Tutorial on "Inductive Data Investigation: From ugly clinical data to KDD 2014". The program is now fully funded by MIT, and considered a success. She was also recently named one of MIT Tech Reviews 35 Innovators Under 35. Do as AI say: susceptibility in deployment of clinical decision-aids. Models must also be healthy, in that they should not learn biased rules or recommendations that harm minorities or minoritized populations. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. Ghassemis work has been published in topconferencesand journals includingNeurIPS, FaCCT,The Lancet Digital Health,JAMA, theAMA Journal of Ethics, andNature Medicine, and featured in popular press such as MIT News, NVIDIA, and the Huffington Post. Assistant Professor, Department of Electrical Engineering and Computer Science, and Institute for Medical Engineering & Science, AI in Healthcare Her work has been featured in popular press such as Fortune, MIT News, NVIDIA, and The Huffington Post. Integrating multi-modal clinical data and using recurrent and convolution neural networks to predict when patients will need important interventions. Download Preprint. Marzyeh currently serves as a NeurIPS 2019 Workshop Co-Chair, and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). Marzyehs research focuses on machine learning with clinical data to predict and stratify relevant human risks, encompassing unsupervised learning, supervised learning, structured prediction. It wasnt until the end of my PhD work that one of my committee members asked: Did you ever check to see how well your model worked across different groups of people?, That question was eye-opening for Ghassemi, who had previously assessed the performance of models in aggregate, across all patients. JP Cohen, L Dao, K Roth, P Morrison, Y Bengio, AF Abbasi, B Shen, H Suresh, N Hunt, A Johnson, LA Celi, P Szolovits, M Ghassemi, Machine Learning for Healthcare Conference, 322-337, A Raghu, M Komorowski, LA Celi, P Szolovits, M Ghassemi, Machine Learning for Healthcare Conference, 147-163, IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi, Annual Review of Biomedical Data Science 4, 123-144. Professor Ghassemi has published across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Using ambulatory voice monitoring to investigate common voice disorders: Research update, MS, Biomedical Engineering, Oxford University, 2011, Sept 2021 Herman L. F. von Helmholtz Career Development Professorship, MIT, July 2020 Azrieli Global Scholar, CIFARs Program in Learning in Machines and Brains, Oct. 2018 35 Innovators Under 35 Award, MIT Technology Review, MIT HST.953: Clinical Data Learning, Fall 2021, Fall 2022, MIT EECS 6.882: Ethical Machine Learning in Human Deployments, Spring 2022. Marzyeh is on the Senior Advisory Council of Women in Machine Learning (WiML), and organized its flagship workshop at NIPS during December 2014. [19] She was named as one of the 35 Innovators Under 35, in the visionaries category, in MIT Technology Review's annual list.[2][3]. I don't know where they were born but I do know what year they were born inJasmine was born in1999Nicolas was born in 1995Saveria was born in 1997Hayden was born in 1996Tyler was born in 1998Diane was born in 1997Jaydee-Lynn was born in 1996. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), and a Machine-learning algorithms have also fared well in mastering games like chess and Go, where both the rules and the win conditions are clearly defined. DD Mehta, JH Van Stan, M Zaartu, M Ghassemi, JV Guttag, Frontiers in bioengineering and biotechnology 3, 155, Annual Update in Intensive Care and Emergency Medicine 2015, 573-586. Healthy ML Clinical Inference Machine Learning. Jake Albrecht (Sage Bionetworks) Marco Ciccone (Politecnico di Torino) Tao Qin (Microsoft Research) Datasets and Benchmarks Chair. IY Chen, P Szolovits, M Ghassemi Critical Care 19 (1), 1-9, State of the Art Review: The Data Revolution in Critical Care 99 2015 Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR Professor Ghassemi has previously served as a NeurIPS Workshop Co-Chair and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). Her work has been featured in popular press such as MIT News, NVIDIA, Huffington Post. Its people. We capture data about the motions of patient's vocal folds to determine if their vocal behavior is normal or abnormal. Theres also the matter of who will collect it and vet it. N1 - Funding Information: The authors thank Rediet Abebe for helpful discussions and contributions to an early draft and Peter Szolovits, Pang Wei Koh, Leah Pierson, Berk Ustun, and Tristan Naumann for useful comments and feedback. If used carefully, this technology could improve performance in health care and potentially reduce inequities, Ghassemi says. A full list of Professor Ghassemis publications can be found here. Challenges to the Reproducibility of Machine Learning Models in Health Care. Open Mic session on "Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data". Magazine Basic created by c.bavota. Dr. Marzyeh Ghassemi leads the Healthy Machine Learning lab at MIT, a group focused on using machine learning to improve delivery of robust, private, fair, and Les articles suivants sont fusionns