Effectiveness of telemedicine: a systematic review of reviews. Tag: machine learning in healthcare research papers . A targeted real-time early warning score (TREWScore) for septic shock. In the article the authors use the Sepsis subset of the MIMIC-III dataset. Neither machine learning nor any other technology can replace this. Artificial intelligence (AI) aims to mimic human cognitive functions. The Lancet Regional Health – Western Pacific, Advancing women in science, medicine and global health, Digital pathology and artificial intelligence, Explaining the unexplainable: discrepancies in results from the CALGB/SWOG 80405 and FIRE-3 studies, Access any 5 articles from the Lancet Family of journals, https://doi.org/10.1016/S1470-2045(19)30149-4, Big data and machine learning algorithms for health-care delivery, https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/A-Proposed-Model-AI-Governance-Framework-January-2019.pdf, https://www.forbes.com/sites/bernardmarr/2017/01/20/first-fda-approval-for-clinical-cloud-based-deep-learning-in-healthcare/#2e0b1a44161c, https://www.medgadget.com/2018/02/arterys-fda-clearance-liver-ai-lung-ai-lesion-spotting-software.html, https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/, https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm604357.htm, https://www.philips.com/a-w/about/news/archive/standard/news/press/2018/20180301-philips-launches-ai-platform-for-healthcare.html, http://newsroom.gehealthcare.com/new-apps-smart-devices-launch-healthcare-edison-ai-platform/, http://social-innovation.hitachi/en/case_studies/mri_predictive_maintenance/?__CAMCID=lknjlhToJY-387&__CAMSID=cUeDHEgFEGyU-74&__CAMVID=EfODhEgFEGYU&_c_d=1&_ct=1548576884716, https://healthpolicy.duke.edu/sites/default/files/atoms/files/dukemargolisaienableddxss.pdf, Correction to Lancet Oncol 2019; 20: e262–73, The Lancet Regional Health – Western Pacific, Recommend Lancet journals to your librarian, Personal Data Protection Commission Singapore. Philips. PHD Guidance. In… Neither machine learning nor any other technology can replace this. Weeks* (Microsoft); Nicholas Becker (Microsoft); Juan L. Ferres (Microsoft), Semantic Nutrition: Estimating Nutrition with Mobile Assistants, Joshua D’Arcy (Duke University); Sabrina Qi (Duke University); Dori Steinberg (Duke University); Jessilyn Dunn (Duke University), Predicting antibiotic resistance in Mycobacterium tuberculosis with genomic machine learning, Chang Ho Yoon (Havard University); Anna G. Green (Havard University); Michael L. Chen (Havard University); Luca Freschi (Havard University); Isaac Kohane (Havard University); Andrew Beam (Havard University); Maha Farhat (Massachusetts General Hospital), Topic Modeling of Patient Portal and Telephone Encounter Messages: Insights from a Cardiology Practice, Jedrek Wosik (Duke University); Shijing Si (Duke University); Ricardo Henao (Duke University); Mark Sendak (Duke Institute of Health Innovation); William Ratliff (Duke Institute of Health Innovation); Suresh Balu (Duke Institute of Health Innovation); Deepthi Krishnamaneni(Duke Health Technology Solutions); Ryan Craig(Duke Health Technology Solutions); Eric Poon (Duke Health Technology Solutions); Lawrence Carin(Duke University); Manesh Patel (Duke University), Development of phenotype algorithms for common acute conditions using SHapley Additive exPlanation values, Konan Hara (The University of Tokyo, TXP Medical Co. Ltd.); Ryoya Yoshihara (The University of Tokyo, TXP Medical Co. Ltd.); Tomohiro Sonoo (The University of Tokyo, TXP Medical Co. Ltd.); Toru Shirakawa (Osaka University, TXP Medical Co. Ltd.); Tadahiro Goto (The University of Tokyo, TXP Medical Co. Ltd.); Kensuke Nakamura (Hitachi General Hospital), TL-Lite: Temporal Visualization for Clinical Supervised Learning, Jeremy C. Weiss (Carnegie Mellon University), Development and Validation of Machine Learning Models to Predict Admission from the Emergency Department to Inpatient and Intensive Care Units, Alexander Fenn (Duke University); Connor Davis (Duke Institute of Health Innovation); Neel Kapadia  (Duke University); Daniel Buckland  (Duke University); Marshall Nichols (Duke Institute of Health Innovation); Michael Gao  (Duke University); William Knechtle  (Duke University); Suresh Balu  (Duke University); Mark Sendak  (Duke University); B. Jason Theiling (Duke Institute of Health Innovation), Predicting Cardiac Decompensation and Cardiogenic Shock Phenotypes for Duke University Hospital Patients, Harvey Shi* (Duke University, Duke Institute of Health Innovation); Will Ratliff* (Duke Institute of Health Innovation); Mark Sendak (Duke Institute of Health Innovation); Michael Gao (Duke Institute of Health Innovation); Marshall Nichols (Duke Institute of Health Innovation); Mike Revoir (Duke Institute of Health Innovation); Suresh Balu (Duke Institute of Health Innovation); Sicong Zhao (Duke Institute of Health Innovation, Duke Social Science Research Institute); Michael Pencina (Duke University); Kelly Kester (Duke Heart Center and Department of Medicine); W. Schuyler Jones (Duke Heart Center and Department of Medicine); Chetan B. Patel (Duke Heart Center and Department of Medicine); Jason Katz (Duke Heart Center and Department of Medicine); Aman Kansal (Duke Heart Center and Department of Medicine); Ajar Kochar (Brigham and Women’s Health); Zachary Wegermann (Duke Heart Center and Department of Medicine); Manesh Patel (Duke Heart Center and Department of Medicine), ICUnity: A software tool to harmonise the MIMIC-III and AmsterdamUMCdb databases, Emma Rocheteau (University of Cambridge); Jacob Deasy (University of Cambridge); Luca Filipe Roggeveen (Amsterdam University Medical Centre); Ari Ercole (University of Cambridge), Development of Machine Learning Model to Predict Risk of Inpatient Deterioration, Stephanie Skove (Duke Institute of Health Innovation); Harvey Shi (Duke Institute of Health Innovation); Ziyuan Shen (Duke University); Michael Gao (Duke Institute of Health Innovation); Mengxuan Cui (Duke University); Marshall Nichols (Duke Institute of Health Innovation); Suresh Balu (Duke Institute of Health Innovation); Armando Bedoya (Duke University); Dustin Tart (Duke University); Benjamin A Goldstein (Duke University); William Ratliff (Duke Institute of Health Innovation); Mark Sendak (Duke Institute of Health Innovation); Cara O’Brien (Duke University), Prediction of Critical Pediatric Perioperative Adverse Events using the APRICOT Dataset, Hannah Lonsdale (Johns Hopkins All Children’s Hospital); Ali Jalali (Johns Hopkins All Children’s Hospital); Hannah M. Yates (Johns Hopkins All Children’s Hospital); Luis M. Ahumada (Johns Hopkins All Children’s Hospital); Mohamed A. Rehman (Johns Hopkins All Children’s Hospital); Walid Habre (University Hospitals of Geneva, Switzerland); Nicola Disma (IRCCS Istituto Giannina Gaslini), A Heart Rate Algorithm to Predict High Risk Children Presenting to the Pediatric Emergency Department, James C. O’Neill (Wake Forest Baptist Health); E. Hunter Brooks (Wake Forest Baptist Health); Rebekah Jewell (Wake Forest Baptist Health); and David Cline (Wake Forest Baptist Health), Machine Learning to Automate Clinician Designed Empirical Manual for Congenital Heart Disease Identification in Large Claims Database, Ariane J. Marelli (McGill Adult Unit for Congenital Heart Disease Excellence); Chao Li (McGill Adult Unit for Congenital Heart Disease Excellence); Aihua Liu (McGill Adult Unit for Congenital Heart Disease Excellence); Hanh Nguyen (McGill Adult Unit for Congenital Heart Disease Excellence); James M Brophy (McGill University); Liming Guo (McGill Adult Unit for Congenital Heart Disease Excellence); David L Buckeridge (McGill University); Jian Tang (Université de Montréal); Joelle Pineau (McGill University); Yi Yang (McGill University); Yue Li (McGill University), Deep Learning Airway Structure Identification for Video Intubation, Ben Barone (Johns Hopkins University); Griffin Milsap (Johns Hopkins University); Nicholas M Dalesio (Johns Hopkins University), Denoising stimulated Raman histology using weak supervision to improve label-free optical microscopy of human brain tumors, Esteban Urias (University of Michigan); Christopher Freudiger (Invenio Imaging Inc.); Daniel Orringer (New York University); Honglak Lee (University of Michigan); Todd Hollon (University of Michigan), Engendering Trust and Usability in Clinical Prediction of Unplanned Admissions: The CLinically Explainable Actionable