Balancing machine learning with human application: the importance of interpretability and context

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][1] These investigators identified two distinct postoperative pain archetypes and developed a

Authors

Seshadri C. Mudumbai (ORCID: https://orcid.org/0000-0001-8486-9966)
Edward R. Mariano (ORCID: https://orcid.org/0000-0003-2735-248X)
Jinlei Li (ORCID: https://orcid.org/0000-0003-4026-2569)
Edidiong Akpabio

Institutions

Meharry Medical College (US)
VA Palo Alto Health Care System (US)
Yale University (US)
Stanford University (US)

Publication Details

Journal: Regional Anesthesia & Pain Medicine
Published: 2026-06-30
DOI: https://doi.org/10.1136/rapm-2026-108163
Primary Topic: Total Knee Arthroplasty Outcomes
Type: article
Field-Weighted Citation Impact: 0.00

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article

Balancing machine learning with human application: the importance of interpretability and context

Seshadri C. Mudumbai, Edward R. Mariano, Jinlei Li, Edidiong Akpabio

Jun 30, 2026

Regional Anesthesia & Pain Medicine

Total Knee Arthroplasty Outcomes

article

Balancing machine learning with human application: the importance of interpretability and context

Seshadri C. Mudumbai, Edward R. Mariano, Jinlei Li, Edidiong Akpabio

June 30, 2026article en

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][1] These investigators identified two distinct postoperative pain archetypes and developed a

Regional Anesthesia & Pain Medicine

Meharry Medical College (US), VA Palo Alto Health Care System (US), Yale University (US), Stanford University (US)

Quality Education

Openalex Percentile: Top 7%

Total Knee Arthroplasty Outcomes

DOI Source OpenAlex

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