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Detect patients that will have increase rates of mortality and negative obstetric outcomes using clinical variables.
The 2020 U.S. maternal mortality rate ranked last among all industrialized countries, with 17.4 deaths per 100,000 pregnancies (2). For every maternal death measured, 100 women also experience severe obstetric morbidity, resulting in significant health consequences (1). More than 60,000 women suffer from severe maternal morbidity annually, and the rate of maternal morbidity increased by 36% from 2008 to 2018 (1)(3). Severe maternal morbidity is associated with a 111% increase in maternity-related costs for commercially-insured populations and 175% for those covered by Medicaid (4). In 2019, there were 3.75 million births, with Medicaid paying for approximately 50% of them (5)(6). Maternal health data reveals immense racial and ethnic disparities. Black women are three to four times more likely to die from pregnancy-related causes compared to White women, and up to 12 times more likely in some cities (1).
Non-Hispanic Black women also have the highest rates for most severe morbidity indicators and are more likely to suffer from pregnancy-induced and chronic conditions (1). Reducing these disparities is crucial for improving obstetric outcomes. Studies indicate that 46% of maternal deaths among Black women are potentially preventable, compared to 33% among White women (1). Additionally, socioeconomic factors, such as hospital quality, play a significant role in these disparities. Approximately 75% of Black deliveries occur in a quarter of U.S. hospitals, which have higher risk-adjusted maternal morbidity rates, compared to 18% of White deliveries in the same hospitals (1). Black and Hispanic women are nearly three times as likely to report concerns about their treatment due to race, ethnicity, and cultural background (7)(8).
To address this issue, a predictive AI model has been designed to assess maternal and obstetric risks. By evaluating clinical data, including medical history and current health status, the model equips healthcare providers with actionable information to provide preventative and personalized care to at-risk pregnant women.
The model uses the Maternal Health Risks dataset from the UCI Machine Learning Repository (9). This data set, which includes information on systolic and diastolic blood pressure, blood sugar, body temperature, heart rate and age, was collected through an IoT-based risk monitoring system in several hospitals and community clinics in Bangladesh. The data presents six significant and relevant risk factors for maternal mortality, addressing a key concern of the United Nations Sustainable Development Goals.
The 2020 U.S. maternal mortality rate ranked last among all industrialized countries, with 17.4 deaths per 100,000 pregnancies (2). For every maternal death measured, 100 women also experience severe obstetric morbidity, resulting in significant health consequences (1). More than 60,000 women suffer from severe maternal morbidity annually, and the rate of maternal morbidity increased by 36% from 2008 to 2018 (1)(3). Severe maternal morbidity is associated with a 111% increase in maternity-related costs for commercially-insured populations and 175% for those covered by Medicaid (4). In 2019, there were 3.75 million births, with Medicaid paying for approximately 50% of them (5)(6). Maternal health data reveals immense racial and ethnic disparities. Black women are three to four times more likely to die from pregnancy-related causes compared to White women, and up to 12 times more likely in some cities (1).
Non-Hispanic Black women also have the highest rates for most severe morbidity indicators and are more likely to suffer from pregnancy-induced and chronic conditions (1). Reducing these disparities is crucial for improving obstetric outcomes. Studies indicate that 46% of maternal deaths among Black women are potentially preventable, compared to 33% among White women (1). Additionally, socioeconomic factors, such as hospital quality, play a significant role in these disparities. Approximately 75% of Black deliveries occur in a quarter of U.S. hospitals, which have higher risk-adjusted maternal morbidity rates, compared to 18% of White deliveries in the same hospitals (1). Black and Hispanic women are nearly three times as likely to report concerns about their treatment due to race, ethnicity, and cultural background (7)(8).
To address this issue, a predictive AI model has been designed to assess maternal and obstetric risks. By evaluating clinical data, including medical history and current health status, the model equips healthcare providers with actionable information to provide preventative and personalized care to at-risk pregnant women.
The model uses the Maternal Health Risks dataset from the UCI Machine Learning Repository (9). This data set, which includes information on systolic and diastolic blood pressure, blood sugar, body temperature, heart rate and age, was collected through an IoT-based risk monitoring system in several hospitals and community clinics in Bangladesh. The data presents six significant and relevant risk factors for maternal mortality, addressing a key concern of the United Nations Sustainable Development Goals.
