Page 180 - AI for Good-Innovate for Impact
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AI for Good-Innovate for Impact



                      diabetes or hypertension.  Prevention of progression of CKD to ESKD also requires screening
                      as 90% of people with CKD are unaware that they have impaired kidney function, because it
                      is usually asymptomatic until it reaches an advanced stage.

                      This project aims to develop an AI system that would be able to aggregate all the collected
                      data at patient level, process it through data cleaning and feature engineering, develop and
                      train a model on such data to predict the likelihood of developing CKD or, if already present,
                      the likelihood to get worse next year. Through the use of Explainable AI techniques, e.g. SHAP
                      or Causal SHAP, we will then be able to understand each individual's main risk factors, to then
                      intervene on those people to help them take the right actions to reduce their risk, leading to
                      health and financial improvements.
                      UN Goals:

                      •    SDG 3: Good Health and Well-being
                      •    SDG 10: Reduced Inequality
                      •    SDG 17: Partnerships to achieve the Goal

                      Justification of UN Goals selection: SDG 3: Good Health and Well-being: The primary goal
                      of this project is to predict chronic kidney disease (CKD), thereby contributing directly to
                      SDG 3, which focuses on ensuring healthy lives and promoting well-being for all at all ages.
                      By leveraging advanced predictive analytics, the project aims to enable early detection and
                      intervention for CKD, ultimately improving patient outcomes and reducing the burden of
                      disease on individuals and healthcare systems. SDG 10: Reduced Inequality: Chronic Kidney
                      Disease often disproportionately affects certain populations due to inequalities based on
                      ethnicity, gender, and socioeconomic status. Addressing SDG 10, which seeks to reduce
                      inequality within and among countries, this project emphasizes the need for an ethical and
                      bias-free approach in its predictive models. By ensuring that the predictive tools are inclusive
                      and equitable, the project aims to mitigate disparities in CKD diagnosis and treatment, fostering
                      a more just healthcare system. SDG 17: Partnerships for the Goals: The project's collaborative
                      framework aligns with SDG 17, which advocates for strengthening the means of implementation
                      and revitalizing the global partnership for sustainable development. Carna's industry expertise,
                      combined with connections to academic experts, underscores the value of multi-sectoral
                      collaboration. This synergy not only enhances the project's impact but also showcases the
                      potential of partnerships in driving innovative solutions for global health challenges.

                      Partner name: Carna Health Partner


                      41�2�2  Future work

                      Proof of concept development, Model development, Collection of more data


                      41�3  Use-case requirements

                      •    REQ-01: It is required that there is a comprehensive collection of patient screening data,
                           medical history, medication records and demographic information.
                      •    REQ-02: It is critical that there are secure databases for storing patient data and data
                           integration tools to handle diverse data sources.
                      •    REQ-03: It is crucial that there is development of a highly accurate machine learning
                           model to make appropriate predictive analysis.





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