outliers datasets. Furthermore, thorough food recommender system unveiled, offering individualized health-conscious recommendations based user preferences medical information. Leveraging efficient inference techniques, achieves meager error rate less 30% using extensive dataset comprising over 100 million user-rated foods. underscores significance integrating classifiers personalized enhance prediction management. proposed has substantial potential facilitate detection, provide guidance, alleviate economic burden associated diabetes-related healthcare expenses.

Язык: Английский

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A Unified Care Delivery Model: Integration of Remote Patient Monitoring and Ambulatory Care for Diabetes and Hypertension Management in the U.S.

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

The prevalence of diabetes mellitus (DM) and hypertension (HTN) continues to rise in the U.S. with an aging population, suboptimal diet, insufficient physical activity. Critical components effective management, such as continuous home monitoring blood pressure (BP) glucose (BG), timely data sharing for clinical decision support, lifestyle improvement, medication adherence, are often inadequate between routine primary care physician (PCP) or endocrinologist follow-up visits. Patients uncontrolled DM HTN continue experience preventable complications increased spending costs healthcare system. This chapter summarizes adoption remote patient (RPM) care, spotlights original research from innovative Unified Care program that integrates RPM onsite team doctor’s office online, app-based health coaching service: a seamless experience. has demonstrated average reduction 11.9 mmHg/−6.3 mmHg over 12 months among Stage II Hypertension patients, glycosylated hemoglobin (HbA1c) 1.4% 6 patients baseline HbA1c. These results show potential unified model beyond managing large population chronic diseases more effectively.

Язык: Английский

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Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2568 - e2568

Опубликована: Фев. 3, 2025

Self-awareness and self-management in diabetes are critical as they enhance patient well-being, decrease financial burden, alleviate strain on healthcare systems by mitigating complications promoting healthier life expectancy. Incomplete understanding persists regarding the synergistic effects of diet exercise management, existing research often isolates these factors, creating a knowledge gap comprehending their combined influence. Current overlooks interplay between self-management. A holistic study is crucial to mitigate burdens effectively. Multi-dimensional questions covering complete diabetic management such publication channels for research, machine learning solutions, physical activity tacking methods, diabetic-associated datasets included this research. In study, using proper protocol primary articles related diet, exercise, datasets, blood analysis selected quality assessed management. This interrelates two major dimensions together that exercise.

Язык: Английский

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