A Predictive Analytics Toolbox for Medical Applications Michael L. Valenzuela and Jerzy W. Rozenblit Allan J. Hamilton Electrical and Computer Engineering Department College of Medicine University of Arizona University of Arizona [email protected], [email protected] [email protected] Keywords: Management, Decision Support, Healthcare, Vi- ral network to improve the outcome of fractionated radiother- sualization apy. Back in 2002, support vector machines improved cancer Abstract classification using gene selection from 86% accurate to 98% Ever more frequently business enterprises are benefiting from accurate [15]. However, the use of advanced tools for hospi- the collection and analysis of large data. This paper reports on tal administration has lagged behind advances in biomedical a work in progress of adapting intelligence, predictive analyt- research, possibly due to the different nature of hospital ad- ics tools for analysis of medical data. While large data has ministration. been used in medical research, only recently has this become The demands on biomedical research are similar, but not a trend for hospital management. A suite of tools originally the same as that of hospital administration. Both share ethical developed for military intelligence analysts are repurposed concerns, but biomedical ethics concerns itself with “playing for hospital management. The original design concepts is re- God” and the dangers of the substances/organisms. Hospitals viewed, its medical applications and challenges are described have to worry more about privacy concerns and patient safety. along with an illustrative example. The top ranking concerns for hospitals have been financial concerns for the last 10 years [16]. As such hospitals are con- 1. INTRODUCTION cerned with avoiding bad debt, reducing operating costs, and preventing lawsuits. Whereas in the world of research the pri- Over the last two decades, information technology (IT) mary demand is get valid, significant results, before anyone systems are being increasingly adopted by hospitals. Even else. seemingly mundane IT systems such as computerized patient In this paper, we present a single comprehensive tool, records were already installed in 1997 at Cabarrus Family Med-ThinkTM. Med-Think, previously developed as an in- Medicine in Concord, NC [1]. In 1996, the Johnson Medical telligence analyst’s toolbox [4–6], reduces the gap between Center in Johnson City, TN, decided it needed a data ware- hospital management tools and those tools used for biomed- house to spot trends and anomalies [1]. The next natural pro- ical research. It offers a plethora of data visualization, ex- gression of this pattern is to use a computer system driven by ploration, querying, analysis, and management capabilities. the data warehouse to provide medical decision support. Even though Med-Think is still under development, it offers Hospital management is in need of decision-support sys- hospital management a data driven management and decision tems (DSS). DSS have been used in many domains, ranging support system. from stability and support operations [2, 3], to intelligence The rest of this article proceeds as follows. Section 2 re- analysis [4–6], to the medical domain [7–11]. Such a sys- views alternative systems and briefly discusses the history of tem would help hospitals in many ways: cut costs; discover Med-Think. This is followed by a description of how Med- and prevent causes of medical mistakes; decide whether a Think’s models data in Section 3. Section 4 describes Med- patient should recover from home or have a longer hospital Think’s capabilities and applications. We discuss immediate stay; identify risk factors; optimize bed assignments; analyze challenges to the adoption of the system in Section 5. Last, work flow; and in general uncover actionable information. we conclude and discuss future directions for Med-Think in Yet, most prior DSS in the medical domain have focused on Section 6. recommending drug prescriptions, diagnosing the source of chest pain, treating infertility, and promptly administering im- munizations [8, 11]. Yes, hospital management systems have 2. BACKGROUND lagged behind that of medical science. Due to the rapidly emerging interest in advanced data A multitude of work applies advanced data analysis tech- driven hospital management, the breadth, variety, and num- niques to biomedical problems. [12] use hidden Markov mod- ber of these tools are likely to explode in the coming years. els (HMM) to shed light on the folding pathways of a com- However, the recent demand for advanced data driven hospi- plex protein. [13] built a metamorphic virus detector based on tal management tools has been left unmet. This is partially HMM. [14] uses reinforcement learning and an artificial neu- due to the lack of available tools. As such, we only review a 180 ...... ...Equipment ... ... few of these systems. –Dose Recent progress in health informatics has focused primar- ...... Medicine –Route ily on data visualization and basic regression analysis. [17] –Side-effects apply temporal