Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

Language models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task’s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.

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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

コンピュータ・サイエンス : ACM computing surveys

Most current machine transliteration systems employ a corpus of known sourcetarget word pairs to train their system, and typically evaluate their systems on a similar corpus. In this paper we explore the performance of transliteration systems on corpora that are varied in a controlled way. In particular, we control the number, and prior language knowledge of human transliterators used to construct the corpora, and the origin of the source words that make up the corpora. We find that the word accuracy of automated transliteration systems can vary by up to 30% (in absolute terms) depending on the corpus on which they are run. We conclude that at least four human transliterators should be used to construct corpora for evaluating automated transliteration systems; and that although absolute word accuracy metrics may not translate across corpora, the relative rankings of system performance remains stable across differing corpora.

Corpus Effects on the Evaluation of Automated Transliteration Systems

Precision medicine - where data from patients, their genes, their lifestyles and the available treatments and their combination are taken into account for finding a suitable treatment - requires searching the biomedical literature and other resources such as clinical trials with the patients' information. The retrieved information could then be used in curating data for clinicians for decision-making. We present information retrieval researchers with an on-line system which enables experimentation in search for precision medicine within the framework provided by the TREC Precision Medicine (PM) track. A number of query and document processing and ranking approaches are provided. These include some ofthe most promising gene mention expansion methods, as well as learning-to-rank using neural networks.

An Experimentation Platform for Precision Medicine

The World Health Organization (WHO) and drug regulators in many countries maintain databases for adverse drug reaction reports. Data duplication is a significant problem in such databases as reports often come from a variety of sources. Most duplicate detection techniques either have limitations on handling large amount of data or lack effective means to deal with data with imbalanced label distribution. In this paper, we propose a scalable duplicate detection method built on top of Spark to address these problems. Our method uses the kNN (k nearest neighbors) classifier to identify labelled report pairs that are most useful for classifying new report pairs. To deal with the high computational cost of kNN, we partition the labelled data into clusters for parallel computing. We give a method to minimize the crosscluster kNN search. Our experimental results show that the proposed method is able to produce robust duplicate detection results and scalable performance.

/pdf/parallel-duplicate-detection-in-adverse-drug-reaction-2rzul2sz5e.pdf

Parallel Duplicate Detection in Adverse Drug Reaction Databases with Spark.

Clinical Decision Support (CDS) systems aim to assist clinicians in their daily decision-making related to diagnosis, tests, and treatments of patients by providing relevant evidence from the scientific literature. This promise however is yet to be fulfilled, with search for relevant literature for a given patient condition still being an active research topic. The TREC CDS track was designed to address this research gap. We developed a platform to facilitate experimentation and hypothesis testing for information retrieval researchers working on this topic. It provides a large range of query and document processing techniques that are explored in the biomedical search domain.

A2A: Benchmark Your Clinical Decision Support Search

The star‐rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the “true quality” of products and services is challenging. The mean‐rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the “helpfulness” social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on “helpfulness” achieve better performance than the statistical and heuristic baseline approaches.

Sarvnaz Karimi

Papers

Corpus Effects on the Evaluation of Automated Transliteration Systems

An Experimentation Platform for Precision Medicine

Parallel Duplicate Detection in Adverse Drug Reaction Databases with Spark.

A2A: Benchmark Your Clinical Decision Support Search

Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness