Keywords
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribers receive full online access to your subscription and archive of back issues up to and including 2002.
Content published before 2002 is available via pay-per-view purchase only.
Subscribe:
Subscribe to Clinics in Laboratory MedicineReferences
- Artificial intelligence in healthcare.Nat Biomed Eng. 2018; 2: 719-731
- A review of challenges and opportunities in machine learning for health.AMIA Summits Transl Sci Proc. 2020; 2020: 191
- Key challenges for delivering clinical impact with artificial intelligence.BMC Med. 2019; 17: 195
- The potential for artificial intelligence in healthcare.Future Healthc J. 2019; 6: 94-98
- Deep learning for healthcare: review, opportunities and challenges.Brief Bioinform. 2018; 19: 1236-1246
- Do no harm: a roadmap for responsible machine learning for health care.Nat Med. 2019; 25: 1337-1340
- Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453
- Machine learning and health need better values.NPJ Digital Med. 2022; 5: 1-4
- Lessons and tips for designing a machine learning study using EHR data.J Clin Translational Sci. 2021; 5
- The clinician’s guide to the machine learning galaxy.Front Physiol. 2021; 12: 658583
- A guide to deep learning in healthcare.Nat Med. 2019; 25: 24-29
- An introduction to machine learning for clinicians.Acad Med. 2019; 94: 1433-1436
- Fundamentals of machine learning for healthcare. Coursera.(Available at:) (Accessed June 10, 2022)
- AI in healthcare. Coursera.(Available at:) (Accessed June 10, 2022)
Ahmad MA, Eckert C, Teredesai A. Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. BCB ’18. Association for Computing Machinery; 2018:559–560.
- The national early warning score 2 (NEWS2). Clinical medicine.J R Coll Physicians Lond. 2019; 19: 260
- Machine learning in healthcare: review, opportunities and challenges.Machine Learn Internet Med Things Healthc. 2021; : 23-45
Shailaja K, Seetharamulu B, Jabbar MA. Machine learning in healthcare: a review. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE; 2018:910–914.
- Machine learning for medical imaging: methodological failures and recommendations for the future.NPJ digital Med. 2022; 5: 1-8
- A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises.Proc IEEE. 2021; 109: 820-838
- Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.NPJ digital Med. 2021; 4: 1-23
Irvin J., Rajpurkar P., Ko M., et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI. Vol 33, 1/27/2019 - 2/1/2019, 590–597.
- MIMIC-III, a freely accessible critical care database.Scientific data. 2016; 3: 1-9
Wang X., Peng Y., Lu L., et al. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI. 7/22/2017 - 7/25/2017, 2097–2106.
- Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning.arXiv. 2017; https://doi.org/10.48550/arXiv.1711.05225
- A novel approach for multi-label chest X-ray classification of common thorax diseases.IEEE Access. 2019; 7: 64279-64288
- Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.Nat Med. 2021; 27: 2176-2182
Seyyed-Kalantari L., Liu G., McDermott M., et al. CheXclusion: fairness gaps in deep chest X-ray classifiers. In: BIOCOMPUTING 2021: Proceedings of the pacific Symposium. World Scientific; 2020:232–243. Availabe at: https://www.atsjournals.org/doi/epdf/10.1164/ajrccm-conference.2018.197.1_MeetingAbstracts.A3299.
- AI recognition of patient race in medical imaging: a modelling study.The Lancet Digital Health. 2022; 4: E406-E414
- Deep learning for diabetic retinopathy detection and classification based on fundus images: a review.Comput Biol Med. 2021; 135: 104599
Beede E, Baylor E, Hersch F, et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ; 2020:1–12.
- Artificial intelligence in radiology: 100 commercially available products and their scientific evidence.Eur Radiol. 2021; 31: 3797-3804
- The eICU Collaborative Research Database, a freely available multi-center database for critical care research.Scientific Data. 2018; 5: 1-13
McDermott M., Yan T., Naumann T., et al. Semi-supervised biomedical translation with cycle wasserstein regression GANs. In: Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA. Vol 32. 2/2/2018 - 2/7/2018.
McDermott M., Nestor B., Kim E., et al. A comprehensive EHR timeseries pre-training benchmark. In: Proceedings of the Conference on Health, Inference, and Learning (Virtual). 4/8/2021 - 4/10/2021, 257–278.
Suresh H, Hunt N, Johnson A, Celi LA, Szolovits P, Ghassemi M. Clinical intervention prediction and understanding with deep neural networks. In: Machine Learning for Healthcare Conference. PMLR; 2017:322–337.
