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  1. Article ; Online: Measuring Palliative Care Communication via Telehealth: A Pilot Study.

    Tarbi, Elise C / Durieux, Brigitte N / Brain, Jessie M / Kwok, Anne / Umeton, Renato / Samineni, Soujanya / Tulsky, James A / Lindvall, Charlotta

    Journal of pain and symptom management

    2023  Volume 66, Issue 1, Page(s) e155–e161

    MeSH term(s) Humans ; Palliative Care ; Pilot Projects ; Hospice and Palliative Care Nursing ; Telemedicine ; Communication ; Videoconferencing
    Language English
    Publishing date 2023-04-08
    Publishing country United States
    Document type Letter
    ZDB-ID 639142-4
    ISSN 1873-6513 ; 0885-3924
    ISSN (online) 1873-6513
    ISSN 0885-3924
    DOI 10.1016/j.jpainsymman.2023.04.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.

    Lindvall, Charlotta / Deng, Chih-Ying / Agaronnik, Nicole D / Kwok, Anne / Samineni, Soujanya / Umeton, Renato / Mackie-Jenkins, Warren / Kehl, Kenneth L / Tulsky, James A / Enzinger, Andrea C

    JCO clinical cancer informatics

    2022  Volume 6, Page(s) e2100136

    Abstract: Purpose: Symptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes (PROs) are valuable for monitoring symptoms, yet there are many challenges to collecting PROs at scale. ...

    Abstract Purpose: Symptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes (PROs) are valuable for monitoring symptoms, yet there are many challenges to collecting PROs at scale. We sought to develop, test, and externally validate a deep learning model to extract symptoms from unstructured clinical notes in the electronic health record.
    Methods: We randomly selected 1,225 outpatient progress notes from among patients treated at the Dana-Farber Cancer Institute between January 2016 and December 2019 and used 1,125 notes as our training/validation data set and 100 notes as our test data set. We evaluated the performance of 10 deep learning models for detecting 80 symptoms included in the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework. Model performance as compared with manual chart abstraction was assessed using standard metrics, and the highest performer was externally validated on a sample of 100 physician notes from a different clinical context.
    Results: In our training and test data sets, 75 of the 80 candidate symptoms were identified. The ELECTRA-small model had the highest performance for symptom identification at the token level (ie, at the individual symptom level), with an F1 of 0.87 and a processing time of 3.95 seconds per note. For the 10 most common symptoms in the test data set, the F1 score ranged from 0.98 for anxious to 0.86 for fatigue. For external validation of the same symptoms, the note-level performance ranged from F1 = 0.97 for diarrhea and dizziness to F1 = 0.73 for swelling.
    Conclusion: Training a deep learning model to identify a wide range of electronic health record-documented symptoms relevant to cancer care is feasible. This approach could be used at the health system scale to complement to electronic PROs.
    MeSH term(s) Deep Learning ; Electronic Health Records ; Fatigue ; Humans ; Neoplasms/drug therapy ; Neoplasms/therapy ; Patient Reported Outcome Measures
    Language English
    Publishing date 2022-06-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2473-4276
    ISSN (online) 2473-4276
    DOI 10.1200/CCI.21.00136
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Federated benchmarking of medical artificial intelligence with MedPerf.

    Karargyris, Alexandros / Umeton, Renato / Sheller, Micah J / Aristizabal, Alejandro / George, Johnu / Wuest, Anna / Pati, Sarthak / Kassem, Hasan / Zenk, Maximilian / Baid, Ujjwal / Narayana Moorthy, Prakash / Chowdhury, Alexander / Guo, Junyi / Nalawade, Sahil / Rosenthal, Jacob / Kanter, David / Xenochristou, Maria / Beutel, Daniel J / Chung, Verena /
    Bergquist, Timothy / Eddy, James / Abid, Abubakar / Tunstall, Lewis / Sanseviero, Omar / Dimitriadis, Dimitrios / Qian, Yiming / Xu, Xinxing / Liu, Yong / Goh, Rick Siow Mong / Bala, Srini / Bittorf, Victor / Reddy Puchala, Sreekar / Ricciuti, Biagio / Samineni, Soujanya / Sengupta, Eshna / Chaudhari, Akshay / Coleman, Cody / Desinghu, Bala / Diamos, Gregory / Dutta, Debo / Feddema, Diane / Fursin, Grigori / Huang, Xinyuan / Kashyap, Satyananda / Lane, Nicholas / Mallick, Indranil / Mascagni, Pietro / Mehta, Virendra / Ferro Moraes, Cassiano / Natarajan, Vivek / Nikolov, Nikola / Padoy, Nicolas / Pekhimenko, Gennady / Reddi, Vijay Janapa / Reina, G Anthony / Ribalta, Pablo / Singh, Abhishek / Thiagarajan, Jayaraman J / Albrecht, Jacob / Wolf, Thomas / Miller, Geralyn / Fu, Huazhu / Shah, Prashant / Xu, Daguang / Yadav, Poonam / Talby, David / Awad, Mark M / Howard, Jeremy P / Rosenthal, Michael / Marchionni, Luigi / Loda, Massimo / Johnson, Jason M / Bakas, Spyridon / Mattson, Peter

    Nature machine intelligence

    2023  Volume 5, Issue 7, Page(s) 799–810

    Abstract: Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and ... ...

    Abstract Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.
    Language English
    Publishing date 2023-07-17
    Publishing country England
    Document type Journal Article
    ISSN 2522-5839
    ISSN (online) 2522-5839
    DOI 10.1038/s42256-023-00652-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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