Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results have been obtained using classical techniques, such as bag-of-words features, in combination with statistical models. Recently however Deep Learning techniques, such as Word Embeddings and Recurrent Neural Networks, have shown to possibly have even greater potential. In this work, we apply several Deep Learning and classical machine learning techniques to the task of predicting violence incidents during psychiatric admission using clinical text that is already registered at the start of admission. For this purpose, we use a novel and previously unexplored dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. Results show that predicting violence incidents with state-of-the-art performance is possible, and that using Deep Learning techniques provides a relatively small but consistent improvement in performance. We finally discuss the potential implication of our findings for the psychiatric practice.

Key Points Question To what extent can inpatient violence risk assessment be performed by applying machine learning techniques to clinical notes in patients’ electronic health records? Findings In this prognostic study, machine learning was used to analyze clinical notes recorded in electronic health records of 2 independent psychiatric health care institutions in the Netherlands to predict inpatient violence. Internal predictive validity was measured using areas under the curve, which were 0.797 for site 1 and 0.764 for site 2; however, applying pretrained models to data from other sites resulted in significantly lower areas under the curve. Meaning The findings suggest that inpatient violence risk assessment can be performed automatically using already available clinical notes without sacrificing predictive validity compared with existing violence risk assessment methods.

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The surge in the amount of available data in health care enables a novel, exploratory research approach that revolves around finding new knowledge and unexpected hypotheses from data instead of carrying out well-defined data analysis tasks. We propose a specification of the Cross Industry Standard Process for Data Mining (CRISP-DM), suitable for conducting expert sessions that focus on finding new knowledge and hypotheses in collaboration with local workforce. Our proposed specification that we name CRISP-IDM is evaluated in a case study at the psychiatry department of the University Medical Center Utrecht. Expert interviews were conducted to identify seven research themes in the psychiatry department, which were researched in cooperation with local health care professionals using data visualization as a modeling tool. During 19 expert sessions, two results that were directly implemented and 29 hypotheses for further research were found, of which 24 were not imagined during the initial expert interviews. Our work demonstrates the viability and benefits of involving work floor people in the analyses and the possibility to effectively find new knowledge and hypotheses using our CRISP-IDM method.

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Identification of patient subgroups is an important process for supporting clinical care in many medical specialties. In psychiatry, patient stratification is mainly done using a psychiatric diagnosis following the Diagnostic and Statistical Manual of Mental Disorders (DSM). Diagnostic categories in the DSM are however heterogeneous, and many symptoms cut across several diagnoses, leading to criticism of this approach. Data-driven approaches using clustering algorithms have recently been proposed, but have suffered from subjectivity in choosing a number of clusters and a clustering algorithm. We therefore propose to apply cluster ensemble techniques to the problem of identifying subgroups of psychiatric patients, which have previously been shown to overcome drawbacks of individual clustering algorithms. We first introduce a process guide for modelling and evaluating cluster ensembles in the form of a Meta Algorithmic Model. Then, we apply cluster ensembles to a novel cross-diagnostic dataset from the Psychiatry Department of the University Medical Center Utrecht in the Netherlands. We finally describe the clusters that are identified, and their relations to several clinically relevant variables.

The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process for more than two decades, is known to leave those organizations lacking operational data mining experience puzzled and unable to start their data mining projects. This is especially apparent in the first phase of Business Understanding, at the conclusion of which, the data mining goals of the project at hand should be specified, which arguably requires at least a conceptual understanding of the knowledge discovery process. We propose to bridge this knowledge gap from a Data Science perspective by applying Natural Language Processing techniques (NLP) to the organizations’ e-mail exchange repositories to extract explicitly stated business goals from the conversations, thus bootstrapping the Business Understanding phase of CRISP-DM. Our NLP-Automated Method for Business Understanding (NAMBU) generates a list of business goals which can subsequently be used for further specification of data mining goals. The validation of the results on the basis of comparison to the results of manual business goal extraction from the Enron corpus demonstrates the usefulness of our NAMBU method when applied to large datasets.

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