Free Text Prediction Algorithm for Appendicitis
Free Text Prediction Algorithm for Appendicitis
Computer-aided diagnostic software has been used to assist physicians in various ways.
 Text-based prediction algorithms have been trained on past medical records through data
 mining and feature analysis. Currently, all text-based machine learning prediction problem
 models have been built on extracted data with no research completed on free text based
 prediction algorithms. This study aims to determine the accuracy of a free text prediction
 algorithm in predicting the probability of appendicitis in patients presenting to the
 Emergency Department with abdominal pain and gastrointestinal symptoms.
Developing machine learning models that have a strong prediction power for diagnosis of
 appendicitis from physician entered free text input can improve diagnostic accuracy of
 doctors. It also offers the possibility of using prediction algorithms to improve routine
 clinical care. In the future, multiple machine learning models can be combined to increase
 prediction accuracy and prediction algorithms can be extended to other diagnoses.

 18,000 cases of emergency department presentations over 10 years were used as a training and
 validation dataset. To develop the appendicitis prediction model, deep learning neural
 networks with a customized medical ontology were used. The diagnostic accuracy of the model
 is expressed as sensitivity (recall), specificity and F1 score (harmonic mean). The developed
 diagnosis predictive model shows high sensitivity (86.3%), specificity (91.9%) and F1 score
 (88.8) in diagnosing appendicitis from patients presenting with abdominal pain.

 The predictive model algorithm will also highlight words in the free text (entered by the
 attending physician) that it assigns higher probability for predicting an outcome. The
 doctors will be instructed to provide a percentage likelihood of appendicitis based on the
 clinical presentation and any available laboratory investigations. The doctor is then shown
 the prediction of the algorithm as well as the highlighted words for the patient entered.
 He/she must then provide another prediction of the likelihood of appendicitis after seeing
 the algorithm generated prediction.

 The aim is to evaluate the performance of the algorithm and to assess if usage of the
 algorithm is able to help emergency doctors improve their diagnosis of appendicitis. The
 prediction results will be tabulated to assess accuracy of the algorithm, doctors before
 algorithm input and doctors after receiving algorithm input. The accuracy will be expressed
 as sensitivity, specificity, accuracy, positive prediction value, F1 score and F0.5 score.

 Approximately 100 emergency doctors will be recruited over the course of 1 year as
 participants in the study. The doctors will be split randomly assigned to two groups - the
 algorithm arm and the no algorithm arm. The randomization will be by time (weekly) using
 variable block randomization of 4 and 6. The patients will be followed up for the final
 discharge diagnoses.
Eligibility criteria of doctors- Inclusion criteria: Junior doctors working in the
 Emergency Department Exclusion criteria: Refusal of consent

 Eligibility criteria of patients-

 Inclusion Criteria:

 -  Presence of abdominal pain, OR

 -  Presence of gastrointestinal symptoms such as nausea, vomiting or diarrhea, OR

 -  Fever with anorexia

 Exclusion Criteria:

 -  Previous history of appendicectomy

 -  Refusal of consent