A machine learning‐based risk scoring system for infertility considering different age groups

2020 ◽  
Author(s):  
ShuJie Liao ◽  
Lei Jin ◽  
Wan‐Qiang Dai ◽  
Ge Huang ◽  
Wulin Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoring System ◽  
Age Groups ◽  
Risk Scoring ◽  
Risk Scoring System

A risk scoring system to predict overall survival for patients with liver cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16635-e16635
Author(s):  
Haibei Xin ◽  
Guanxiong Zhang ◽  
Wei Zhou ◽  
Huan Chen ◽  
Dandan Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Overall Survival ◽  
Surgical Resection ◽  
Scoring System ◽  
Therapeutic Interventions ◽  
Primary Hepatocellular Carcinoma ◽  
Cd8 Cells ◽  
Risk Scoring ◽  
Risk Scoring System

e16635 Background: Surgical resection is a common curative treatment for patients with primary hepatocellular carcinoma (HCC), and conventional strategies of assessing clinical and pathologic risk factors have been adopted to predict clinic outcomes in patients after curative surgery. We hypothesized that an ensemble learning approach which incorporates multidimensional features would enable effective prediction of patient survival. Methods: We analyzed data from 222 stage II-III HCC patients who underwent surgical resection at Eastern Hepatobiliary Surgery Hospital (Shanghai, China). Baseline information for each patient includes clinical and pathologic risk factors, laboratory tests and in situ immunological profiles. Using machine learning, we developed CoxPH, GBS, CGBS, FSVM and NSVM models for patient overall survival (OS). Models were trained on 155 cases with 48 features. Thirteen-fold cross-validation (CV) was used to measure performance with area under the ROC curve (AUC) and C-Index (CI). The ensemble model was used to predict patient OS and validated on the subsequent 67 cases. Results: For all models tested, immune features, including the fraction of CD68+ and CD8+ cells in tumor, and CD8+ cells in stroma, play a crucial role in predicting patient OS. Using the ensemble CoxPH model (with superior value of AUC and CI), a risk scoring system for patient OS was developed. The scoring system could stratify patients into high-risk or low-risk groups, revealing different prognosis (validation cohort: HR, 6.5, 95% CI, 2.4-18, P = 3.4e-05). The CoxPH model was also predictive of the time to-recurrence (p < 0.0001). In validation set, the scoring system predicted half-year mortality of patients with AUC of 0.9, and 1-year mortality of patients with AUC of 0.897. The scoring system could also predict half-year recurrence and 1-year recurrence with AUC more than 0.83. Conclusions: The machine learning-based risk scoring system offers a novel strategy for incorporating multidimensional risk factors to predict clinic outcome and may help medical practitioners to optimize clinical follow-up or therapeutic interventions. [Table: see text]

A Risk Scoring System for Predicting Overall Survival in Patients with Liver Cancer

2020 ◽  
Author(s):  
Haibei Xin ◽  
Guanxiong Zhang ◽  
Wei Zhou ◽  
Shanshan Li ◽  
Minfeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Liver Cancer ◽  
Scoring System ◽  
Risk Scoring ◽  
Risk Scoring System

A Novel Risk Scoring System for Predicting Primary Graft Dysfunction after Orthotopic Heart Transplantation

2020 ◽  
Vol 26 (10) ◽  
pp. S136-S137
Author(s):  
Syed Adeel Ahsan ◽  
Jasjit Bhinder ◽  
Syed Zaid ◽  
Parija Sharedalal ◽  
Chhaya Aggarwal-Gupta ◽  
...  
Keyword(s):  
Heart Transplantation ◽  
Scoring System ◽  
Orthotopic Heart Transplantation ◽  
Primary Graft Dysfunction ◽  
Graft Dysfunction ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Novel risk scoring system for metastatic renal cell carcinoma patients treated with cabozantinib

2021 ◽  
pp. 100393
Author(s):  
Dylan J. Martini ◽  
Meredith R. Kline ◽  
Yuan Liu ◽  
Julie M. Shabto ◽  
Bradley C. Carthon ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Scoring System ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 853
Author(s):  
Jee-Yun Kim ◽  
Jeong Yee ◽  
Tae-Im Park ◽  
So-Youn Shin ◽  
Man-Ho Ha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Linear Regression ◽  
Parenteral Nutrition ◽  
Prediction Model ◽  
Scoring System ◽  
Prediction Equation ◽  
Icu Patients ◽  
Risk Scoring ◽  
Risk Scoring System

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

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