Travis Goodwin

Medical Question Answering for Clinical Decision Support

The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40\%.

Automatic Generation of a Qualified Medical Knowledge Graph and Its Usage for Retrieving Patient Cohorts from Electronic Medical Records

An extraordinary amount of clinical information is available within Electronic Medical Records. However, interpreting this knowledge typically demands a significant level of clinical understanding. This can facilitated by access to structured knowledge bases. However, even if vast, biomedical knowledge bases have very limited relational information available. In contrast, clinical text expresses many relations between concepts using an extraordinary amount of variation regarding the authors belief state - whether a medical concept is present, uncertain, or absent. In this paper, we propose a method for automatically constructing a graph of clinically related concepts based on their belief state. For this purpose, we first devise a method for classifying the belief state of certain medical concepts. Second, we designed a technique for constructing a graph of related medical concepts qualified by the physicians belief value. Thirdly, we demonstrate several techniques for inferring the similarity between qualified medical concepts, and present a generalized algorithm for determining the second-order similarity between qualified medical concepts. Finally, we show that incorporating the knowledge encoded from this graph yield competitive results when applied to query expansion for the retrieval of hospital patient cohorts.

The Impact of Belief Values on the Identification of Patient Cohorts

Retrieving relevant patient cohorts has the potential to accelerate clinical research. Recent evaluations have shown promising results, but also relevance measures that still need to be improved. To address the challenge of better modelling hospital visit relevance, we considered the impact of two forms of medical knowledge on the quality of patient cohorts. First, we automatically identified three types of medical concepts and, second, we asserted their belief values. This allowed us to perform experiments that capture the impact of incorporating knowledge of belief values within a retrieval system for identifying hospital visits corresponding to patient cohorts. We show that this approach generates a 149% increase for inferred average precision, a 36.5% increase of NDCG, and a 207% increase to the precision of the first ten returned documents.

Automatic recognition of symptom severity from psychiatric evaluation records

This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation. The machine learning techniques rely on the assumptions that (1) the severity of a patients positive valence symptoms exists on a latent continuous spectrum and (2) all the patients answers and narratives documented in the psychological evaluation records are informed by the patients latent severity score along this spectrum. These assumptions motivated our two-step machine learning framework for automatically recognizing psychological symptom severity. In the first step, the latent continuous severity score is inferred from each record; in the second step, the severity score is mapped to one of the four discrete severity levels used in the CEGS/N-GRID challenge. We evaluated three methods for inferring the latent severity score associated with each record: (i) pointwise ridge regression; (ii) pairwise comparison-based classification; and (iii) a hybrid approach combining pointwise regression and the pairwise classifier. The second step was implemented using a tree of cascading support vector machine (SVM) classifiers. While the official evaluation results indicate that all three methods are promising, the hybrid approach not only outperformed the pairwise and pointwise methods, but also produced the second highest performance of all submissions to the CEGS/N-GRID challenge with a normalized MAE score of 84.093% (where higher numbers indicate better performance). These evaluation results enabled us to observe that, for this task, considering pairwise information can produce more accurate severity scores than pointwise regression - an approach widely used in other systems for assigning severity scores. Moreover, our analysis indicates that using a cascading SVM tree outperforms traditional SVM classification methods for the purpose of determining discrete severity levels.

Knowledge Representations and Inference Techniques for Medical Question Answering

Answering medical questions related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowledge and (2) sophisticated inference techniques. In this article, we examine the representation and role of combining medical knowledge automatically derived from (a) clinical practice and (b) research findings for inferring answers to medical questions. Knowledge from medical practice was distilled from a vast Electronic Medical Record (EMR) system, while research knowledge was processed from biomedical articles available in PubMed Central. The knowledge automatically acquired from the EMR system took into account the clinical picture and therapy recognized from each medical record to generate a probabilistic Markov network denoted as a Clinical Picture and Therapy Graph (CPTG). Moreover, we represented the background of medical questions available from the description of each complex medical case as a medical knowledge sketch. We considered three possible representations of medical knowledge sketches that were used by four different probabilistic inference methods to pinpoint the answers from the CPTG. In addition, several answer-informed relevance models were developed to provide a ranked list of biomedical articles containing the answers. Evaluations on the TREC-CDS data show which of the medical knowledge representations and inference methods perform optimally. The experiments indicate an improvement of biomedical article ranking by 49% over state-of-the-art results.

GRAPHICAL INDUCTION OF QUALIFIED MEDICAL KNOWLEDGE

The introduction of electronic medical records (EMRs) enabled the access of unprecedented volumes of clinical data, both in structured and unstructured formats. A significant amount of this clinical data is expressed within the narrative portion of the EMRs, requiring natural language processing techniques to unlock the medical knowledge referred to by physicians. This knowledge, derived from the practice of medical care, complements medical knowledge already encoded in various structured biomedical ontologies. Moreover, the clinical knowledge derived from EMRs also exhibits relational information between medical concepts, derived from the cohesion property of clinical text, which is an attractive attribute that is currently missing from the vast biomedical knowledge bases. In this paper, we describe an automatic method of generating a graph of clinically related medical concepts by considering the belief values associated with those concepts. The belief value is an expression of the clinicians assertion that the concept is qualified as present, absent, suggested, hypothetical, ongoing, etc. Because the method detailed in this paper takes into account the hedging used by physicians when authoring EMRs, the resulting graph encodes qualified medical knowledge wherein each medical concept has an associated assertion (or belief value) and such qualified medical concepts are spanned by relations of different strengths, derived from the clinical contexts in which concepts are used. In this paper, we discuss the construction of a qualified medical knowledge graph (QMKG) and treat it as a BigData problem addressed by using MapReduce for deriving the weighted edges of the graph. To be able to assess the value of the QMKG, we demonstrate its usage for retrieving patient cohorts by enabling query expansion that produces greatly enhanced results against state-of-the-art methods.

The Role of Semantic and Discourse Information in Learning the Structure of Surgical Procedures

Electronic Operative Notes are generated after surgical procedures for documentation and billing. These operative notes, like many other Electronic Medical Records (EMRs) have the potential of an important secondary use: they can enable surgical clinical research aimed at improving evidence-based medical practice. Recognizing surgical techniques by capturing the structure of a surgical procedure requires the semantic processing and discourse understanding of operative notes. Identifying only predicates pertaining to surgical actions does not explain the various possible surgical scripts. Similarly, recognizing all actions and observations pertaining to a surgical step cannot be performed without taking into account discourse structure. In this paper we show how combining both forms of clinical language processing leads to learning the structure of surgical procedures. Experimental results on two large sets of operative notes show promising results.