Risk (CLEAR) Model, Ruijun Chen (Columbia University, Weill Cornell Medical College); Victor Rodriguez (Columbia University); Lisa Grossman Liu (Columbia University); Elliot G Mitchell (Columbia University); Amelia Averitt (Columbia University); Oliver Bear Don't Walk IV (Columbia University); Shreyas Bhave (Columbia University); Tony Sun (Columbia University); Phyllis Thangaraj (Columbia University); Columbia DBMI CMS AI Challenge Team (Columbia University), Effects of Mislabeled Race Categorizations on Prediction of Inpatient Hyperglycemia, Morgan Simons* (Duke School of Medicine, Duke Institute for Health Innovation); Kristin Corey* (Duke School of Medicine, Duke Institute for Health Innovation); Marshall Nicols (Duke Institute for Health Innovation); Michael Gao (Duke Institute for Health Innovation); Suresh Balu (Duke Institute for Health Innovation); Mark Sendak* (Duke Institute for Health Innovation); Joseph Futoma (Harvard University, Duke Statistical Science), Development of Machine Learning Models for Early Prediction of Clinical Deterioration in Pediatric Inpatients, Zohaib Shaikh  (Duke School of Medicine, Duke Institute for Health Innovation); Daniel Witt (Duke Institute for Health Innovation, Mayo Clinic Alix School of Medicine); Tong Shen (Duke University); William Ratliff (Duke Institute for Health Innovation); Harvey Shi (Duke Institute for Health Innovation); Michael Gao (Duke Institute for Health Innovation); Marshall Nichols (Duke Institute for Health Innovation); Mark Sendak (Duke Institute for Health Innovation); Suresh Balu (Duke Institute for Health Innovation); Karen Osborne (Duke University Health System); Karan Kumar (Duke University); Kimberly Jackson (Duke University); Andrew McCrary (Duke University); Jennifer Li (Duke University), The use of natural language processing to improve identification of patients with peripheral artery disease, E. Hope Weissler (Duke University Medical School); Jikai Zhang (Duke University Medical School); Steven Lippmann (Duke University Medical School); Shelley Rusincovitch; Ricardo Henao (Duke University Medical School); W. Schuyler Jones (Duke University Medical School), Unsupervised identification of atypical medication orders: A GANomaly-based approach, Maxime Thibault (CHU Sainte-Justine); Pierre Snell (Université Laval); Audrey Durand (Université Laval, Mila – Quebec AI Institute), Novel Machine Learning Alert Model to Predict Cardiothoracic Intensive Care Unit Readmission or Mortality After Cardiothoracic Surgery, George A. Cortina (Duke Institute for Health Innovation, University of Virginia School of Medicine); Shujin Zhong (Duke Institute for Health Innovation); Marshall Nichols (Duke Institute for Health Innovation); Michael Gao (Duke Institute for Health Innovation); Will Ratliff (Duke Institute for Health Innovation); William Knechtle (Duke Institute for Health Innovation); Suresh Balu (Duke Institute for Health Innovation); Kelly Kester (Duke University Health System); Mary Lindsay (Duke University Health System); Jill Engel (Duke University Health System); Ashok Bhatta (Duke University Health System); Jacob Schroder (Duke University Health System); Ricardo Henao (Duke University); Mark Sendak (Duke Institute for Health Innovation); Mihai Podgoreanu (University of Virginia School of Medicine), Phenotyping Patients with Asthma: Preprocessing, and Clustering Algorithms, Richard Peters* (The University of Texas at Austin); Ali Lotfi Rezaabad* (The University of Texas at Austin); Matthew Sither (The University of Texas at Austin); Abhishek Shende (BrilliantMD, Inc.); Sriram Vishwanath (The University of Texas at Austin), Adoption of a Deep Learning “Risk Scale” Predictive Model to Reduce 7-day Readmission of Respiratory Patients at a Pediatric Center, John Morrison (Johns Hopkins All Children’s Hospital); Ali Jalali (Johns Hopkins All Children’s Hospital); Hannah Lonsdale (Johns Hopkins All Children’s Hospital); Paola Dees (Johns Hopkins All Children’s Hospital); Brittany Casey (Johns Hopkins All Children’s Hospital); Mohamed Rehman (Johns Hopkins All Children’s Hospital); Luis Ahumada (Johns Hopkins All Children’s Hospital). AI can be applied to various types of healthcare data (structured and unstructured).