LAUSR

h 0 One of the frequently asked questions by informatics students is What is the difference between Machine Learning and Prediction odels?” It is common to misuse and misunderstood the word Machine Learning”. Predictive Analytics is a use and Machine earning is a technique. Although, there are other traditional statisical methods available for predictive analytics, Machine Learning echniques have their own nuances and advantages. This month’s ditor’s choice articles are about prediction models and machine earning techniques used in clinical settings. One of the Editor’s choice article of this month is, “Prediction of abor onset type: Spontaneous vs induced; role of electrohysterogaphy?” demonstrates how to use traditional obstetrical data along ith the electrophysiological parameters derived from the electroysterogram (EHG) to predict wheather it will be a spontaneous or nduced delivery. J. Alberola-Rubio et al. from Spain [1] developed nd evaluated predictive models using both traditional obstetrial data and also electrophysiological parameters derived from the lectrohysterogram (EHG). This study concludes that measuring the lectrophysiological uterine condition by means of electrohysteroraphic recordings yielded a promising clinical decision support ystem for distinguishing patients that will spontaneously achieve ctive labor before the end of full term from those who will reuire late term induction of labor. The second article from Editor’s choice, “Prognostic value of tuor volume for patients with advanced lung cancer treated with hemotherapy”, Kuo et al. from Taiwan [2] develop a reference sysem utilizing computed tomography to calculate changes in tumor olume of lung cancer patients after chemotherapy to assist physiians in clinical treatment and evaluation. From the image processng techniques, tumor volume from each patient were obtained ithin an average of 7.25 seconds. The proposed method by Kuo t al. was shown to achieve rapid positioning of lung tumors and olume reconstruction with an estimation error of 1.92% when calbrated with an irregularly shaped stone. The technique proposed n this study can automatically find the location of tumors in the ung, reconstruct the volume, and calculate changes in volume beore and after treatment, thus obtaining an innovative survival preiction index. This proposed technique will facilitate early and acurate predictions of disease outcomes during the course of therpy, and categorize patient stratification into risk groups for more fficient therapies. The third article from Editor’s choice “High-accuracy detection f airway obstruction in asthma using machine learning algorithms nd forced oscillation measurements” is by J.L.M. Amaral et al from