Add pages for volume v252

Gemfile

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+source "https://rubygems.org"
+
+git_source(:github) {|repo_name| "https://github.com/#{repo_name}" }
+
+gem 'jekyll'
+
+group :jekyll_plugins do
+  gem 'github-pages'
+  gem 'jekyll-remote-theme'
+  gem 'jekyll-include-cache'
+  gem 'webrick'
+end
+
+# gem "rails"
+

README.md

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+# PMLR 252
+
+To suggest fixes to this volume please make a pull request containing the changes requested and a justification for the changes.
+
+To edit the details of this conference work edit the [_config.yml](./_config.yml) file and submit a pull request.
+
+To make changes to the individual paper details, edit the associated paper file in the [./_posts](./_posts) subdirectory.
+
+For details of how to publish in PMLR please check https://proceedings.mlr.press/faq.html
+
+For details of what is required to submit a proceedings please check https://proceedings.mlr.press/spec.html
+
+
+
+Published as Volume 252 by the Proceedings of Machine Learning Research on 25 November 2024.
+
+Volume Edited by:
+  * Kaivalya Deshpande
+  * Madalina Fiterau
+  * Shalmali Joshi
+  * Zachary Lipton
+  * Rajesh Ranganath
+  * Iñigo Urteaga
+
+Series Editors:
+  * Neil D. Lawrence

_config.yml

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+---
+booktitle: Proceedings of the 9th Machine Learning for Healthcare Conference
+volume: '252'
+shortname: MLHC
+conference_number: '9'
+year: '2024'
+start: &1 2024-08-16
+end: 2024-08-17
+published: 2024-11-25
+url: https://proceedings.mlr.press
+layout: proceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: MLHC-2024
+month: 0
+cycles: false
+bibtex_editor: Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton,
+  Zachary and Ranganath, Rajesh and Urteaga, I\~nigo
+editor:
+- given: Kaivalya
+  family: Deshpande
+- given: Madalina
+  family: Fiterau
+- given: Shalmali
+  family: Joshi
+- given: Zachary
+  family: Lipton
+- given: Rajesh
+  family: Ranganath
+- given: Iñigo
+  family: Urteaga
+title: Proceedings of Machine Learning Research
+description: |
+  Proceedings of the 9th Machine Learning for Healthcare Conference
+    Held in Toronto, Canada on 16-17 August 2024
+
+  Published as Volume 252 by the Proceedings of Machine Learning Research on 25 November 2024.
+
+  Volume Edited by:
+    Kaivalya Deshpande
+    Madalina Fiterau
+    Shalmali Joshi
+    Zachary Lipton
+    Rajesh Ranganath
+    Iñigo Urteaga
+
+  Series Editors:
+    Neil D. Lawrence
+date_str: 16--17 Aug
+author:
+  name: PMLR
+baseurl: "/v252"
+twitter_username: MLResearchPress
+github_username: mlresearch
+markdown: kramdown
+exclude:
+- README.md
+- Gemfile
+- ".gitignore"
+plugins:
+- jekyll-feed
+- jekyll-seo-tag
+- jekyll-remote-theme
+remote_theme: mlresearch/jekyll-theme
+style: pmlr
+permalink: "/:title.html"
+ghub:
+  edit: true
+  repository: v252
+display:
+  copy_button:
+    bibtex: true
+    endnote: true
+    apa: true
+  comments: false
+volume_type: Volume
+volume_dir: v252
+email: ''
+conference:
+  name: Machine Learning for Healthcare Conference
+  url:
+  location: Toronto, Canada
+  dates:
+  - *1
+  - 2024-08-17
+analytics:
+  google:
+    tracking_id: UA-92432422-1
+orig_bibfile: "/Users/neil/mlresearch/v252/mlhc2024_accepted_submissions.bib"
+# Site settings
+# Original source:  /Users/neil/mlresearch/v252/mlhc2024_accepted_submissions.bib