dans GoogleScholar. Room E25-330 degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Roth, K., Milbich, T., Ommer, B., Cohen, J. P.,Ghassemi, M. (2021). From 2013-2014, she was a student representative on MITs Womens Advisory Group Presidential Committee, and additionally was elected as a Graduate Student Council (GSC) Housing Community Activities Co-Chair. Imagine if we could take data from doctors that have the best performance and share that with other doctors that have less training and experience, Ghassemi says. This led the GSC to commit $30,000 to a pilot for the program, which was matched by the administration. But does that really show that medical treatment itself is free from bias? Upon a closer look, she saw that models often worked differently specifically worse for populations including Black women, a revelation that took her by surprise. Engineering & Science IMES PhD programs, select Marzyeh Ghassemi as a PI you are interested in working with. WebSept 2022 - Marzyeh Ghassemi co-authored a new article in Nature Medicine on bias in AI healthcare datasets, and was interviewed by the Healthcare Strategies podcast. Daryush Mehta, Jarrad H. Van Stan, Matias Zaartu. A new method could provide detailed information about internal structures, voids, and cracks, based solely on data about exterior conditions. [1] She currently holds the Canada CIFAR Artificial Intelligence (AI) Chair position. She also is on the Senior Advisory Council of Women in Machine Learning (WiML) and founded the ACM Conference on Health, Inference and Learning (ACM CHIL). Read more about our Usingexplainability methods can worsen model performance on minoritiesin these settings. How many minutes does it take to drive 23 miles? As an MIT undergrad interested in an UROP: Contact Fern Keniston (fern@csail.mit.edu) to determine if there are research slots available for the semester, and schedule a 30 minute session with Dr. Ghassemi. Prior to her PhD in Computer Science at MIT, she received an MSc. Research Directions and McDermott, M., Nestor, B., Kim, E., Zhang, W., Goldenberg, A., Szolovits, P., Ghassemi, M. (2021). ", "MIT Uses Deep Learning to Create ICU, EHR Predictive Analytics", "Using machine learning to improve patient care", "How machine learning can help with voice disorders", "2018 Innovator Under 35: Marzyeh Ghassemi - MIT Technology Review", "Eight U of T researchers named AI chairs by Canadian Institute for Advanced Research", "Six U of T researchers join Vector Institute", "Former Google CEO lauds role of universities in Canada's innovation ecosystem", "Marzyeh Ghassemi: From MIT and Google to the Department of Medicine", "29 researchers named to first cohort of Canada CIFAR Artificial Intelligence Chairs", "From AI to immigrant integration: 56 U of T researchers supported by Canada Research Chairs Program", "Marzyeh Ghassemi - Google Scholar Citations", https://en.wikipedia.org/w/index.php?title=Marzyeh_Ghassemi&oldid=1145490261, Academic staff of the University of Toronto, Articles using Template Infobox person Wikidata, Creative Commons Attribution-ShareAlike License 3.0, The Disparate Impacts of Medical and Mental Health with AI. M Ghassemi, LA Celi, DJ Stone Prior to her PhD in Computer Science at MIT, she received an MSc. She is currently an assistant professor at the University of Toronto's Department of Computer Science and Faculty of Medicine, and is a Canada CIFAR Artificial Intelligence (AI) chair and Canada Research Chair (Tier Two) in machine learning for health. M Ghassemi, LA Celi, JD Stone IEEE Transactions on Biomedical Engineering Volume 61, Issue 6, Page: 16681675 asTBME.2013.2297372 Prof. Marzyeh Ghassemi speaks with WBUR reporter Geoff Brumfiel about her research studying the use of artificial intelligence in healthcare. Following the publication of the original article [], we were notified that current affiliations 17, 18 and 19 were erroneously added to the first author rather than the senior author (Marzyeh Ghassemi). The Lancet Digital Health 3 (11), e745-e750. Annual Update in Intensive Care and Emergency Medicine 2015, 573-586, Predicting early psychiatric readmission with natural language processing of narrative discharge summaries 95 2016 We focus on furthering the application of technology and artificial intelligence in medicine and health-care. Cambridge, MA 02139. arXiv preprint arXiv:2006.11988, Unfolding Physiological State: Mortality Modelling in Intensive Care Units 225 2014 Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Irene Y. Chen, Rajesh Ranganath Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. She has also organized and MITs first Hacking Discrimination event, and was awarded MITs 2018 Seth J. Teller Award for Excellence, Inclusion and Diversity. Cambridge, MA 02139-4307 On leave. Les, Le dcompte "Cite par" inclut les citations des articles suivants dans GoogleScholar. [4], During her PhD, Ghassemi collaborated with doctors based within Beth Israel Deaconess Medical Center's intensive care unit and noted the extensive amount of clinical data available. A campus summit with the leader and his delegation centered around dialogue on biotechnology and innovation ecosystems. MIT News | Massachusetts Institute of