Parkinson’s Disease Management via Wearable Sensors: A Systematic Review

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 35219 - 35237

Published: Jan. 1, 2022

Wearable technology has played an essential role in the Mobile Health (mHealth) sector for diagnosis, treatment, and rehabilitation of numerous diseases disorders. One such neuro-degenerative disorder is Parkinson’s Disease (PD). It categorized by motor symptoms that affect a patient’s skills non-motor general health PD patient. The quality life patient with highly compromised. To date, there no cure disease, but early intervention assistive care can help to perform daily activities considerable ease. Many research works management discuss challenges healthcare professionals face detection this disease. Sensor devices have been promising overcome these certain degree because low cost accuracy measurement, yielding precise conclusive results detect, monitor, manage PD. This paper presents Systematic Literature Review (SLR) provides in-depth analysis symptoms, Motor Non-Motor Symptoms (NMS), current diagnosis techniques used their efficacy. also highlights work various researchers wearable sensors proposals improve diagnosing, monitoring, managing remotely via sensors. Another area focus commercially available wearables few progress. will be beneficial future identify existing gaps provide clinicians better insight into disease progression, avoid complications. analyzes around 50+ articles from 2016 2021 concludes still much room improvement during process. While attributed Symptom management, little on NMS Furthermore, management.

Language: Английский

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Mental Health Analysis in Social Media Posts: A Survey

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(3), P. 1819 - 1842

Published: Jan. 3, 2023

The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community find validate associations between media posts mental health status. Cross-sectional longitudinal studies of data bring fore importance real-time responsible AI models analysis. Aiming classify directions computing tracking advances development machine learning (ML) deep (DL) based models, we propose a comprehensive survey on quantifying media. We compose taxonomy healthcare highlight recent attempts examining well-being with writings define all possible investigate thread handling online stress, depression suicide detection this work. key features manuscript are (i) feature extraction classification, (ii) advancements (iii) publicly available dataset, (iv) new frontiers future directions. compile information introduce young academic practitioners field computational intelligence analysis In manuscript, carry out quantitative synthesis qualitative review corpus over 92 potential articles. context, release collection existing work an easily accessible updatable repository:https://github.com/drmuskangarg/mentalhealthcare.

Language: Английский

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Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(26), P. 68281 - 68315

Published: Jan. 25, 2024

Language: Английский

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Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?

Medical Sciences, Journal Year: 2025, Volume and Issue: 13(1), P. 8 - 8

Published: Jan. 11, 2025

Depression poses significant challenges to global healthcare systems and impacts the quality of life individuals their family members. Recent advancements in artificial intelligence (AI) have had a transformative impact on diagnosis treatment depression. These innovations potential significantly enhance clinical decision-making processes improve patient outcomes settings. AI-powered tools can analyze extensive data—including medical records, genetic information, behavioral patterns—to identify early warning signs depression, thereby enhancing diagnostic accuracy. By recognizing subtle indicators that traditional assessments may overlook, these enable providers make timely precise decisions are crucial preventing onset or escalation depressive episodes. In terms treatment, AI algorithms assist personalizing therapeutic interventions by predicting effectiveness various approaches for individual patients based unique characteristics history. This includes recommending tailored plans consider patient’s specific symptoms. Such personalized strategies aim optimize overall efficiency healthcare. theoretical review uniquely synthesizes current evidence applications primary care depression management, offering comprehensive analysis both personalization capabilities. Alongside advancements, we also address conflicting findings field presence biases necessitate important limitations.

