Ben Wissel, MD, PhD
@BDWissel
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Neurosurgery Resident @DukeNeurosurg | PhD in Machine Learning for Biomedical Data | Views expressed are my own and not those of my employer.
Durham, NC
Joined November 2019
🧠🤖 Excited to share the results of our #RCT in @EpilepsiaJourn examining the impact of #AI-powered clinical decision support on epilepsy surgery referrals. #Epilepsy #Neurosurgery #MachineLearning #DigitalHealth 1/10 https://t.co/FjS7u7HkNK
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Amazing work @BDWissel ! Your subI presentation last year on this topic sparked a whole new dimension to @DukeSpine as well. Who says we can’t learn from our trainees and from other subspecialties in #neurosurgery?
Thank you @Englot for highlighting our work identifying epilepsy surgery candidates. Great discussion about where we are and where we need to go. https://t.co/o001zZQj4Y
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Thank you @Englot for highlighting our work identifying epilepsy surgery candidates. Great discussion about where we are and where we need to go. https://t.co/o001zZQj4Y
journals.sagepub.com
Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study Wissel BD, Greiner HM, Glauser TA, Pestia...
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🔜 Duke Neurosurgery Thank you to everyone that stood by me during my wildest dreams. This is only the beginning, and to all that are watching: if I can, then you can too. #Match2024
#Neurosurgery
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Help us roll out the blue carpet for these three as they prepare to join us at Duke Neurosurgery! #Match2024
#LetsGo !!💫 🎉 Tyrone DeSpenza @YaleMed @tdespenza Julia Duvall @harvardmed @juliabduvall Benjamin Succop @UNC
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This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with #epilepsy who require resective surgery from those who do not. https://t.co/d4TIhZaUFQ
#NeuroTwitter
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Check out our new article! Can we study 10s of 1000s of PD patients in EHR? Does the data accurately represent all races? Not yet! @AlbertoEspay @BDWissel @bkissela
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Wishing these three the best first week ever. Let the journey begin! #PGY1 #MedTwitter @CarolineCFolz @BDWissel
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So important. Epilepsy surgery is associated with higher 10 year survival for pediatric patients with drug resistant #epilepsy than those on anti-seizure meds only Free full manuscript: https://t.co/61Jy8v9Zru
@epilepsysociety @epilepsyaction @DEE_Pconnection @EndEpilepsy
New study by @sandi_lam in @LancetChildAdol: In observational cohort (n=18,292), compared to medical management of epilepsy, VNS reduced risk of death by 40% and resective surgery reduced risk of death by 81%. https://t.co/y6quhahob6
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Can GPT-4 solve really hard medical cases and come up with a good list of differential diagnoses? @zahirkanjee @byrondcrowe and my study is out in @JAMA_current , and the short answer is, “Yes.” But what does this all mean? 🧵⬇️
In this study, a generative artificial intelligence (AI) model provided the correct diagnosis in its differential in 64% of challenging cases and as its top diagnosis in 39%.
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"Health system-scale language models are all-purpose prediction engines" by @ekoermann new in @Nature Will be interesting to see whether hospitals develop their own models, or if one (prob commercially owned) mega-model will rule them all. https://t.co/N6UF9cQESm
nature.com
Nature - A clinical language model trained on unstructured clinical notes from the electronic health record enhances prediction of clinical and operational events.
🆕 @Nature An important LLM #AI for healthcare report using all unstructured clinical notes @nyulangone from 336,000 patients to predict readmission, mortality, comorbidity, and insurance denials https://t.co/pIJ6nDellT by @ekoermann and colleagues
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New study by @sandi_lam in @LancetChildAdol: In observational cohort (n=18,292), compared to medical management of epilepsy, VNS reduced risk of death by 40% and resective surgery reduced risk of death by 81%. https://t.co/y6quhahob6
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Using transformers to study the natural history of epilepsy from info in clinic notes https://t.co/t5A2IWEi84
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We excited to expand this system across healthcare systems. Next up, @DukeHealth! cc @Dukeneurosurg 10/10
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These alerts were sent to specialized epileptologists. We think they can have a huge impact in community practices, too. 9/10
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Conclusions: Machine learning–based automated alerts can significantly improve the utilization of epilepsy surgery evaluations. #AIinHealthcare #EpilepsyCare 8/10
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Importantly, nine patients (4.4%) in the alert group underwent epilepsy surgery, compared to NONE in the control (no alert) group. The power of #AI in action. #DigitalHealth #EpilepsySurgery 7/10
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Results: Patients whose provider received an alert were 3x MORE LIKELY TO BE REFERRED for a presurgical evaluation compared to the control group (9.8% vs. 3.1%, HR = 3.21). 6/10
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The primary outcome was referral for a neurosurgical evaluation. We followed patients for a median of 24 months. 5/10
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A total of 4,858 children were screened, with 284 (5.8%) identified as potential surgical candidates. Of these, the majority (2:1) were randomized to have their providers alerted. #EpilepsySurgery #DigitalHealth 4/10
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