The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion

Jiang, Junhuang; Lu, Anqi; Ma, Xiangyu; Ouyang, Defang; Williams, Robert O.

doi:10.1016/j.ijpx.2023.100164

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…The use of machine learning in medicine for developing highly precise predictive models is on the rise [22][23][24]. To achieve this, a common approach involves utilizing the XGBoost algorithm, which is known for its high accuracy, along with the transparent Shapely Additive Explanations (SHAP) algorithm to determine crucial covariates and their predictive direction [25,26]. In our research, we expanded upon this approach by integrating dendrograms and heatmaps to visually summarize covariates based on their gain, cover, and frequency.…”

Section: Introductionmentioning

confidence: 99%

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

Huang

2023

PLoS ONE

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Background There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality. Objective The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency. Methods Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map. Results Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics. Conclusion Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

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Section: Introductionmentioning

confidence: 99%

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

Huang

2023

PLoS ONE

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“…In early formulation development, 9- or 12-mm co-rotating twin-screw extruders (TSE) are frequently used for the production of amorphous solid dispersions (ASD) mainly resulting in a solubility enhancement of poorly water-soluble drugs, but still require batch sizes of about 20–30 g, which result in substantial amounts of drug substance and development costs, respectively ( Jiang et al, 2023 ; Zecevic and Wagner, 2013 ). The amount of protein-based drug candidates within early formulation development studies however, is often limited and thus small-scale HME is particularly relevant ( Dauer et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%