Combining Survival Analysis and Machine Learning for Mass Cancer Risk Prediction using EHR data

Purely medical cancer screening methods are often costly, time-consuming, and weakly applicable on a large scale. Advanced Artificial Intelligence (AI) methods greatly help cancer detection but require specific or deep medical data. These aspects affect the mass implementation of cancer screening methods. For these reasons, it is a disruptive change for healthcare to apply AI methods for mass personalized assessment of the cancer risk among patients based on the existing Electronic Health Records (EHR) volume. This paper presents a novel method for mass cancer risk prediction using EHR data. Among other methods, our one stands out by the minimum data greedy policy, requiring only a history of medical service codes and diagnoses from EHR. We formulate the problem as a binary classification. This dataset contains 175 441 de-identified patients (2 861 diagnosed with cancer). As a baseline, we implement a solution based on a recurrent neural network (RNN). We propose a method that combines machine learning and survival analysis since these approaches are less computationally heavy, can be combined into an ensemble (the Survival Ensemble), and can be reproduced in most medical institutions. We test the Survival Ensemble in some studies. Firstly, we obtain a significant difference between values of the primary metric (Average Precision) with 22.8% (ROC AUC 83.7%, F1 17.8%) for the Survival Ensemble versus 15.1% (ROC AUC 84.9%, F1 21.4%) for the Baseline. Secondly, the performance of the Survival Ensemble is also confirmed during the ablation study. Thirdly, our method exceeds age baselines by a significant margin. Fourthly, in the blind retrospective out-of-time experiment, the proposed method is reliable in cancer patient detection (9 out of 100 selected). Such results exceed the estimates of medical screenings, e.g., the best Number Needed to Screen (9 out of 1000 screenings)

Core-excited states of SF6_{6} probed with soft X-ray femtosecond transient absorption of vibrational wavepackets

A vibrational wavepacket in SF$_6$, created by impulsive stimulated Raman scattering with a few-cycle infrared pulse, is mapped onto five sulfur core-excited states using table-top soft X-ray transient absorption spectroscopy between 170-200 eV. The amplitudes of the X-ray energy shifts of the femtosecond oscillations depend strongly on the nature of the state. The prepared wavepacket is controlled with the pump laser intensity to probe the core-excited levels for various extensions of the S-F stretching motion. This allows the determination of the relative core-level potential energy gradients, in good agreement with TDDFT calculations. This experiment demonstrates a new means of characterizing core-excited potential energy surfaces

Effectiveness of the method for selecting rotary-steerable systems based on the machine learning algorithm Random Forest Classifier

Relevance. Urgent need to consider and determine possible ways to use machine learning methods in drilling industry, since artificial intelligence is developing rapidly. Achieving this task will provide industrial enterprises with a huge competitive advantage and make an important contribution to the scientific community for its future research. This is emphasized by such regulations as the Decree of the President of the Russian Federation dated 10.10.2019 G. No. 490 "On the development of Artificial Intelligence in the Russian Federation" and "The National Strategy for the Development of Artificial Intelligence for the period up to 2030". Aim. To study the effectiveness of using the machine learning method Random Forest Classifier, to develop methods for selecting rotary-steerable systems, to consider the efficiency of machine learning to determine target parameters when solving the task assigned to it within the drilling industry and to determine the approximate amount of time that can be spent by the algorithm to work out a possible solution. Object. Random Forest Classifier machine learning method in the conditions of solving a problem from the drilling industry on the selection of an optimal rotary-steerable system for specifically specified conditions. Methods. The authors have performed two computational experiments using two computing and electronic machines, namely a laptop and a remote server, the prerequisite for which was the data collected and analyzed on the basis of the study of the scientific literature in the field of research. This article explores the possibility of using the machine learning method Random Forest Classifier, to optimize well construction, using the example of developing a method for selecting rotary-steerable systems. Computational experiments were performed on two computers using the Python programming language, version 3.8.10, as well as the following libraries: NumPy, Pandas, Scikit-learn. Results. The computational experiments carried out proved the ability of the considered method to solve the problems of choosing suitable drilling equipment, an example of which was rotary-steerable systems. This method is able to independently determine the dependencies necessary to perform the task and spends a small amount of time on this process. The totality of these conclusions makes it possible to unequivocally assert the expediency and necessity of developing new approaches to the use of machine learning methods in the drilling industry, as well as performing multiple scientific studies on the possibilities of using machine learning in well construction and analyzing their effectiveness, since this direction is advanced and can radically change existing ideas about the processes occurring during well drilling

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

The Physics of the B Factories

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

An embedding technique to determine ττ backgrounds in proton-proton collision data

An embedding technique is presented to estimate standard model tau tau backgrounds from data with minimal simulation input. In the data, the muons are removed from reconstructed mu mu events and replaced with simulated tau leptons with the same kinematic properties. In this way, a set of hybrid events is obtained that does not rely on simulation except for the decay of the tau leptons. The challenges in describing the underlying event or the production of associated jets in the simulation are avoided. The technique described in this paper was developed for CMS. Its validation and the inherent uncertainties are also discussed. The demonstration of the performance of the technique is based on a sample of proton-proton collisions collected by CMS in 2017 at root s = 13 TeV corresponding to an integrated luminosity of 41.5 fb(-1).Peer reviewe

Measurement of the Splitting Function in &ITpp &ITand Pb-Pb Collisions at root&ITsNN&IT=5.02 TeV

Data from heavy ion collisions suggest that the evolution of a parton shower is modified by interactions with the color charges in the dense partonic medium created in these collisions, but it is not known where in the shower evolution the modifications occur. The momentum ratio of the two leading partons, resolved as subjets, provides information about the parton shower evolution. This substructure observable, known as the splitting function, reflects the process of a parton splitting into two other partons and has been measured for jets with transverse momentum between 140 and 500 GeV, in pp and PbPb collisions at a center-of-mass energy of 5.02 TeV per nucleon pair. In central PbPb collisions, the splitting function indicates a more unbalanced momentum ratio, compared to peripheral PbPb and pp collisions.. The measurements are compared to various predictions from event generators and analytical calculations.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe

Electroweak production of two jets in association with a Z boson in proton-proton collisions root s =13 TeV

A measurement of the electroweak (EW) production of two jets in association with a Z boson in proton-proton collisions at root s = 13 TeV is presented, based on data recorded in 2016 by the CMS experiment at the LHC corresponding to an integrated luminosity of 35.9 fb(-1). The measurement is performed in the lljj final state with l including electrons and muons, and the jets j corresponding to the quarks produced in the hard interaction. The measured cross section in a kinematic region defined by invariant masses m(ll) > 50 GeV, m(jj) > 120 GeV, and transverse momenta P-Tj > 25 GeV is sigma(EW) (lljj) = 534 +/- 20 (stat) fb (syst) fb, in agreement with leading-order standard model predictions. The final state is also used to perform a search for anomalous trilinear gauge couplings. No evidence is found and limits on anomalous trilinear gauge couplings associated with dimension-six operators are given in the framework of an effective field theory. The corresponding 95% confidence level intervals are -2.6 <cwww/Lambda(2) <2.6 TeV-2 and -8.4 <cw/Lambda(2) <10.1 TeV-2. The additional jet activity of events in a signal-enriched region is also studied, and the measurements are in agreement with predictions.Peer reviewe