Technology, The downside of machine learning in health care. WebMarzyeh Ghassemi Academic Research @ MIT CSAIL Research - Papers, Talks & Proceedings Curriculum vitae Refereed Conference Papers Clinical Intervention Prediction and Understanding using Deep Networks Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi MLHC 2017, Boston, MA. What is the cast of surname sable in maharashtra? Translational psychiatry 6 (10), e921-e921, Can AI Help Reduce Disparities in General Medical and Mental Health Care? Leveraging a critical care database: SSRI use prior to ICU admission is associated with increased hospital mortality. She has also organized and MITs first Thats different from the applications where existing machine-learning algorithms excel like object-recognition tasks because practically everyone in the world will agree that a dog is, in fact, a dog. Going further, we show that using treatment patterns and clinical notes, we are able to infer a patient's race. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Prior to MIT, Marzyeh received B.S. Pranav Rajpurkar, Emma Chen, Eric J. Topol. Coming from computers, the product of machine-learning algorithms offers the sheen of objectivity, according to Ghassemi. Copy. The event still happens every Monday in CSAIL. (33% Using ambulatory voice monitoring to investigate common voice disorders: Research update. The research center will support two nonprofits and four government agencies in designing randomized evaluations on housing stability, procedural justice, transportation, income assistance, and more. 77 Massachusetts Ave. We find that race, even in the great equalizer of end-of-life care, does continue to influence the treatments administered to a patient. Marzyeh Ghassemi is a Visiting Researcher with Googles Verily and a post-doc in the Clinical Decision Making Group at MITs Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Dr. Peter Szolovits. Learning to detect vocal hyperfunction from ambulatory necksurface acceleration features: Initial results for vocal fold nodules Her work has been featured in popular press such as degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University, worked at Intel Corporation, and received an MSc. Chen, I., Szolovits, P., and. Prior to MIT, Marzyeh received B.S. Marzyeh Ghassemi is an assistant professor at MIT and a faculty member at the Vector Institute (and a 35 Innovators honoree in 2018). M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, As an MIT MEng: Contact Fern Keniston (fern@csail.mit.edu) with a topic and research plan that is relevant to the group. Credit: Unsplash/CC0 Public Domain. [14][15], Ghassemi is a faculty member at the Vector Institute. Even mechanical devices can contribute to flawed data and disparities in treatment. Ghassemi has received BS degrees in computer science and electrical engineering from New Mexico State University, an MSc degree in biomedical engineering from Oxford University, and PhD in computer science from MIT. The Campaign was chaired by Dr. Ted Shortliffe (who also offered a 1:1 match for all donations up to WebMarzyeh Ghassemi, PhD Core Faculty Herman L. F. von Helmholtz Career Development Professor Assistant Professor, Electrical Engineering and Computer Science and Institute When was AR 15 oralite-eng co code 1135-1673 manufactured? Our team uses accelerometers and machine learning to help detect vocal disorders. Selected for a TBME Spotlight; Cited 10 times in the following year. Website Google Scholar. J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, Ghassemis research interests span representation learning, behavioral ML, healthcare ML, and healthy ML. Short-Term Mortality Prediction for Elderly Professor She is currently an assistant professor at the University of Toronto's Department of Computer Science and Faculty of Medicine, and is a Canada CIFAR Artificial Intelligence (AI) chair and Canada Research Chair (Tier Two) in machine learning for health. WebWhy aren't mistakes always a bad thing? KDD 2014, A multivariate timeseries modeling approach to severity of illness assessment and forecasting in icu with sparse, heterogeneous clinical data 192 2015 We really need to collect this data and audit it., The challenge here is that the collection of data is not incentivized or rewarded, she notes. Anna Rumshisky. A Rumshisky, M Ghassemi, T Naumann, P Szolovits, VM Castro, M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, M Ghassemi, MAF Pimentel, T Naumann, T Brennan, DA Clifton, Twenty-Ninth AAAI Conference on Artificial Intelligence, M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath, AMIA Summits on Translational Science Proceedings 191. (*) These authors contributed equally, and should be considered co-first authors. Dr. Marzyeh Ghassemi leads the Healthy Machine Learning lab at MIT, a group focused on using machine learning to improve delivery of robust, private, fair, and equitable healthcare. [1][2][3], In 2012, Ghassemi was a member of the Sana AudioPulse team, who won the GSMA Mobile Health Challenge as a result of developing a mobile phone app to screen for hearing impairment remotely. Simultaneous Similarity-based Self-Distillation for Deep Metric Learning, A comprehensive EHR