Sensor Technology for Opening New Pathways in Diagnosis and Therapeutics of Breast, Lung, Colorectal and Prostate Cancer

Roshani, Saeed; Coccia, Mario; Mosleh, Melika

doi:10.1101/2022.02.18.22271186

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…Problem-Based Learning (PBL) is a key point for learning activities (13) and technology plays an important role in the learning process (14). Computer science and health care field is correlated and lots of research is going on with combination of these two fields (15)(16)(17). Ahsan et al (18), this paper is a comprehensive review of various algorithms in Machine Learning for popular diseases based on symptoms.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Chatbot that Predicts an Illness Dynamically using Machine Learning Techniques

Shedthi B.,

Shetty,

Chadaga

et al. 2024

IJE

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Timely access to healthcare is crucial in order to maintain a high standard of living. However, obtaining medical consultations can be difficult, especially for those living in remote areas or during a pandemic when face-to-face consultations are not always possible. The ability to accurately diagnose diseases is essential for effective treatment, and recent technological advancements offer a potential solution. Machine learning (ML) and Natural language processing (NLP) enables computer programs to understand human language and extract desired features from responses, allowing for human-like interaction with users. By leveraging these technologies, healthcare professionals can potentially provide more accessible and efficient medical consultations to individuals, regardless of their location. The concept is to establish an online platform where users can ask medical-related queries and receive responses from both medical professionals and fellow users. The platform would feature a Medical Chatbot, which employs advanced ML techniques to analyze user-provided symptoms and provide initial disease diagnosis and related information prior to consulting with a doctor. This disease prediction chatbot interacts dynamically with the users to enter the symptoms of the diseases and based on syntactic and semantic similarity response is given. In this work the threshold of similarity score is kept of 0.7. K-Nearest neighbors, Random forest, Support vector machine, Naive bayes and Logistic regression algorithms are used for prediction of disease based on symptoms which are faced by users. The syntactic similarity, fuzzy string matching and semantic similarity using all-MiniLM-L6-v2 model is used to improve the efficiency of the result.

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Section: Related Workmentioning

confidence: 99%

Implementation of Chatbot that Predicts an Illness Dynamically using Machine Learning Techniques

Shedthi B.,

Shetty,

Chadaga

et al. 2024

IJE

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“…[13][14][15][16][17] For clinical research data integration, machine learning and meta-analysis have become critical. [18][19][20] Meta-analysis by Sahebkar et al [21] suggests that statins were safe in treatment of systemic lupus erythematosus patients and may reduce cardiovascular risk through lowering C-reactive protein (CRP) levels.…”

Section: Introductionmentioning

confidence: 99%

The efficacy of statins in the treatment of rheumatoid arthritis: A systematic review and meta-analysis

Ren,

2023

Medicine

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Background: A large body of research has investigated the use of statins in rheumatoid arthritis (RA); however, the therapeutic effects of statins remain uncertain. Thus, we designed a systematic review and meta-analysis to evaluate the role of statins in patients with RA. Methods: Databases searched to detect clinical randomized controlled trials or clinical controlled trials on the interaction between statins and RA before January 2020 included PubMed, Web of Sciences, Embase, Cochrane Library, CNKI, Wan Fang Database. Efficacy was measured by Disease Activity Score in 28 Joints (DAS28), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), tenderness of the joint (TJ), swelling of the joint (SJ), and interleukin-6. The level of blood lipid was also evaluated. STATA 12.0 was used for the meta-analysis. The Cochrane method was used for quality assessment. Heterogeneity was considered to determine fixed effects or random effects models. Results: Nineteen clinical trials with a total of 22,906 subjects were included in the meta-analysis. Sixteen studies reported a change in DAS28 after statin treatment. The pooled analysis showed that statins reduced DAS28 in RA patients. Change in ESR after statin treatment was reported in 9 studies. The summary analysis showed that statins lowered ESR in RA patients. Twelve studies reported a change in CRP after statin treatment. The results of the entire analysis showed that statins lowered CRP in RA patients. Seven studies reported a change in TJ after statin treatment. The combined analysis showed that statins reduced TJ at RA patients. Six studies reported changes in IL6 after statin therapy. The results showed that statins failed to reduce IL6 in RA patients. Seven studies reported changes in SJ after statin therapy, which showed that statins failed to reduce SJ in RA patients. We also found that statins can reduce blood lipid levels in RA patients. Conclusion: In conclusion, statins were able to reduce DAS28, ESR, CRP, TJ, and blood lipids. It indicated that stains can benefit patients with RA by inhibiting the expression of inflammatory factors and reducing the levels of lipids in the blood. Our study may offer a new perspective on the treatment of RA and provide research ideas for future larger multi-center clinical trials.

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“…The goal of this study is to analyze the scientific ecosystem (interacting research fields) of sensor research and technologies over time to show interactions supporting technological trajectories directed to fulfil human goals and needs, and solve problems in society. In particular, this paper investigates sensor research and technologies, from 1990 to 2020 period, to clarify major changes in the scientific structure over these 30 years [3][4][5][6]. In general, technological change in sensor research can support new technologies, such as smart or intelligent sensors [7][8][9][10] and the Internet of Things [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Structure and Evolution of Sensor Research to Forecast Emerging Scientific and Technological Directions

Coccia¹,

Roshani²,

Mosleh³

2022

Preprint

Self Cite

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How do sensor research and technologies grow over time? This paper applies the network analysis with a new computational approach to map the structure and evolution of sensor research and technologies over a 30-year time frame (1990-2020).The goal of this study is to analyze the evolution of sensor research for forecasting emerging scientific and technological trajectories. Results show that the scientific interaction within ecosystem (represented with networks) of sensor generates a co-evolution of scientific fields supporting the accelerated growth of different technological tra-jectories, such as: wireless sensors, fiber optic and optical sensors, gas sensors and biosensors. These results suggest main theoretical implications that explain the evolution of sensor research with critical aspects of innovation management to support R&D investments towards new technological trajectories having a high potential of growth.

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Sensor Technology for Opening New Pathways in Diagnosis and Therapeutics of Breast, Lung, Colorectal and Prostate Cancer

Cited by 8 publications

References 52 publications

Implementation of Chatbot that Predicts an Illness Dynamically using Machine Learning Techniques

Implementation of Chatbot that Predicts an Illness Dynamically using Machine Learning Techniques

The efficacy of statins in the treatment of rheumatoid arthritis: A systematic review and meta-analysis

Structure and Evolution of Sensor Research to Forecast Emerging Scientific and Technological Directions

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