Model-free Representation Learning and Exploration in Low-rank MDPs

The low rank MDP has emerged as an important model for studying representation learning and exploration in reinforcement learning. With a known representation, several model-free exploration strategies exist. In contrast, all algorithms for the unknown representation setting are model-based, thereby requiring the ability to model the full dynamics. In this work, we present the first model-free representation learning algorithms for low rank MDPs. The key algorithmic contribution is a new minimax representation learning objective, for which we provide variants with differing tradeoffs in their statistical and computational properties. We interleave this representation learning step with an exploration strategy to cover the state space in a reward-free manner. The resulting algorithms are provably sample efficient and can accommodate general function approximation to scale to complex environments

Joint Learning of Linear Time-Invariant Dynamical Systems

Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant literature is to leverage commonalities amongst related linear systems to estimate their transition matrices more accurately. To address this problem, the current paper investigates methods for jointly estimating the transition matrices of multiple systems. It is assumed that the transition matrices are unknown linear functions of some unknown shared basis matrices. We establish finite-time estimation error rates that fully reflect the roles of trajectory lengths, dimension, and number of systems under consideration. The presented results are fairly general and show the significant gains that can be achieved by pooling data across systems in comparison to learning each system individually. Further, they are shown to be robust against model misspecifications. To obtain the results, we develop novel techniques that are of interest for addressing similar joint-learning problems. They include tightly bounding estimation errors in terms of the eigen-structures of transition matrices, establishing sharp high probability bounds for singular values of dependent random matrices, and capturing effects of misspecified transition matrices as the systems evolve over time

Machine Learning Framework: Competitive Intelligence and Key Drivers Identification of Market Share Trends Among Healthcare Facilities

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.Comment: 7 Pages 5 figures 6 tables To appear in ICHA 202

Proposed model to predict preeclampsia using machine learning approach

Pregnancy complications, which are the biggest cause of death in productive women, are more common in developing countries with low incomes. One of the contributors to death in pregnant women is preeclampsia which contributes 2-8% every day. Based on research results, more than 70% of the use of technology can be a solution for early prevention in detecting cases of pregnancy. The aim of this research is to build a model for early detection of preeclampsia using a machine learning approach. Sample using retrospective data with sample size 1.473. Based on the result, decision tree (DT) is the best model with accuracy 92.2% (area under curve (AUC): 0.91; Spec: 92.3; and Sens: 83.6), according to weigh correlation we can show 3 (three) highest features causes preeclampsia is history of hypertension, history of diabetes mellitus, and history of preeclampsia. The health of pregnant women is essential in the development of the fetus, so it needs optimal monitoring. Monitoring during pregnancy can now be done through technology-based examinations for assist health workers in making decisions during pregnancy

Antibody–drug conjugates: smart chemotherapy delivery across tumor histologies

Antibody-drug conjugates; Enfortumab vedotin; Smart chemotherapyConjugats anticossos-medicament; Enfortumab vedotin; Quimioteràpia intel·ligentConjugados anticuerpos-fármaco; Enfortumab vedotin; Quimioterapia inteligenteAs distinct cancer biomarkers have been discovered in recent years, a need to reclassify tumors by more than their histology has been proposed, and therapies are now tailored to treat cancers based on specific molecular aberrations and immunologic markers. In fact, multiple histology-agnostic therapies are currently adopted in clinical practice for treating patients regardless of their tumor site of origin. In parallel with this new model for drug development, in the past few years, several novel antibody–drug conjugates (ADCs) have been approved to treat solid tumors, benefiting from engineering improvements in the conjugation process and the introduction of novel linkers and payloads. With the recognition that numerous surface targets are expressed across various cancer histologies, alongside the remarkable activity of modern ADCs, this drug class has been increasingly evaluated as suitable for a histology-agnostic expansion of indication. For illustration, the anti-HER2 ADC trastuzumab deruxtecan has demonstrated compelling activity in HER2-overexpressing breast, gastric, colorectal, and lung cancer. Examples of additional novel and potentially histology-agnostic ADC targets include trophoblast cell-surface antigen 2 (Trop-2) and nectin-4, among others. In the current review article, the authors summarize the current approvals of ADCs by the US Food and Drug Administration focusing on solid tumors and discuss the challenges and opportunities posed by the multihistological expansion of ADCs.Paulo Tarantino reports personal fees from AstraZeneca outside the submitted work. Roberto Carmagnani Pestana reports consulting fees from Bayer and honoraria from Pfizer, Merck, Bayer, and Servier outside the submitted work. Shanu Modi reports grants and nonfinancial support from Daiichi-Sankyo, Genentech, Novartis, Synta Pharmaceuticals, Seattle Genetics, Macrogenics, Carrick Pharmaceuticals, and Eli Lilly outside the submitted work. Aditya Bardia reports institutional grants from Genentech, Novartis, Pfizer, Merck, Sanofi, Radius Health, Immunomedics Inc, Mersana, Innocrin, and Biotheranostics Inc; consulting fees from Biotheranostics Inc, Pfizer, Novartis, Genentech, Merck, Radius Health, Immunomedics Inc, Spectrum Pharma, Taiho, Sanofi, Daiichi Pharma, AstraZeneca, Puma, Phillips, and Eli Lilly; and meeting support from Biotheranostics Inc, Pfizer, Novartis, Genentech, Merck, Radius Health, Immunomedics Inc, Spectrum Pharma, Taiho, Sanofi, and Phillips outside the submitted work. Sara M. Tolaney reports consulting fees from Novartis, Eli Lilly, Pfizer, Merck, AstraZeneca, Eisai, Puma, Genentech, Immunomedics Inc, Nektar, Tesaro, Daiichi-Sankyo, Athenex, Bristol Myers Squibb, and Nanostring outside the submitted work. Javier Cortes reports grants from Roche, Ariad Pharmaceuticals, AstraZeneca, Baxalta GMBH-Servier Affaires, Bayer Healthcare, Eisai, F Hoffman-LaRoche, Guardant Health, MSD, Pfizer, Piqur Therapeutics, Puma C, and Queen Mary University of London; intellectual property for MedSIR; consulting fees from Roche, Celgene, Cellestia, AstraZeneca, Biothera Pharmaceuticals, Seattle Genetics, Daiichi Sankyo, Erytech, Athenex, Polyphor, Lilly, Merck Sharp & Dohme, GlaxoSmithKline, Leuko, Bioasis, and Clovis Oncology; and honoraria from Roche, Novartis, Celgene, Eisai, Pfizer, Samsung Bioepis, Lilly, Merck Sharp & Dohme, and Daiichi-Sankyo outside the submitted work. Jean-Charles Soria was formerly employed at AstraZeneca and is now employed by Amgen; owns stock in AstraZeneca, Gritstone Bio, and Relay Therapeutics; and serves on the Board of Directors for Hookipa Pharmaceuticals outside the submitted work. Giuseppe Curigliano reports a grant from Merck; consulting fees from Roche, Pfizer, Novartis, Seattle Genetics, Lilly, Ellipses Pharma, Foundation Medicine, Samsung, Daichii-Sankyo, and Exactsciences; honoraria from Pfizer, Novartis, Seattle Genetics, and Daichii-Sankyo; and meeting support from Foche and Pfizer outside the submitted work. Chiara Corti made no disclosures