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3 results for “MIMIC-III”
Structure Annotations of Assessment and Plan Sections from MIMIC-III
<p>Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the "assessment and plan". This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. To structure these sections we collected a dataset of annotations over assessment and plan sections from the publicly available and de-identified MIMIC-III dataset, and developed deep-learning based models to perform this task, described in the associated paper available as a pre-print at: <a href="https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1">https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1</a></p> <p>When using this data please cite our paper:</p> <pre><code>@article {Stupp2022.04.13.22273438, author = {Stupp, Doron and Barequet, Ronnie and Lee, I-Ching and Oren, Eyal and Feder, Amir and Benjamini, Ayelet and Hassidim, Avinatan and Matias, Yossi and Ofek, Eran and Rajkomar, Alvin}, title = {Structured Understanding of Assessment and Plans in Clinical Documentation}, year = {2022}, doi = {10.1101/2022.04.13.22273438}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2022/04/17/2022.04.13.22273438}, journal = {medRxiv} }</code></pre> <p>The dataset, presented here, contains annotations of assessment and plan sections of notes from the publicly available and de-identified MIMIC-III dataset, marking the active problems, their assessment description, and plan action items. Action items are additionally marked as one of 8 categories (listed below). The dataset contains over 30,000 annotations of 579 notes from distinct patients, annotated by 6 medical residents and students. </p> <p>The dataset is divided into 4 partitions - a training set (481 notes), validation set (50 notes), test set (48 notes) and an inter-rater set. The inter-rater set contains the annotations of each of the raters over the test set. Rater 1 in the inter-rater set should be regarded as an intra-rater comparison (details in the paper). The labels underwent automatic normalization to capture entire word boundaries and remove flanking non-alphanumeric characters.</p> <p>Code for transforming labels into TensorFlow examples and training models as described in the paper will be made available at GitHub: <a href="https://github.com/google-research/google-research/tree/master/assessment_plan_modeling">https://github.com/google-research/google-research/tree/master/assessment_plan_modeling</a></p> <p>In order to use these annotations, the user additionally needs to obtain the text of the notes which is found in the NOTE_EVENTS table from MIMIC-III, access to which is to be acquired independently (<a href="http://mimic.mit.edu">https://mimic.mit.edu/</a>)</p> <p>Annotations are given as character spans in a CSV file with the following schema:</p> <table> <tbody> <tr> <td>Field</td> <td>Type</td> <td>Semantics</td> </tr> <tr> <td>partition</td> <td>categorical (one of [train, val, test, interrater]</td> <td>The set of ratings the span belongs to.</td> </tr> <tr> <td>rater_id</td> <td>int</td> <td>Unique id for each the raters</td> </tr> <tr> <td>note_id</td> <td>int</td> <td>The note’s unique note_id, links to the MIMIC-III notes table (as ROW-ID).</td> </tr> <tr> <td>span_type</td> <td>categorical (one of [PROBLEM_TITLE,<br> PROBLEM_DESCRIPTION, ACTION_ITEM]</td> <td>Type of the span as annotated by raters.</td> </tr> <tr> <td>char_start</td> <td>int</td> <td>Character offsets from note start</td> </tr> <tr> <td>char_end</td> <td>int</td> </tr> <tr> <td>action_item_type</td> <td>categorical (one of [MEDICATIONS, IMAGING, OBSERVATIONS_LABS, CONSULTS, NUTRITION, THERAPEUTIC_PROCEDURES, OTHER_DIAGNOSTIC_PROCEDURES, OTHER])</td> <td>Type of action item if the span is an action item (empty otherwise) as annotated by raters.</td> </tr> </tbody> </table>
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
<p><b>Objective:</b> The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.</p> <p><b>Design: </b>A retrospective cohort study.</p> <p><b>Setting and Participants: </b>Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed.</p> <p><strong>Methods</strong>: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.</p> <p><b>Results:</b> Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.</p> <p><b>Conclusions</b><b>:</b> Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.</p>
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
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