timeseries pre-training benchmark, An empirical framework for domain generalization in clinical settings. Association for Health Learning and Inference. COVID-19 Image Data Collection: Prospective Predictions Are the Future, The potential of artificial intelligence to bring equity in health care, How an AI tool for fighting hospital deaths actually worked in the real world, Using machine learning to improve patient care. She served on MITs Presidential Committee on Foreign Scholarships from 2015-2018, working with MIT students to create competitive applications for distinguished international scholarships. WebMarzyeh Ghassemi, Luke Oakden-Rayner, Andrew L Beam The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to Language links are at the top of the page across from the title. Can AI Help Reduce Disparities in General Medical and Mental Health Care? WebMarzyeh Ghassemi is an assistant professor at MIT in the Department of Electrical Engineering and Computer Science and at the Institute for Medical Engineering Marzyeh Ghassemi is a Canada-based researcher in the field of computational medicine, where her research focuses on developing machine-learning algorithms to inform health-care decisions. The Healthy ML group tackles the many novel technical opportunities for machine learning in health, and works to make important progress with careful application to this domain. One of her focuses is on real-world applications of machine learning, such as turning diverse clinical data into cohesive information with the ability to predict patient needs. Understanding vasopressor intervention and weaning: Risk prediction in a public heterogeneous clinical time series database. Our analysis agrees with previous studies that nonwhites tend to receive more aggressive (high-risk, high reward) treatments, such as mechanical ventilation than non-whites, despite receiving comparable-or-moderately-less noninvasive treatments. [2][10], Ghassemi then joined as an assistant professor at the University of Toronto in fall 2018, where she was co-appointed to the Department of Computer Science and the University of Toronto's Faculty of Medicine, making her the first joint hire in computational medicine for the university. And given that I am a visible minority woman-identifying computer scientist at MIT, I am reasonably certain that many others werent aware of this either., In a paper published Jan. 14 in the journal Patterns, Ghassemi who earned her doctorate in 2017 and is now an assistant professor in the Department of Electrical Engineering and Computer Science and the MIT Institute for Medical Engineering and Science (IMES) and her coauthor, Elaine Okanyene Nsoesie of Boston University, offer a cautionary note about the prospects for AI in medicine. WebDr. In 2015, she also worked as a graduate student member of MITs CJAC (Corporation Joint Advisory Committee on Institute-wide Affairs), a committee to which the Corporation can turn for consideration and advice on special Institute-wide issues. From 20132014, she was a student representative on MITs Womens Advisory Group Presidential Committee, and additionally was elected as a Graduate Student Council (GSC) Housing Community Activities Co-Chair. And these deficiencies are most acute when oxygen levels are low precisely when accurate readings are most urgent. Machine Learning for Healthcare Conference, 147-163, State of the art review: the data revolution in critical care 99 2015 real-world applications of machine learning, such as turning diverse clinical data into cohesive information with the ability to predict patient needs. Reproducibleandethical machine learningin health are important, along with improved understanding ofthe bias in that may be present in models learned with medical images,clinical notes, or throughprocesses and devices. Professor Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Reviews 35 Innovators Under 35. Her work has appeared in KDD, AAAI, IEEE TBME, MLHC, JAMIA, and AMIA-CRI; she has also co-organized the NIPS 2016 Machine Learning for Healthcare (ML4HC) and 2014 Women in Machine Learning (WIML) workshops. Similarly, women face increased risks during metal-on-metal hip replacements, Ghassemi and Nsoesie write, due in part to anatomic differences that arent taken into account in implant design. Facts like these could be buried within the data fed to computer models whose output will be undermined as a result. JMLR Workshop and Conference Track Volume 56, IEEE Transactions on Biomedical Engineering, OHDSI Collaborator Showcase in OHDSI Symposium. Room 1-206 Invited Talk on "Physiological Acuity Modelling with (Ugly) Temporal Clinical Data", First place winner of the MIT $100K Accelerate $10,000 Daniel M. Lewin Accelerate Prize. MIT EECS or WebMarzyeh Ghassemi (MIT) Saadia Gabriel (University of Washington) Competition Chair. Professor Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Reviews 35 Innovators Under 35. She holds MIT affiliations with the Jameel Clinic and CSAIL. Emily Denton (Google) Joaquin Vanschoren (Eindhoven University of Technology) ACM Conference on Health, Inference and Learning, Association for Health Learning and Inference. This CoR takes a unified approach to cover the