data mining and exploratory data analysis methods to hospital management data. They visualize pairs of ...... Chemo- trends as trajectories, plot data, and use a generalized linear Therapy model to predict hospital revenue in terms of stay duration, gender, age, treatment outcomes, and admission time. How- ...... ever, their analysis is adhoc, being built upon an amalgam of Alkylating –Rate tools. They performed the generalized linear model analysis Agent using the “R Project” and a custom C++ program for the tra- jectory analysis [18]. Furthermore, the applied techniques are ......Hexamethylmelamine ThioTEPA far from as sophisticated as the techniques applied in biomed- ical research. Figure 1. Example Entity Hierarchy While there is little known about Raytheon’s INTER- SECT CONNECT○R , the available public information sug- 3. MODELING DATA gests that it can easily be adapted to be a contender as an Med-Think possesses a database backend to handle large, advanced data driven management tool. Officially INTER- varied data. Having such, Med-Think’s modeling is not lim- SECT CONNECT○R is “[s]oftware that enables users to mon- ited by computer memory. The database mostly comprises: itor and analyze multi-source streams of data and to produce reports based thereon” [19]. Much is still unknown about this ∙ raw imported data including reports, comma separated project. Furthermore, at the time this article was written, it value (CSV) files, and multimedia, was non-trivial to purchase a license to use the software. ∙ “entities” (organizations, persons, places, events, equip- Another available tool is the Hospital Management Sys- ments, etc.), tem.1 It is a free and open source management system, mean- ing that a programmer can make changes to the software to ∙ a hierarchy of entity types and their corresponding data better suit the needs of the hospital. However, this tool han- fields, dles only hospital logistics. It supports managing “patient in- ∙ five-tuples composed of an entity, latitude, longitude, formation, staff information, stores and medicines, billing and date, and time, report generation. This complex application communicates with a backend database server and manages all information ∙ directed, typed relationships between two entities, and related to hospital logistics.” It lacks the data analysis capa- bilities needed to offer data driven decision support. ∙ queries and their results. Med-Think, at its roots, is derived from an intelligence an- We use the generic term “entity” to refer to any object we alyst’s toolbox known as the Asymmetric Threat Response wish to model. Fundamentally, an entity has a type from a and Analysis Program (ATRAP) [4, 5]. As such, it was origi- user-customizable type hierarchy. Each type in the hierarchy nally developed to assist in the processing and dissemination may have its own data fields and inherited data fields from of massive data in an unstable, rapidly changing environment. its ancestor types. For example, an entity might be specified Its capabilities are the abilities to: a) ingest large data sets in as a medicine (parametrized by a dose, route, etc.), an alky- multiple formats, b) derive links and pattern over data sets and lating agent (furthermore parametrized by a rate), or specific carry our extensive social network analysis, c) visualize data substances such as hexamethylmelamine and ThioTEPA (cf. patterns in geo-temporal spaces, and d) carry out “what if sce- Figure 1). Entities may be an instantiation a type or a sin- narios” through sophisticated game-theory based algorithms. gleton for that type. Additionally, an entity may have zero ATRAP’s capabilities were appropriate to port over to Med- or more geo-temporal data points. Each five-tuple assigns an Think. Our system focuses on both geo-temporal data (e.g., absolute time and location to an entity. This is particularly the spread of a pathogen through a hospital) and relational useful for modeling the spread of a disease through a hospi- data (e.g., how doctor X, nurse Y, patient Z, and a medical tal, city, or country. Lastly, entities may have any number of error are connected). We discuss these capabilities in more directed, typed relationships to other entities. For example, a detail in the following section. doctor may specialize in treating a disease, but a patient may have a disease. The types of relationships are also user cus- 1It is an open source project hosted at http://hospitalmanage. tomizable. sourceforge.net/ Queries are modeled as the following 10-tuple: 181 ∙ an input (specifically a collection of entities), if none is provided the whole database is used, ∙ an input entity type, ∙ an option to match more general entity types, ∙ a search string, ∙ a collection of data fields and their values, ∙ a collection of relationships the entity must share, Figure 2. Med-Think supports importing data from many ∙ an optional start date-time, and varied sources ∙ an optional end date-time, ∙ a geographic area, and ∙ an output entity type. The query allows for any data associated with entities to be searched.