Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

Abstract

Abstract. From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.

An analytical study of information extraction from unstructured and multidimensional big data

“…It has been identified that text ambiguity, lack of resources, complex nested entities, identification of contextual information, noise in the form of homonyms, language variability and missing data are important challenges in entity recognition from unstructured big data [11,16,105]. It is also found that the volume of unstructured big data changed the technological paradigm from traditional rule-based or learning-based techniques to [9,10].…”

“…These techniques extract entity mentions from the text, clusters the similar entities and identify relations [120]. In this case, intensive data preprocessing will be required for big data because unstructured big data sets have missing values, noise and other errors [16] that produce uninformative as well as incoherent extractions. Semi-supervised techniques use both labeled and unlabeled corpus with small degree of supervision [121].…”

“…Over-fitting can be resolved with self-training [18] and to overcome the limitation of large annotated dataset availability, reinforcement learning or distant supervision can be used because these techniques use small labeled dataset [26], [126]. Timeliness of distribution of data [126], balance of informativeness, representativeness, and diversity [127], data modeling performance for heterogeneous, dimensional, sparse and imbalance data [16] and structuring the unstructured data [10] are open challenges for IE using unstructured big data sets.…”

“…With huge volume and complexity of unstructured big data, natural language free text data implies various issues for the users to extract the most relevant and required information. Noisy and low-quality data is one of the major challenges in IE from big data [16,31,128,129]. It causes difficulties in identifying semantic relatedness among entities and terms [130], improving the effectiveness and performance of IE systems [128], extracting contextually relevant information [31], data modeling [16] and structuring the data [10].…”

A Giant with Feet of Clay: On the Validity of the Data that Feed Machine Learning in Medicine

“…The question of the quality of medical record and of the data extracted from there is still understudied [81,10], let alone in regard to machine learning projects [27].…”