The Psychedelic Future of Post-Traumatic Stress Disorder Treatment

Current Neuropharmacology, Journal Year: 2024, Volume and Issue: 22(4), P. 636 - 735

Published: Jan. 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.

Language: Английский

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

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 11

Published: May 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.

Language: 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, Journal Year: 2023, Volume and Issue: 4, P. 100227 - 100227

Published: July 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.

Language: 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, Journal Year: 2023, Volume and Issue: 31(4), P. 722 - 738

Published: July 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

Language: Английский

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

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0307718 - e0307718

Published: Jan. 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.

Language: 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, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 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.

Language: 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, Journal Year: 2025, Volume and Issue: 11, P. e2568 - e2568

Published: Feb. 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.

Language: Английский

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Digital health technologies and innovation patterns in diabetes ecosystems

Digital Health, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 1, 2025

Background The global rise in type-2 diabetes (T2D) has prompted the development of new digital technologies for management. However, despite proliferation health companies T2D care, scaling their solutions remains a critical challenge. This study investigates transformation ecosystems and seeks to identify key innovation patterns. We examine: (1) What are emerging organizations ecosystems? (2) value streams (3) Which patterns present Methods conducted literature review market analysis characterize ecosystems, pre- post-digital transformation. used e3-value methodology visualize (RQ1 RQ2) expert interviews (RQ3). Results Our analyses revealed emergence eight organization segments ecosystems: real-world evidence analytics, healthcare management platforms, clinical decision support, diagnostic monitoring, therapeutics, wellness, online community, pharmacy (RQ1). Visualizing among these highlights crucial importance individual data (RQ2). Furthermore, our four major within ecosystem: open ecosystem strategies, outcome-based payment models, platformization, user-centric software Conclusions findings illustrate transition from traditional chains care platform-based outcome-oriented models. These can inform strategic decisions providers, potentially helping anticipate trends across other chronic disease ecosystems.

Language: Английский

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Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare

HORMONES, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Language: 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, Journal Year: 2024, Volume and Issue: 26(S1), P. 14 - 29

Published: Feb. 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.

Language: Английский

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