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  <contributors>
    <authors>
      <author>Luongo, Giorgio</author>
      <author>Azzolin, Luca</author>
      <author>Schuler, Steffen</author>
      <author>Rivolta, Massimo W.</author>
      <author>Almeida, Tiago P.</author>
      <author>Martínez, Juan P.</author>
      <author>Soriano, Diogo C.</author>
      <author>Luik, Armin</author>
      <author>Müller-Edenborn, Björn</author>
      <author>Jadidi, Amir</author>
      <author>Dössel, Olaf</author>
      <author>Sassi, Roberto</author>
      <author>Laguna, Pablo</author>
      <author>Loewe, Axel</author>
    </authors>
  </contributors>
  <titles>
    <title>Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG</title>
    <secondary-title>Cardiovasc. digit. health j.</secondary-title>
  </titles>
  <doi>10.1016/j.cvdhj.2021.03.002</doi>
  <pages/>
  <volume/>
  <number/>
  <dates>
    <year>2021</year>
    <pub-dates>
      <date>2021</date>
    </pub-dates>
  </dates>
  <abstract/>
</record>

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