full range of research areas required for success in artificial intelligence, including hardware, foundations, software systems, and applications. Professor Ghassemi has previously served as a NeurIPS Workshop Co-Chair and General Chair for the [2][6][11][12][13] Ghassemi's lab is titled the Machine Learning for Health (ML4H) lab. The program is now fully funded by MIT, and considered a success. Marzyeh Ghassemi is an assistant professor and the Hermann L. F. von Helmholtz Professor with appointments in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering & Science at MIT. ACM Conference on Health, Inference and Learning (CHIL). In 2015, she also worked as a graduate student member of MITs CJAC (Corporation Joint Advisory Committee on Institute-wide Affairs), a committee to which the Corporation can turn for consideration and advice on special Institute-wide issues. WebDr. Copyright 2023 Marzyeh Ghassemi. WebDr. Mobility-related data show the pandemic has had a lasting effect, limiting the breadth of places people visit in cities. The problem is not machine learning itself, she insists. All Rights Reserved. Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches. What is sunshine DVD access code jenna jameson? Health is important, and improvements in health improve lives. WebMarzyeh Ghassemi (MIT) Saadia Gabriel (University of Washington) Competition Chair. Prior to her PhD in Computer Science at MIT, she received an MSc. 2021. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. The Healthy ML group at MIT, led by Data augmentation is a com-mon method used to prevent overtting and im-prove OOD generalization. AMA Journal of Ethics 21 (2), 167-179, Using ambulatory voice monitoring to investigate common voice disorders: Research update And what does AI have to do with that? When discussing racial disparities in medical treatments, critics often cite social factors as confounders which explain away any differences. [9], Upon completing her PhD, Ghassemi was affiliated with both Alphabets Verily (as a visiting researcher) and at MIT (as a part-time post-doctoral researcher in Peter Szolovits' Computer Science and Artificial Intelligence Lab). Such asymmetries in the latent space must be corrected methodologically withmethods that distill multi-level knowledge, or deliberately targeted todecorrelate sensitive information from the prediction setting. Colak, E., Moreland, R., Ghassemi, M. (2021). Wiki User. Computer Science & Artificial Intelligence Laboratory. Hundreds packed Killian and Hockfield courts to enjoy student performances, amusement park rides, and food ahead of Inauguration Day. Doctors know what it means to be sick, Ghassemi explains, and we have the most data for people when they are sickest. Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi. More work should be done to establish howadvice from biased AI can be mitigated by delivery method, for instance by presenting it descriptively rather than prescriptively. Download PDF. Furthermore, there is still great uncertainty about medical conditions themselves. G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, Machine Learning for Healthcare Conference, 249-269, A Raghu, M Komorowski, I Ahmed, L Celi, P Szolovits, M Ghassemi. AI in health and medicine. [18] Ghassemi has been cited over 1900 times, and has an h-index and i-10 index of 23 and 36 respectively. She also founded the non-profit She is currently on leave from the University of Toronto Departments of Computer Science and Medicine. But the data they are given are produced by humans, who are fallible and whose judgments may be clouded by the fact that they interact differently with patients depending on their age, gender, and race, without even knowing it. This led the GSC to commit $30,000 to a pilot for the program, which was matched by the administration. 2014-05-24 01:29:44. Previously, she was a Visiting Researcher with Alphabets Verily and a post-doc with Peter Szolovits at MIT. Professor Marzyeh Ghassemi empowered this weeks audience at the AI for Good seminar series with her critical and thoughtful assessment of the current state and future potential of AI in healthcare. First Place winner at the 2012 GSMA Mobile Health Student Challenge in Cape Town! A reviewled Prof. Marzyeh Ghassemi has found that a major issue in health-related machine learning models is the relative scarcity of publicly available data sets in medicine, reports Emily Sohn for Nature. Do you have pictures of Gracie Thompson from the movie Gracie's choice? Assistant Professor, EECS.CSAIL/IMES, MIT. WebMarzyeh Ghassemi, Luke Oakden-Rayner, Andrew L Beam The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. Ghassemi recommends assembling diverse groups of researchers clinicians, statisticians, medical ethicists, and computer scientists to first gather diverse patient data and then focus on developing fair and equitable improvements in health care that can be deployed in not just one advanced medical setting, but in a wide range of medical settings., The objective of the Patterns paper is not to discourage technologists from bringing their expertise in machine learning to the medical world, she says.
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