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Computer-Based Medical Consultations: MYCIN


This text is a description of a computer-based system designed to assist physicians with clinical decision-making. This system, termed MYCIN, utilizes computer techniques derived principally from the subfield of computer science known as artificial intelligence (AI). MYCIN's task is to assist with the decisions involved in the selection of appropriate therapy for patients with infections.

MYCIN contains considerable medical expertise and is also a novel application of computing technology. Thus, this text is addressed both to members of the medical community, who may have limited computer science backgrounds, and to computer scientists with limited knowledge of medical computing and clinical medicine. Some sections of the text may be of greater interest to one community than to the other. A guide to the text follows so that you may select those portions most pertinent to your particular interests and background.

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Harvard Pathology Lab Develops Cancer-Detecting AI NVIDIA Blog


Pathologists agreed just three-quarters of the time when diagnosing breast cancer from biopsy specimens, according to a recent study. The difficult, time-consuming process of analyzing tissue slides is why pathology is one of the most expensive departments in any hospital. Faisal Mahmood, assistant professor of pathology at Harvard Medical School and the Brigham and Women's Hospital, leads a team developing deep learning tools that combine a variety of sources -- digital whole slide histopathology data, molecular information, and genomics -- to aid pathologists and improve the accuracy of cancer diagnosis. Mahmood, who heads his eponymous Mahmood Lab in the Division of Computational Pathology at Brigham and Women's Hospital, spoke this week about this research at GTC DC, the Washington edition of our GPU Technology Conference. The variability in pathologists' diagnosis "can have dire consequences, because an uncertain determination can lead to more biopsies and unnecessary interventional procedures," he said in a recent interview.

Harvard: Israel's MedAware could save US health system millions per year


Technology developed by Israel's MedAware could potentially save the United States health system $800 million annually by preventing medication errors, based on a study published earlier this week in the Joint Commission Journal on Quality and Patient Safety.MedAware developed an AI-based patient safety solution. The new study that was conducted by two Harvard doctors validates both the significant clinical impact and anticipated ROI of MedAware's machine learning-enabled clinical decision support platform designed to prevent medication-related errors and risks.MedAware uses AI methods similar to those used in the finance sector to stop fraud, by identifying "outliers" from a trend or practice in order to recognize suspicious or erroneous transactions. Most other electronic health record alert systems are rule based.In the US alone, prescription drug errors result in "substantial morbidity, mortality and excess health care costs estimated at more than $20 billion annually in the United States," according to Dr. Ronen Rozenblum, assistant professor at Harvard Medical School and director of business development for patient safety research and practice at Brigham and Women's Hospital. Rozenblum was the study's lead author, along with Harvard professor Dr. David Bates. Rozenblum, an Israeli who has been living in Boston for more than a decade, has been testing MedAware for the past five years.

Deep learning accurately stains digital biopsy slides


Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy slides can be stained computationally, using deep learning algorithms trained on data from physically dyed slides. Pathologists who examined the computationally stained H&E slide images in a blind study could not tell them apart from traditionally stained slides while using them to accurately identify and grade prostate cancers. What's more, the slides could also be computationally "de-stained" in a way that resets them to an original state for use in future studies, the researchers conclude in their May 20 study published in JAMA Network. This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

Machine learning model provides rapid prediction of C. difficile infection risk: Model successfully applied to data from medical centers with different patient populations, electronic health record systems


"Despite substantial efforts to prevent C. difficile infection and to institute early treatment upon diagnosis, rates of infection continue to increase," says Erica Shenoy, MD, PhD, of the MGH Division of Infectious Diseases, co-senior author of the study and assistant professor of Medicine at Harvard Medical School. "We need better tools to identify the highest risk patients so that we can target both prevention and treatment interventions to reduce further transmission and improve patient outcomes." The authors note that most previous models of C. difficile infection risk were designed as "one size fits all" approaches and included only a few risk factors, which limited their usefulness. Co-lead authors Jeeheh Oh, a U-M graduate student in Computer Science and Engineering, and Maggie Makar, MS, of MIT's Computer Science and Artificial Intelligence Laboratory and their colleagues took a "big data" approach that analyzed the whole electronic health record (EHR) to predict a patient's C. difficile risk throughout the course of hospitalization. Their method allows the development of institution-specific models that could accommodate different patient populations, different EHR systems and factors specific to each institution.