_posts/2024-11-25-asiimwe24a.md

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+---
+title: 'MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting'
+abstract: 'In medical reporting, the accuracy of radiological reports, whether generated
+  by humans or machine learning algorithms, is critical. We tackle a new task in this
+  paper: image- conditioned autocorrection of inaccuracies within these reports. Using
+  the MIMIC-CXR dataset, we first intentionally introduce a diverse range of errors
+  into reports. Subsequently, we propose a two-stage framework capable of pinpointing
+  these errors and then making corrections, simulating an autocorrection process.
+  This method aims to address the short- comings of existing automated medical reporting
+  systems, like factual errors and incorrect conclusions, enhancing report reliability
+  in vital healthcare applications. Importantly, our approach could serve as a guardrail,
+  ensuring the accuracy and trustworthiness of automated report generation. Experiments
+  on established datasets and state of the art report generation models validate this
+  method’s potential in correcting medical reporting errors.'
+openreview: iW9ItiwxyC
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: asiimwe24a
+month: 0
+tex_title: 'MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting'
+cycles: false
+bibtex_author: Asiimwe, Arnold Caleb and Coll-Vinent, Didac Suris and Rajpurkar, Pranav
+  and Vondrick, Carl
+author:
+- given: Arnold Caleb
+  family: Asiimwe
+- given: Didac Suris
+  family: Coll-Vinent
+- given: Pranav
+  family: Rajpurkar
+- given: Carl
+  family: Vondrick
+date: 2024-11-25
+address:
+container-title: Proceedings of the 9th Machine Learning for Healthcare Conference
+volume: '252'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 11
+  - 25
+pdf: https://raw.githubusercontent.com/mlresearch/v252/main/assets/asiimwe24a/asiimwe24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-11-25-banerjee24a.md

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+---
+title: Direct Preference Optimization for Suppressing Hallucinated Prior Exams in
+  Radiology Report Generation
+abstract: Recent advances in generative vision-language models (VLMs) have exciting
+  potential implications for AI in radiology, yet VLMs are also known to produce hallucinations,
+  nonsensical text, and other unwanted behaviors that can waste clinicians’ time and
+  cause patient harm. Drawing on recent work on direct preference optimization (DPO),
+  we propose a simple method for modifying the behavior of pretrained VLMs performing
+  radiology report generation by suppressing unwanted types of generations. We apply
+  our method to the prevention of hallucinations of prior exams, addressing a long-established
+  problem behavior in models performing chest X-ray report generation. Across our
+  experiments, we find that DPO fine-tuning achieves a 3.2-4.8x reduction in lines
+  hallucinating prior exams while maintaining model performance on clinical accuracy
+  metrics. Our work is, to the best of our knowledge, the first work to apply DPO
+  to medical VLMs, providing a data- and compute- efficient way to suppress problem
+  behaviors while maintaining overall clinical accuracy.
+openreview: kvYYP1LfRq
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: banerjee24a
+month: 0
+tex_title: Direct Preference Optimization for Suppressing Hallucinated Prior Exams
+  in Radiology Report Generation
+cycles: false
+bibtex_author: Banerjee, Oishi and Zhou, Hong-Yu and Wu, Kay and Adithan, Subathra
+  and Kwak, Stephen and Rajpurkar, Pranav
+author:
+- given: Oishi
+  family: Banerjee
+- given: Hong-Yu
+  family: Zhou
+- given: Kay
+  family: Wu
+- given: Subathra
+  family: Adithan
+- given: Stephen
+  family: Kwak
+- given: Pranav
+  family: Rajpurkar
+date: 2024-11-25
+address:
+container-title: Proceedings of the 9th Machine Learning for Healthcare Conference
+volume: '252'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 11
+  - 25
+pdf: https://raw.githubusercontent.com/mlresearch/v252/main/assets/banerjee24a/banerjee24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-11-25-chan24a.md

