Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Can children with speech difficulties process an unfamiliar accent?

This study explores the hypothesis that children identified as having phonological processing problems may have particular difficulty in processing a different accent. Children with speech difficulties (n = 18) were compared with matched controls on four measures of auditory processing. First, an accent auditory lexical decision task was administered. In one condition, the children made lexical decisions about stimuli presented in their own accent (London). In the second condition, the stimuli were spoken in an unfamiliar accent (Glaswegian). The results showed that the children with speech difficulties had a specific deficit on the unfamiliar accent. Performance on the other auditory discrimination tasks revealed additional deficits at lower levels of input processing. The wider clinical implications of the findings are considered

The Future of Emotional Harm

Why should tort law treat claims for emotional harm as a second-class citizen? Judicial skepticism about these claims is long entrenched, justified by an amalgam of perceived problems ranging from proof difficulties for causation and the need to constrain fraudulent claims, to the ubiquity of the injury, and a concern about open-ended liability. To address this jumble of justifications, the law has developed a series of duty limitations to curb the claims and preclude them from reaching the jury for individualized analysis. The limited duty approach to emotional harm is maintained by the latest iteration of the Restatement (Third) of Torts. This Article argues that many of the justifications for curtailing this tort have been discredited by scientific developments. In particular, the rapid advances in neuroscience give greater insight into the changes that occur in the brain from emotional harm. Limited duty tests should no longer be used as proxies for validity or justified by the presumed untrustworthiness of the claim. Instead, validity evidence for emotional harm claims—like evidence of physical harm—should be entrusted to juries. This approach will reassert the jury’s role as the traditional factfinder, promote corrective justice and deterrence values, and lead to greater equity for negligent infliction of emotional distress (NIED) claimants. The traditional limitations on tort recovery, including the rules of evidence and causation, are more than adequate to avoid opening the floodgates to emotional distress claims

The Future of Emotional Harm

Why should tort law treat claims for emotional harm as a second-class citizen? Judicial skepticism about these claims is long entrenched, justified by an amalgam of perceived problems ranging from proof difficulties for causation and the need to constrain fraudulent claims, to the ubiquity of the injury, and a concern about open-ended liability. To address this jumble of justifications, the law has developed a series of duty limitations to curb the claims and preclude them from reaching the jury for individualized analysis. The limited duty approach to emotional harm is maintained by the latest iteration of the Restatement (Third) of Torts. This Article argues that many of the justifications for curtailing this tort have been discredited by scientific developments. In particular, the rapid advances in neuroscience give greater insight into the changes that occur in the brain from emotional harm. Limited duty tests should no longer be used as proxies for validity or justified by the presumed untrustworthiness of the claim. Instead, validity evidence for emotional harm claims—like evidence of physical harm—should be entrusted to juries. This approach will reassert the jury’s role as the traditional factfinder, promote corrective justice and deterrence values, and lead to greater equity for negligent infliction of emotional distress (NIED) claimants. The traditional limitations on tort recovery, including the rules of evidence and causation, are more than adequate to avoid opening the floodgates to emotional distress claims

Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decision

Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes