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Addressing Racial Disparities in Maternal Mortality using Cognitive Science Technology

Introduction:

Maternal mortality remains a significant public health challenge worldwide, particularly among women of color. According to recent studies, African American women are three times more likely than White women to die during childbirth or shortly after. In addition, Latinx women have a 170% higher risk of pregnancy-related deaths compared to non-Hispanic Whites. These alarming statistics highlight the urgent need for effective interventions that address the root causes of such disparities. At Reclassify AI, we believe that cognitive science technologies can play a vital role in mitigating these issues by providing healthcare providers and policymakers with timely, accurate, and personalized insights that enable preventive care, early diagnosis, targeted treatment, and continuous monitoring. In this post, we present a real-world use case of how we deployed semantic AI approaches to provide a diagnostic aid for clinicians.


Use Case Overview:

Our customer segment includes urban hospital systems serving predominantly low-income communities, that are seeking assistance in reducing maternal mortality rates, especially among African American and Latinx patients. Many hospitals have already implemented various initiatives aimed at improving maternal outcomes, but they seek to leverage cutting-edge technologies such as artificial intelligence (AI), natural language processing (NLP), and graph databases to achieve more meaningful impacts. After conducting extensive consultations, requirement gathering, and feasibility assessments, Reclassify proposes a multiphase solution comprising semantic AI components. Our goal is to deliver highly personalized and contextualized approaches for addressing multiple risk factors.


Phase 1: Cognitive Science Technologies

In the initial phase, we focus on developing a data pipeline that can analyze high volumes of structured and unstructured clinical data from diverse sources including electronic medical records (EMRs), laboratory tests, imaging scans, surgical notes, patient surveys, and social determinants of health databases. Using NLP techniques, we extract relevant features and transform them into vector formats suitable for machine learning algorithms. We can then train a series of deep neural net models using supervised, semi-supervised, and unsupervised learning paradigms to predict maternal mortality risks based on patient demographics, medical histories, lifestyle factors, environmental exposures, and genetic profiles. Our score models across different metrics such as precision, recall, F1 score, AUC-ROC, and Brier score. Moreover, we incorporate interpretability and explainability tools that explain the rationale behind each prediction and highlight the most influential variables affecting the outcome.


Phase 2: Secure

Recognizing the importance of privacy, confidentiality, integrity, availability, and resilience in AI applications, we can outline initial security principles for the current and future states of the product. Our design considers defense-in-depth strategy by deploying multiple layers of protection starting from identity verification, device attestation, network segmentation, data masking, access controls, least privilege principle, encryption, auditing, alerting, logging, and recovery procedures. Additionally, we consider the integration of threat intelligence feeds and intrusion prevention systems to detect and respond to emerging cybersecurity challenges rapidly. Our approach ensures that patient data remains safe, secure, and confidential throughout its lifecycle while still enabling intelligent decision-making.


Phase 3: Data Integration & Interoperability

To ensure optimal performance and scalability, we architect our system to be adaptable and configurable. We follow open standards and APIs wherever possible to facilitate seamless integration of heterogeneous datasets from disparate sources. We also implement data cleaning, normalization, harmonization, mapping, enrichment, and validation routines to minimize errors, inconsistencies, redundancies, and omissions. We utilize graphs databases to store and retrieve relational and hierarchical data efficiently, ensuring fast query responses, low latency updates, and efficient memory usage.


Phase 4: Contextualized AI

We recognize the critical role of context in understanding and managing maternal mortality risks comprehensively. We leverage our proprietary semantic models and publicly available ontologies, taxonomies, and contextual data such as socioeconomic status, cultural beliefs, family dynamics, community resources, neighborhood safety, transportation options, air pollution, noise pollution, weather conditions, and disaster preparedness to gain deeper insights into maternal mortality disparities. We combine this information with biomedical, psychological, and sociological perspectives to generate a nuanced view of maternal wellbeing. We further explore ways to intervene proactively and preventatively rather than reactively and punitively.


Evaluation Results:

Our proof of concept realized several positive outcomes. The project shows promising work in improving in various indicators such as prenatal screening compliance, delivery planning adherence, postpartum follow-up attendance, pain management satisfaction, breastfeeding success rate, infant feeding practices, developmental milestone tracking, and parenting skills training. We also note that this work can increase patient engagement, trust, and loyalty due to the empathetic and tailored nature of the recommendations. Furthermore, our solution enables better resource allocation, staffing optimization, quality assurance, and research collaboration through real-time analytics and visualizations.


Conclusion:

At Reclassify AI, we strongly believe that leveraging cognitive science technologies can significantly enhance maternal health outcomes, particularly among underserved populations. By following a holistic and multi-disciplinary approach, we are able to provide actionable insights, empower healthcare teams, engage patients, and foster partnerships between stakeholders. However, there is still much work to be done, given the complex and multifaceted nature of maternal mortality disparities. Future research directions may include exploring the impact of genetic variants, epigenetic modifications, gut microbiota, environmental exposures, immune dysregulation, inflammation, stress response, sleep disorders, mental illnesses, substance abuse, violence exposure, and trauma history on maternal health. Additionally, developing novel AI methods such as reinforcement learning, active learning, transfer learning, federated learning, distributed learning, and explainable AI can help overcome some of the limitations and barriers associated with traditional approaches. Ultimately, our goal is to continue innovating and collaborating towards achieving universal reproductive rights and justice, where every woman can give birth safely and without undue harm or prejudice.

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