Developing predictive models for HIV Drug resistance: A genomic and AI approach

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 521 - 543

Опубликована: Май 5, 2024

This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With increasing prevalence drug-resistant strains HIV, there is critical need for innovative strategies predict manage mutations, thereby optimizing treatment outcomes prolonging efficacy antiretroviral therapy (ART). Drawing on advances in genomics AI, this study outlines conceptual framework that can identify potential drug-resistance mutations genomes inform clinical decision-making. The proposed integrates from HIV-infected individuals with AI algorithms capable learning complex patterns within data. By analyzing sequences obtained HIV-positive patients, aim genetic variations associated resistance, likelihood development, guide selection appropriate regimens. holds promise personalized medicine care, enabling clinicians tailor based an individual's profile risk resistance. Key components include preprocessing extract relevant features, model training using machine techniques such as deep ensemble methods, validation performance cross-validation independent testing. Furthermore, integration data, history viral load measurements, enhances accuracy provides valuable insights into response dynamics.The represents paradigm shift offering proactive management surveillance. technologies, healthcare providers anticipate address emerging before they compromise efficacy. Ultimately, implementation improve patient outcomes, reduce transmission strains, advance global fight against HIV/AIDS. Keywords: Developing, Predictive Models, Drug Resistance, Genomic, Approach.

Язык: Английский

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Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 558 - 578

Опубликована: Май 5, 2024

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains significant challenge. This review explores effectiveness challenges of using machine learning (ML) for forecasting spread high-risk areas. ML models have shown promise identifying patterns trends data, enabling more accurate predictions targeted interventions. insights into outbreak leverage various data sources, including demographic, epidemiological, behavioural data. By analysing these algorithms can identify populations geographical areas susceptible to transmission. information is crucial public health authorities allocate resources efficiently implement preventive measures effectively. Despite potential benefits, several exist predictions. These include quality issues, such as incomplete or inaccurate which affect reliability Additionally, complexity transmission dynamics need real-time pose models. To address challenges, researchers practitioners are exploring innovative approaches, integrating multiple sources advanced techniques. Collaborations between researchers, officials, technology experts also developing robust In conclusion, while offers valuable addressing model essential its effective use. overcoming has significantly improve prevention efforts ultimately reduce burden region. Keywords: Machine Learning, AI, Outbreaks: Predictions, Insights.

Язык: Английский

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Environmental data in epidemic forecasting: Insights from predictive analytics

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(5), С. 1113 - 1125

Опубликована: Май 5, 2024

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, geographical features, holds valuable insights for predicting identifying areas prone epidemics. This paper explores integration predictive analytics with environmental data enhance epidemic capabilities. By leveraging techniques, researchers officials can analyze identify regions at higher risk experiencing outbreaks. Through statistical modeling, machine learning algorithms, computational simulations, utilize indicators forecast likelihood spread For example, high temperatures humidity may be conducive mosquito-borne diseases, while poor quality experience increased rates respiratory infections. Case studies highlight application various contexts, including diseases tropical tracking infections urban quality. Early warning systems, informed by provide timely alerts potential threats, interventions resource allocation. While into offers significant benefits, challenges remain, availability, ethical considerations. Continued research collaboration are essential address these further effectiveness mitigating risks. In conclusion, this underscores importance forecasting, emphasizing their improve outcomes efforts face emerging climate change. Keywords: Data, Forecasting, Predictive Analytics.

Язык: Английский

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Utilising IoT and Remote Leadership to Enhance Team Collaboration and Performance in the Post-Pandemic Era

Опубликована: Окт. 3, 2024

Язык: Английский

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