学際的なチームによるランダム化比較試験のためのマルチタスク機械学習モデルの構築 Interdisciplinary team builds multitask machine learning model for randomized controlled trial
2022-09-13 ワシントン大学セントルイス
無作為化対照試験における介入群と対照群を組み合わせて、統一されたモデルを共同で訓練し、治療を受けた人と受けていない人の個人的な転帰を予測する、マルチタスクの枠組みを開拓した。このアプローチにより、研究コホートを機械学習モデル用の小さなグループに分割することを避け、グループ間で動的な知識の伝達を行い、介入あり・なしの両方について予測性能を最適化することができる。
<関連情報>
- https://source.wustl.edu/2022/09/personalized-prediction-of-depression-treatment-outcomes-with-wearables/
- https://dl.acm.org/doi/10.1145/3534591
ランダム化比較試験のためのマルチタスク学習:ウェアラブルデータを用いたうつ病の予測に関するケーススタディ。 Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data
Ruixuan Dai,Thomas Kannampallil,Jingwen Zhang,Nan Lv,Jun Ma,Chenyang Lu
Proceedings of the ACM on Interactive, Model, Wearable and Ubiquitous Technologies Published:07 July 2022
DOI:https://doi.org/10.1145/3534591
Abstract
A randomized controlled trial (RCT) is used to study the safety and efficacy of new treatments, by comparing patient outcomes of an intervention group with a control group. Traditionally, RCTs rely on statistical analyses to assess the differences between the treatment and control groups. However, such statistical analyses are generally not designed to assess the impact of the intervention at an individual level. In this paper, we explore machine learning models in conjunction with an RCT for personalized predictions of a depression treatment intervention, where patients were longitudinally monitored with wearable devices. We formulate individual-level predictions in the intervention and control groups from an RCT as a multi-task learning (MTL) problem, and propose a novel MTL model specifically designed for RCTs. Instead of training separate models for the intervention and control groups, the proposed MTL model is trained on both groups, effectively enlarging the training dataset. We develop a hierarchical model architecture to aggregate data from different sources and different longitudinal stages of the trial, which allows the MTL model to exploit the commonalities and capture the differences between the two groups. We evaluated the MTL approach in an RCT involving 106 patients with depression, who were randomized to receive an integrated intervention treatment. Our proposed MTL model outperforms both single-task models and the traditional multi-task model in predictive performance, representing a promising step in utilizing data collected in RCTs to develop predictive models for precision medicine.