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+---
+title: Leveraging LLMs for Multimodal Medical Time Series Analysis
+abstract: 'The complexity and heterogeneity of data in many real-world applications
+  pose significant challenges for traditional machine learning and signal processing
+  techniques. For instance, in medicine, effective analysis of diverse physiological
+  signals is crucial for patient monitoring and clinical decision-making and yet highly
+  challenging. We introduce MedTsLLM, a general multimodal large language model (LLM)
+  framework that effectively integrates time series data and rich contextual information
+  in the form of text to analyze physiological signals, performing three tasks with
+  clinical relevance: semantic segmentation, boundary detection, and anomaly detection
+  in time series. These critical tasks enable deeper analysis of physiological signals
+  and can provide actionable insights for clinicians. We utilize a reprogramming layer
+  to align embeddings of time series patches with a pretrained LLM’s embedding space
+  and make effective use of raw time series, in conjunction with textual context.
+  Given the multivariate nature of medical datasets, we develop methods to handle
+  multiple covariates. We additionally tailor the text prompt to include patient-specific
+  information. Our model outperforms state-of-the-art baselines, including deep learning
+  models, other LLMs, and clinical methods across multiple medical domains, specifically
+  electrocardiograms and respiratory waveforms. MedTsLLM presents a promising step
+  towards harnessing the power of LLMs for medical time series analysis that can elevate
+  data-driven tools for clinicians and improve patient outcomes.'
+openreview: W4ZxKk14HM
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: chan24a
+month: 0
+tex_title: Leveraging {LLM}s for Multimodal Medical Time Series Analysis
+cycles: false
+bibtex_author: Chan, Nimeesha and Parker, Felix and Bennett, William C and Wu, Tianyi
+  and Jia, Mung Yao and MD, James Fackler and Ghobadi, Kimia
+author:
+- given: Nimeesha
+  family: Chan
+- given: Felix
+  family: Parker
+- given: William C
+  family: Bennett
+- given: Tianyi
+  family: Wu
+- given: Mung Yao
+  family: Jia
+- given: James Fackler
+  family: MD
+- given: Kimia
+  family: Ghobadi
+date: 2024-11-25
+address:
+container-title: Proceedings of the 9th Machine Learning for Healthcare Conference
+volume: '252'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 11
+  - 25
+pdf: https://raw.githubusercontent.com/mlresearch/v252/main/assets/chan24a/chan24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-11-25-chien24a.md

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+---
+title: 'Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous,
+  Time-Varying Treatment Effects'
+abstract: Randomized controlled trials (RCTs), though essential for evaluating the
+  efficacy of novel treatments, are costly and time-intensive. Due to strict eligibility
+  criteria, RCTs may not adequately represent diverse patient populations, leading
+  to equity issues and limited generalizability. Additionally, conventional trial
+  analysis methods are limited by strict assumptions and biases. Real-world evidence
+  (RWE) offers a promising avenue to explore treatment effects beyond trial settings,
+  addressing gaps in representation and providing additional insights into patient
+  outcomes over time. We introduce TRIALSCOPE-X and TRIALSCOPE-XL, machine learning
+  pipelines designed to analyze treatment outcomes using RWE by mitigating biases
+  that arise from observational data and addressing the limitations of conventional
+  methods. We estimate causal, time-varying treatment effects across heterogeneous
+  patient populations and varied timeframes. Preliminary results investigating the
+  treatment benefit of Keytruda, a widely-used cancer immunotherapy drug, demonstrate
+  the utility of our methods in evaluating treatment outcomes under novel settings
+  and uncovering potential disparities. Our findings highlight the potential of RWE-based
+  analysis to provide data-driven insights that inform evidence-based medicine and
+  shape more inclusive and comprehensive clinical research, supplementing traditional
+  clinical trial findings.
+openreview: wUruL3DqKB
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: chien24a
+month: 0
+tex_title: 'Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous,
+  Time-Varying Treatment Effects'
+cycles: false
+bibtex_author: Chien, Isabel and Wong, Cliff and Gero, Zelalem and Bagga, Jaspreet
+  and Ueno, Risa and Turner, Richard E. and Weerasinghe, Roshanthi K. and Piening,
+  Brian and Naumann, Tristan and Bifulco, Carlo and Poon, Hoifung and Hern\'andez,
+  Javier Gonz\'alez
+author:
+- given: Isabel
+  family: Chien
+- given: Cliff
+  family: Wong
+- given: Zelalem
+  family: Gero
+- given: Jaspreet
+  family: Bagga
+- given: Risa
+  family: Ueno
+- given: Richard E.
+  family: Turner
+- given: Roshanthi K.
+  family: Weerasinghe
+- given: Brian
+  family: Piening
+- given: Tristan
+  family: Naumann
+- given: Carlo
+  family: Bifulco
+- given: Hoifung
+  family: Poon
+- given: Javier González
+  family: Hernández
+date: 2024-11-25
+address:
+container-title: Proceedings of the 9th Machine Learning for Healthcare Conference
+volume: '252'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 11
+  - 25
+pdf: https://raw.githubusercontent.com/mlresearch/v252/main/assets/chien24a/chien24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---