This paper uses the MIMIC-IV dataset to examine the fairness and bias in an XGBoost binary classification model predicting the Intensive Care Unit (ICU) length of stay (LOS). Highlighting the critical role of the ICU in managing critically ill patients, the study addresses the growing strain on ICU capacity. It emphasizes the significance of LOS prediction for resource allocation. The research reveals class imbalances in the dataset across demographic attributes and employs data preprocessing and feature extraction. While the XGBoost model performs well overall, disparities across race and insurance attributes reflect the need for tailored assessments and continuous monitoring. The paper concludes with recommendations for fairness-aware machine learning techniques for mitigating biases and the need for collaborative efforts among healthcare professionals and data scientists.

WASHINGTON, DC – Yesterday, the U.S. General Services Administration (GSA) launched the Applied AI Healthcare Challenge, a prize competition seeking diverse and practical solutions to help federal agencies provide the highest level of medical care. The Centers of Excellence (CoE) is working in partnership with Challenge.gov, In particular, GSA encourages large and small enterprises, women-owned, minority-owned, small disadvantaged, and service-disabled veteran-owned small businesses to participate. "Technology Transformation Services drives innovation by partnering with technologists in all sectors to identify, demonstrate, test, and prove out technology products that improve delivery of government services and benefits. The Applied AI Healthcare Challenge helps the public and private sector work together to identify promising new AI technology products that support healthcare services and initiatives, centering accessibility, privacy, and customer experience," said TTS Director and FAS Deputy Commissioner Ann Lewis.

The first paper, "Predicting Diabetes Risk from Electronic Health Records: A Machine Learning Approach," uses machine learning to improve diabetes risk prediction accuracy. Diabetes is a chronic disease that affects millions of people worldwide, and accurate risk prediction is important for identifying individuals who are at risk of developing the disease and for targeting preventive interventions. The authors of this paper propose a machine learning approach that uses data from electronic health records (EHRs) to predict diabetes risk, and demonstrate that their approach outperforms other state-of-the-art methods. In the second paper, "Deep Learning for Medical Image Analysis: A Review," deep learning is discussed for the analysis of medical images. For diagnosis and treatment planning, medical images such as X-rays, CT scans, and MRIs are crucial.

There is growing evidence showing the significant role of social determinant of health (SDOH) on a wide variety of health outcomes. In the era of artificial intelligence (AI), electronic health records (EHRs) have been widely used to conduct observational studies. However, how to make the best of SDOH information from EHRs is yet to be studied. In this paper, we systematically reviewed recently published papers and provided a methodology review of AI methods using the SDOH information in EHR data. A total of 1250 articles were retrieved from the literature between 2010 and 2020, and 74 papers were included in this review after abstract and full-text screening. We summarized these papers in terms of general characteristics (including publication years, venues, countries etc.), SDOH types, disease areas, study outcomes, AI methods to extract SDOH from EHRs and AI methods using SDOH for healthcare outcomes. Finally, we conclude this paper with discussion on the current trends, challenges, and future directions on using SDOH from EHRs.

PSFK's Future of Health looks into all the ways artificial intelligence is transforming healthcare The state of your health shouldn't be a mystery, nor should patients or doctors have to wait long to find answers to pressing medical concerns. In PSFK's Future of Health Report, we dig deep into the latest in AI, big data algorithms and IoT tools that are enabling a new, more comprehensive overview of patient data collection and analysis. Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations. The impact of improved AI helps patients form a baseline for well-being and is making changes all across the healthcare industry. AI not only streamlines intake processes and reduces processing volume at clinics, it also controls input and diagnostic errors within a patient record, allowing doctors to focus on patient care and communication, rather than data entry.

For more than a decade the company formerly known as Google, latterly rebranded Alphabet to illustrate the full breadth of its A to Z business ambitions, has engineered an annually increasing revenue generating empire which last year pulled in 75 billion. And it's done this mostly by mining user data for ad targeting intel. Slice it and dice it how you like but Google's business engine needs data like the human body needs oxygen. Most of its products are thus designed to remove friction to accessing more user data; whether it's free search, free email, free cloud storage, free document editing tools, free messaging apps, a fuzzy social network that no one loves but which is somehow still hanging around, free maps, a mobile OS platform that OEMs can load onto smartphone hardware without paying a license fee… Most of what Google builds it opens to all comers to keep the data pouring in. The bits and bytes must flow.