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322</figure>
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324<p>The Clinical AI Value Alignment lab studies artificial intelligence (AI) used in healthcare settings. 
325We focus on algorithmic notions of interpretability, fairness, accuracy, and robustness in medical applications of AI.</p>
326
327<p>The lab is part of the <a href="http://www.chip.org">Computational Health Informatics Program</a> at Boston Children’s Hospital, affiliated with Harvard Medical School.</p>
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329
330
331<h2 class="archive__subtitle">Recent Papers</h2>
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333<div class="entries-">
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340 
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345<div class="pubitem">
346    <div class="pubtitle" id="aldeiaguilhermeDeepTimetoEventModels">
347    Deep Time-to-Event Models for Intrapartum Fetal Monitoring
348    </div>
349    <div class="pubauthors">
350        
351        
352
353        
354            
355            
356            Guilherme S. Imai  Aldeia,
357        
358            
359            
360            Helena  Coggan,
361        
362            
363            
364            Yuting  Yang,
365        
366            
367            
368            Lisa  Levine,
369        
370            
371            
372            Jennifer A.  McCoy,
373        
374            
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376            William G.  La Cava
377        
378        (2026)
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386        Preprint
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402        <a href="/assets/papers/Aldeia et al. - 2026 - Deep Time-to-Event Models for Intrapartum Fetal Mo.pdf">pdf</a>
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406</div>
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411<div class="pubitem">
412    <div class="pubtitle" id="huPretrainFinetuneFramework2026">
413    A pre-train and fine-tune framework for adaptive boosting of pre-trained polygenic risk scores
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420            
421            
422            Jie  Hu,
423        
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426            Raelynn  Chen,
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429            
430            Maxwell  Salvatore,
431        
432            
433            
434            Olivia  Wu,
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437            
438            Okan Bilge  Ozdemir,
439        
440            
441            
442            Yiwen  Lu,
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450            Elizabeth W.  Karlson,
451        
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454            Atlas  Khan,
455        
456            
457            
458            Krzysztof  Kiryluk,
459        
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462            Iftikhar J.  Kullo,
463        
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466            Johanna L.  Smith,
467        
468            
469            
470            Eimear E.  Kenny,
471        
472            
473            
474            Yuan  Luo,
475        
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477            
478            Zaldy S.  Tan,
479        
480            
481            
482            William G.  La Cava,
483        
484            
485            
486            Marylyn D.  Ritchie,
487        
488            
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490            Yong  Chen,
491        
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494            Ruowang  Li
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496        (2026)
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503    <div class=pubjournal>
504        Nature Communications
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513            <a href="https://www.nature.com/articles/s41467-026-77128-5"><i class="fas fa-external-link-alt"></i> nature.com </a>
514        
515
516        
517
518        
519        
520        
521        
522        &nbsp;|&nbsp; 
523        
524        <a href="/assets/papers/Hu et al. - 2026 - A pre-train and fine-tune framework for adaptive b.pdf">pdf</a>
525        
526        
527    </div>
528</div>
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530      
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532
533<div class="pubitem">
534    <div class="pubtitle" id="aldeiaFoundationmodelApproachPediatric2026">
535    A foundation-model approach to pediatric headache classification from rs-fMRI
536    </div>
537    <div class="pubauthors">
538        
539        
540
541        
542            
543            
544            Guilherme S. Imai  Aldeia,
545        
546            
547            
548            Clara  Moon,
549        
550            
551            
552            Julie  Shulman,
553        
554            
555            
556            Navil  Sethna,
557        
558            
559            
560            Allison  Smith,
561        
562            
563            
564            Alyssa  Lebel,
565        
566            
567            
568            William G.  La Cava,
569        
570            
571            
572            Scott  Holmes
573        
574        (2026)
575    </div>
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578    
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581    <div class=pubjournal>
582        Machine Learning for Healthcare Conference
583    </div>
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586        
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590            
591            <a href="http://arxiv.org/abs/2608.07287"><i class="fas fa-external-link-alt"></i> arxiv.org </a>
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596        
597        
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600        &nbsp;|&nbsp; 
601        
602        <a href="/assets/papers/Aldeia et al. - 2026 - A foundation-model approach to pediatric headache.pdf">pdf</a>
603        
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607                
608                
609                
610                
611                        &nbsp;|&nbsp;In press
612                    
613            
614        
615    </div>
616</div>
617  
618</div>
619
620<h2 class="archive__subtitle">Recent Posts</h2>
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634    <div class="archive__item-title no_toc" itemprop="headline">
635      <b>
636      
637        <a href="/2025/10/24/ed-admissions.html" rel="permalink">What Drives Disparities in Emergency Admissions?
638</a>
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640      </b>
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657          7 minute read
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660    
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664    <p class="archive__item-excerpt" itemprop="description">Understanding unwarranted variations in emergency care
665</p>
666  </article>
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674  <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork">
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676    <div class="archive__item-title no_toc" itemprop="headline">
677      <b>
678      
679        <a href="/2025/02/14/ecg-survival.html" rel="permalink">Benchmarking mortality risk prediction from electrocardiograms
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699          4 minute read
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706    <p class="archive__item-excerpt" itemprop="description">Taking a closer look at survival modeling with ECGs
707</p>
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715<div class="list__item">
716  <article class="archive__item" itemscope itemtype="https://schema.org/CreativeWork">
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718    <div class="archive__item-title no_toc" itemprop="headline">
719      <b>
720      
721        <a href="/2024/04/14/inexact-simplification.html" rel="permalink">Simplifying symbolic regression models without simplification rules
722</a>
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740        
741          6 minute read
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744    
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748    <p class="archive__item-excerpt" itemprop="description">Relaxing the definition of equivalent mathematical expressions to get simpler and more interpretable models
749</p>
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865  </body>
866</html>

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.