- Learning to diagnose with LSTM recurrent neural networks.arXiv. 2015; https://doi.org/10.48550/arXiv.1511.03677
Yoon J, Jordon J, van der Schaar M. GAIN: Missing Data Imputation using generative adversarial nets. In: Dy JG, Krause A, eds Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018. Vol 80. Proceedings of Machine Learning Research. PMLR; 2018:5675-5684.
Nestor B, McDermott MBA, Boag W, et al. Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. In: Doshi-Velez F, Fackler J, Jung K, et al., eds Proceedings of the 4th Machine Learning for Healthcare Conference. Vol 106. Proceedings of Machine Learning Research. PMLR; 09–10 Aug 2019:381–405.
- Why is my classifier discriminatory?.in: Advances in neural information processing systems. 31. Curran Associates, Inc, 2018 (Available at:)
- Ethical machine learning in healthcare.Annu Rev Biomed Data Sci. 2021; 4: 123-144
Futoma J, Hariharan S, Heller K, et al. An improved multi-output gaussian process rnn with real-time validation for early sepsis detection. In: Machine Learning for Healthcare Conference. PMLR; 2017:243–254.
Futoma J, Hariharan S, Heller K. Learning to detect sepsis with a multitask Gaussian process RNN classifier. In: International Conference on Machine Learning. PMLR; 2017:1174–1182.
- What is sepsis: investigating the heterogeneity of patient populations captured by different sepsis definitions.in: B43. Critical care: i still haven’t found what i'm looking for-identifying and managing sepsis. American Thoracic Society, 2018: A3299
- Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study.JMIR Med Inform. 2020; 8: e15182
- Towards regulatory-compliant mlops: oravizio’s journey from a machine learning experiment to a deployed certified medical product.SN Computer Sci. 2021; 2: 342
- Machine learning in patient flow: a review.Prog Biomed Eng. 2021; 3: 022002
- A systematic review of the prediction of hospital length of stay: towards a unified framework.PLoS Digital Health. 2022; 1: e0000017
- How we revolutionize the operational management of hospitals with Calyps AI. CALYPS.(Available at:) (Accessed May 16, 2022)
- Qventus. How Boston Medical Center uses automation for early discharge planning. Becker’s Health IT.(Accessed May 16, 2022)
- Deep learning in clinical natural language processing: a methodical review.J Am Med Inform Assoc. 2020; 27: 457-470
- Others. Clinical text data in machine learning: systematic review.JMIR Med Inform. 2020; 8: e17984
- Machine learning and natural language processing in mental health: systematic review.J Med Internet Res. 2021; 23: e15708
- The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.J Am Med Inform Assoc. 2020; 27: 1529-1537https://doi.org/10.1093/jamia/ocaa106
- Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT.Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020; 117: 1500-1519
McDermott MBA, Hsu TMH, Weng WH, Ghassemi M, Szolovits P. CheXpert++: approximating the CheXpert labeler for speed, differentiability, and probabilistic output. In: Doshi-Velez F, Fackler J, Jung K, et al., eds Proceedings of the 5th Machine Learning for Healthcare Conference. Vol 126. Proceedings of Machine Learning Research. PMLR; 07–08 Aug 2020:913–927.
- Reflex: flexible framework for relation extraction in multiple domains.Proceedings of the 18th BioNLP Workshop and Shared Task. 2019; W19-5004: 30-47
Roy A, Pan S. Incorporating medical knowledge in BERT for clinical relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. ; 2021:5357–5366.
Wei Q, Ji Z, Si Y, et al. Relation extraction from clinical narratives using pre-trained language models. In: AMIA Annual Symposium Proceedings. Vol 2019. American Medical Informatics Association; 2019:1236.
- Evaluating temporal relations in clinical text: 2012 i2b2 challenge.J Am Med Inform Assoc. 2013; 20: 806-813
- Language models are few-shot learners.Adv Neural Inf Process Syst. 2020; 33: 1877-1901
- Multitask prompted training enables zero-shot task generalization.Proceedings of the International Conference on Learning Representations. 2022; (Available at:)
- Clinically accurate chest x-ray report generation. In: machine Learning for Healthcare Conference.PMLR. 2019; 106: 249-269
- Automated radiology report generation using conditioned transformers.Inform Med Unlocked. 2021; 24: 100557
- Automated methods for the summarization of electronic health records.J Am Med Inform Assoc. 2015; 22: 938-947
Liang J, Tsou CH, Poddar A. A novel system for extractive clinical note summarization using EHR data. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop. ; 2019:46–54.
