The Impact of Text Message On Self-Management for Coronary Heart Disease: A Meta-Analysis of Randomized Controlled Trials

Wan, Yong; Wu, Xin; Kou, Yan

doi:10.1532/hsf.2519

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…(2) Neurodegenerative diseases [55,49]: while not directly identifiable through text, subtle changes in typing speed and fine motor skills required for typing might provide early clues of Parkinson's disease [39,54,1,20]; (3) Sleep disorders [73,66]: late-night time stamps and content that indicates restlessness or consistent complaints about lack of sleep may be suggestive of insomnia; (4) Infectious diseases [71,12,72,16]: it is less likely to identify infectious diseases from text messages unless the content explicitly describes symptoms or experiences related to the infectious disease [71]; (5) Chronic diseases [41,43,45]: if someone frequently discusses feelings of tiredness, changes in weight, or other symptomatic experiences, this could indirectly hint at chronic conditions like diabetes or thyroid issues [31]. While text message analysis may provide signals indicative of a health issue [58,75], this method is far from diagnostic so this can be just a preventive supportive tool for specialists (see Figure 1). The identification of diseases requires thorough clinical evaluation and should not be done solely on the basis of text message analysis.…”

Section: Introductionmentioning

confidence: 99%

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Biró,

Cuesta-Vargas,

Szilágyi

2023

Acta Universitatis Sapientiae, Informatica

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The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices. Background: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions. Materials and Methods: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics. Results: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time. Conclusions: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.

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Section: Introductionmentioning

confidence: 99%

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Biró,

Cuesta-Vargas,

Szilágyi

2023

Acta Universitatis Sapientiae, Informatica

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2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease

Virani,

Newby,

Arnold

et al. 2023

Journal of the American College of Cardiology

159

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2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

Virani,

Newby,

Arnold

et al. 2023

Circulation

398

112

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AIM The “2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease” provides an update to and consolidates new evidence since the “2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease” and the corresponding “2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease.” Methods A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Structure This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost–value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

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The Impact of Text Message On Self-Management for Coronary Heart Disease: A Meta-Analysis of Randomized Controlled Trials

Cited by 6 publications

References 33 publications

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease

2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

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