Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation

Arai, Yasuyuki; Kondo, Tadakazu; Fuse, Kyoko; Shibasaki, Yasuhiko; Masuko, Masayoshi; Sato, Junichi; Teshima, Takanori; Uchida, Naoyuki; Fukuda, Takahiro; Kakihana, Kazuhiko; Ozawa, Yukiyasu; Eto, Tetsuya; Tanaka, Masatsugu; Ikegame, Kazuhiro; Mori, Takehiko; Iwato, Koji; Ichinohe, Tatsuo; Kanda, Yoshinobu; Atsuta, Yoshiko

doi:10.1182/bloodadvances.2019000934

Cited by 45 publications

(54 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Importantly, due to dynamic characteristics unique to each study, it remains challenging to identify an optimal ML technique that can be applied robustly across all conditions. However, our results suggest that ADT technique could be useful in the field of HSCT due to their interpretability which is crucial in the clinical settings as shown in primary studies [17,24,25,27]. The findings were generalizable, robust, and clinically relevant.…”

Section: Discussionmentioning

confidence: 60%

“…However, the requirement of discretizing input variables could be a major drawback. ADT technique was used in four of the reviewed studies where it was used primarily to predict survival/death and relapse post-HSCT [17,24,25,27]. ADT outperformed RF model in one study [16,20,24,26,35,36,40].…”

Section: Discussionmentioning

confidence: 99%

“…Predicting the risk of post-HSCT complications (e.g., GVHD) using ML techniques was investigated in some studies [26,27]. Tang et al [26] examined 324 allogenic HSCT recipient data from the EHR using penalized logistic regression to predict risk of grade II-IV acute GVHD.…”

Section: Post-hsct Complicationsmentioning

confidence: 99%

“…The investigators found that temperature, systolic blood pressure and features representing longitudinal trends were the most significant features for predicting risk of acute GVHD. Another study [27] developed a predictive model for predicting acute GVHD (grades II-IV and III-IV), using ADT. Pre-transplant donor and recipient clinical characteristics were used for the prediction model.…”

Section: Post-hsct Complicationsmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Gupta

Braun

Chowdhury

et al. 2020

Sensors

View full text Add to dashboard Cite

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

show abstract

Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Post-hsct Complicationsmentioning

confidence: 99%

Section: Post-hsct Complicationsmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Gupta

Braun

Chowdhury

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Using 15/40 variables, they predicted aGVHD grade II-IV with an AUC of 0.616. The authors went on to validate these 15 variables with conventional statistics and showed a cumulative incidence of aGVHD II-IV of 58.9% with the high-risk score and 29% in the low risk score (48). This type of method can also be used at a smaller scale to identify new features in complex phenotypes such as cGVHD.…”

Section: Analytical Toolsmentioning

confidence: 99%

Biomarkers for Allogeneic HCT Outcomes

et al. 2020

View full text Add to dashboard Cite

Allogeneic hematopoietic cell transplantation (HCT) remains the only curative therapy for many hematological malignant and non-malignant disorders. However, key obstacles to the success of HCT include graft-versus-host disease (GVHD) and disease relapse due to absence of graft-versus-tumor (GVT) effect. Over the last decade, advances in "omics" technologies and systems biology analysis, have allowed for the discovery and validation of blood biomarkers that can be used as diagnostic test and prognostic test (that risk-stratify patients before disease occurrence) for acute and chronic GVHD and recently GVT. There are also predictive biomarkers that categorize patients based on their likely to respond to therapy. Newer mathematical analysis such as machine learning is able to identify different predictors of GVHD using clinical characteristics pre-transplant and possibly in the future combined with other biomarkers. Biomarkers are not only useful to identify patients with higher risk of disease progression, but also help guide treatment decisions and/or provide a basis for specific therapeutic interventions. This review summarizes biomarkers definition, omics technologies, acute, chronic GVHD and GVT biomarkers currently used in clinic or with potential as targets for existing or new drugs focusing on novel published work.

show abstract

Using AI to Steer Brain Regeneration: The Enhanced Regenerative Medicine Paradigm

Panuccio,

Subramaniyam,

Canal‐Alonso

et al. 2024

Big Data Analysis and Artificial Intelligence for Medical Sciences

View full text Add to dashboard Cite

Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation

Abstract: Key Points The machine learning algorithms produced clinically reasonable and robust risk stratification scores for aGVHD. Predicting scores for aGVHD also demonstrated the link between risk of development of aGVHD and overall survival after HSCT.

Cited by 45 publications

References 29 publications

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Biomarkers for Allogeneic HCT Outcomes

Using AI to Steer Brain Regeneration: The Enhanced Regenerative Medicine Paradigm

Contact Info

Product

Resources

About