{
  "abstract": "We read with interest the study by Chew et al, who applied an innovative machine learning (ML) pipeline to a dataset of 17 200 patients who underwent total knee arthroplasty (TKA) at a single center.1 These investigators identified two distinct postoperative pain archetypes and developed a predictive model able to classify patients in either high or low pain clusters with 64% accuracy.1 We commend the authors for sharing their detailed methodology and clinically meaningful findings and wish to offer a few additional perspectives.",
  "authors": [
    {
      "affiliations": [
        "Meharry Medical College, Nashville, Tennessee, USA"
      ],
      "name": "Edidiong Akpabio"
    },
    {
      "affiliations": [
        "Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA",
        "Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA"
      ],
      "name": "Seshadri C Mudumbai"
    },
    {
      "affiliations": [
        "Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut, USA"
      ],
      "name": "Jinlei Li"
    },
    {
      "affiliations": [
        "Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA",
        "Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA"
      ],
      "name": "Edward R Mariano"
    }
  ],
  "title": "Balancing machine learning with human application: the importance of interpretability and context",
  "uid": "4229f667-bb24-54b2-8bb1-968aa28baa30"
}