Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology

المؤلفون المشاركون

Ferroni, Patrizia
Guadagni, Fiorella
Roselli, Mario
Zanzotto, Fabio M.
Scarpato, Noemi
Riondino, Silvia

المصدر

Disease Markers

العدد

المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-7، 7ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2017-09-17

دولة النشر

مصر

عدد الصفحات

7

التخصصات الرئيسية

الأمراض

الملخص EN

Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treated cancer patients.

This study was designed to validate a model incorporating the two best predictors and to compare their combined performance with that of the currently recommended Khorana score (KS).

Age, sex, tumor site/stage, hematological attributes, blood lipids, glycemic indexes, liver and kidney function, BMI, performance status, and supportive and anticancer drugs of 608 cancer outpatients were all entered in the model, with numerical attributes analyzed as continuous values.

VTE rate was 7.1%.

The VTE risk prediction performance of the combined model resulted in 2.30 positive likelihood ratio (+LR), 0.46 negative LR (−LR), and 4.88 HR (95% CI: 2.54–9.37), with a significant improvement over the KS [HR 1.73 (95% CI: 0.47–6.37)].

These results confirm that a ML approach might be of clinical value for VTE risk stratification in chemotherapy-treated cancer outpatients and suggest that the ML-RO model proposed could be useful to design a web service able to provide physicians with a graphical interface helping in the critical phase of decision making.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Ferroni, Patrizia& Zanzotto, Fabio M.& Scarpato, Noemi& Riondino, Silvia& Guadagni, Fiorella& Roselli, Mario. 2017. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Disease Markers،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1152565

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Ferroni, Patrizia…[et al.]. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Disease Markers No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1152565

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Ferroni, Patrizia& Zanzotto, Fabio M.& Scarpato, Noemi& Riondino, Silvia& Guadagni, Fiorella& Roselli, Mario. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Disease Markers. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1152565

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1152565