Language: Английский

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Depression Sentiment Analysis using Machine Learning Techniques:A Review

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 20, 2025

Depression is one of the habitual psychological well-being diseases and a significant number depressed individuals end their lives. People suffering from depression don’t ask for help doctors due to hesitation or unawareness about that causes delay in diagnosis treatment. A lot people share opinions emotions on social networking sites. Several studies site posts related rely upon Facebook, Twitter, Blogs, other networks because they recording behavioral attributes which are person’s thinking, socialization, communication, etc. Datasets various sites useful sentiment analysis. Various machine learning deep techniques like Naïve Bayes, maximum entropy, Support Vector Machine (SVM), Decision Tree classifiers neural networks, recurrent have been used detection. This paper presents review analysis performed media platforms detection The datasets utilized also discussed. comparative existing work area provided get clear understanding used. Finally, challenges future can be done field discussed

Language: Английский

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A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Complex & Intelligent Systems, Journal Year: 2022, Volume and Issue: 9(1), P. 1027 - 1058

Published: May 31, 2022

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) demonstrated ability to connect various medical apparatus, sensors, specialists ensure best treatment in a distant location. Patient safety improved, prices have decreased dramatically, services become more approachable, operational efficiency industry increased. This paper offers recent review current future applications, security, market trends, IoMT-based implementation. analyses advancement IoMT implementation addressing concerns from perspectives enabling technologies, services. potential obstacles issues system are also discussed. Finally, survey includes comprehensive overview different disciplines empower researchers who eager work make advances field obtain better understanding domain.

Language: Английский

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Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care

Issues in Mental Health Nursing, Journal Year: 2023, Volume and Issue: 44(10), P. 1020 - 1034

Published: Oct. 3, 2023

This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence health. They also facilitate remote monitoring, bridge geographical gaps, support clinical decision-making. The evolution virtual assistants AI-enhanced therapeutic interventions are discussed. These technological advancements reshape nurse-patient interactions while ensuring personalized, efficient, high-quality addresses AI's ethical responsible use emphasizing privacy, data security, balance between human interaction tools. As applications care continue to evolve, this encourages continued innovation advocating implementation, thereby optimally leveraging potential nursing.

Language: Английский

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A Smart Data-Driven Prototype for Depression and Stress Tracking in Patients

Lecture notes in networks and systems, Journal Year: 2023, Volume and Issue: unknown, P. 423 - 434

Published: Jan. 1, 2023

Language: Английский

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Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model

ACM Transactions on Asian and Low-Resource Language Information Processing, Journal Year: 2022, Volume and Issue: 23(1), P. 1 - 20

Published: Nov. 5, 2022

Depression is a kind of emotion that negatively impacts people's daily lives. The number people suffering from long-term feelings increasing every year across the globe. Depressed patients may engage in self-harm behaviors, which occasionally result suicide. Many psychiatrists struggle to identify presence mental illness or negative early provide better course treatment before they reach critical stage. One most challenging problems detecting depression at earliest possible Researchers are using Natural Language Processing (NLP) techniques analyze text content uploaded on social media, helps design approaches for depression. This work analyses numerous prior studies used learning existing methods suffer model representation detect with high accuracy. present addresses solution these by creating new hybrid deep neural network representations called “Fasttext Convolution Neural Network Long Short-Term Memory (FCL).” In addition, this utilizes advantage NLP simplify analysis during development. FCL comprises fasttext embedding considering out-of-vocabulary (OOV) semantic information, convolution (CNN) architecture extract global and (LSTM) local features dependencies. was implemented real-world datasets utilized literature. proposed technique provides results than state-of-the-art

Language: Английский

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Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

Computer Systems Science and Engineering, Journal Year: 2023, Volume and Issue: 46(3), P. 2651 - 2666

Published: Jan. 1, 2023

With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging real-world tasks. Given scope this area, various techniques are found in literature. One such notable technique, Multiple Deep Q-Network (DQN) based use multiple DQN-based-entities, which learn together communicate with each other. The learning has to be distributed wisely among all entities a scheme inter-entity communication protocol carefully designed. As more complex DQNs fore, overall complexity these multi-entity increased folds leading issues like difficulty training, need high resources, training time, fine-tuning performance issues. Taking cue from parallel processing nature efficacy, we propose lightweight ensemble approach solving core It uses binary action having shared state reward. benefits proposed simplicity, faster convergence better compared conventional DQN approaches. can potentially extended any type by forming ensemble. Conducting extensive experimentation, promising results obtained using on OpenAI Gym tasks, Atari 2600 games as recent techniques. gives stateof-the-art score 500 Cartpole-v1 task, 259.2 LunarLander-v2 four out five games.

Language: Английский

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