Abacha AB, M’rabet Y, Zhang Y, Shivade C, Langlotz C, Demner-Fushman D. Overview of the mediqa 2021 shared task on summarization in the medical domain. In: Proceedings of the 20th Workshop on Biomedical Language Processing. ; 2021:74–85.
- emrqa: a large corpus for question answering on electronic medical records.Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018; D18-1258: 2357-2368
Weng WH, Chung YA, Szolovits P. Unsupervised clinical language translation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ; 2019:3121–3131.
- ELIZA—a computer program for the study of natural language communication between man and machine.Commun ACM. 1966; 9: 36-45
- The medical futurist. The top 12 healthcare chatbots. The medical futurist.(Available at:) (Accessed May 18, 2022)
- Transformer-based behavioral representation learning enables transfer learning for mobile sensing in small datasets.arXiv. 2021; (Available at:)
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal.BMJ. 2020; 369: m1328
- Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.Nat Machine Intelligence. 2021; 3: 199-217
Gong JJ, Naumann T, Szolovits P, Guttag JV. Predicting clinical outcomes across changing electronic health record systems. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2017:1497–1505.
- The parable of google flu: traps in big data analysis.Science. 2014; 343: 1203-1205
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?.npj Digital Med. 2021; 4: 62
Adam GA, Chang CHK, Haibe-Kains B, Goldenberg A. Hidden risks of machine learning applied to healthcare: unintended feedback loops between models and future data causing model degradation. In: Doshi-Velez F, Fackler J, Jung K, et al., eds Proceedings of the 5th Machine Learning for Healthcare Conference. Vol 126. Proceedings of Machine Learning Research. PMLR; 07–08 Aug 2020:710–731.
Subbaswamy A, Schulam P, Saria S. Preventing failures due to dataset shift: learning predictive models that transport. In: Chaudhuri K, Sugiyama M, eds Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Vol 89. Proceedings of Machine Learning Research. PMLR; 16–18 Apr 2019:3118–3127.
- Scalable and accurate deep learning with electronic health records.npj Digital Med. 2018; 1: 18
- Transferring clinical prediction models across hospitals and electronic health record systems.in: Cellier P. Driessens K. Machine learning and knowledge discovery in databases. Springer International Publishing, 2020: 605-621
- Hidden stratification causes clinically meaningful failures in machine learning for medical imaging.CoRR. 2019; (abs/1909.12475. Available at:)
Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N. Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ; 2015:1721–1730.
- Predicting dire outcomes of patients with community acquired pneumonia.J Biomed Inform. 2005; 38: 347-366
Zhang H, Lu AX, Abdalla M, McDermott M, Ghassemi M. Hurtful words: quantifying biases in clinical contextual word embeddings. In: Proceedings of the ACM Conference on Health, Inference, and Learning. ; 2020:110–120.
- An algorithmic approach to reducing unexplained pain disparities in underserved populations.Nat Med. 2021; 27: 136-140
- A systematic study of bias amplification.arXiv. 2022; 2201: 11706
- Hidden in plain sight — reconsidering the use of race correction in clinical algorithms.N Engl J Med. 2020; 383: 874-882
- Reproducibility in machine learning for health research: still a ways to go.Sci Transl Med. 2021; 13: eabb1655
- Machine learning for health: algorithm auditing & quality control.J Med Syst. 2021; 45: 105
- The importance of interpretability and visualization in machine learning for applications in medicine and health care.Neural Comput Appl. 2020; 32: 18069-18083
- Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?.J Med Ethics. 2021; 48: 581-585
- Interpretability of machine learning-based prediction models in healthcare.Wiley Interdiscip Rev Data Min Knowl Discov. 2020; 10: e1379
- Explainable deep learning in healthcare: a methodological survey from an attribution view.Wires Mech Dis. 2022; 14: e1548
- The mythos of model interpretability.CoRR. 2016; (abs/1606.03490. Available at:)
Tonekaboni S, Joshi S, McCradden MD, Goldenberg A. What clinicians want: contextualizing explainable machine learning for clinical end use. In: Doshi-Velez F, Fackler J, Jung K, et al., eds Proceedings of the 4th Machine Learning for Healthcare Conference. Vol 106. Proceedings of Machine Learning Research. PMLR; 09–10 Aug 2019:359–380.
- The false hope of current approaches to explainable artificial intelligence in health care.Lancet Digital Health. 2021; 3: e745-e750
Poursabzi-Sangdeh F, Goldstein DG, et al. Manipulating and measuring model interpretability. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ; 2021:1–52.