Prediction of blood pressure changes associated with abdominal pressure changes during robotic laparoscopic low abdominal surgery using deep learning.

BACKGROUND: Intraoperative hypertension and blood pressure (BP) fluctuation are known to be associated with negative patient outcomes. During robotic lower abdominal surgery, the patient's abdominal cavity is filled with CO2, and the patient's head is steeply positioned toward the floor (Trendelenburg position). Pneumoperitoneum and the Trendelenburg position together with physiological alterations during anesthesia, interfere with predicting BP changes. Recently, deep learning using recurrent neural networks (RNN) was shown to be effective in predicting intraoperative BP. A model for predicting BP rise was designed using RNN under special scenarios during robotic laparoscopic surgery and its accuracy was tested. METHODS: Databases that included adult patients (over 19 years old) undergoing low abdominal da Vinci robotic surgery (ovarian cystectomy, hysterectomy, myomectomy, prostatectomy, and salpingo-oophorectomy) at Soonchunhyang University Bucheon Hospital from October 2018 to March 2021 were used. An RNN-based model was designed using Python3 language with the PyTorch packages. The model was trained to predict whether hypertension (20% increase in the mean BP from baseline) would develop within 10 minutes after pneumoperitoneum. RESULTS: Eight distinct datasets were generated and the predictive power was compared. The macro-average F1 scores of the datasets ranged from 68.18% to 72.33%. It took only 3.472 milliseconds to obtain 39 prediction outputs. CONCLUSIONS: A prediction model using the RNN may predict BP rises during robotic laparoscopic surgery.

Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.

Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the 'Autocorrelation' module contributed more while the 'Atom type electrotopological state' contributed the least to the model.