Daphne

.gitignore

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+node_modules
+build
+npm-debug.log
+.env
+.DS_Store
+package-lock.json
+
+# Elastic Beanstalk Files
+.elasticbeanstalk/*
+!.elasticbeanstalk/*.cfg.yml
+!.elasticbeanstalk/*.global.yml

Algorithms/Bayes_Distance_combo/combination.js

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+var db = require('../../controllers/DatabaseConnection')
+var Disease = db.Disease;
+var Symptom = db.Symptom;
+const {Op} = require('sequelize');
+var distance_function = require('euclidean-distance')
+
+function arraysEqual(a, b) {
+  if (a === b) return true;
+  if (a == null || b == null) return false;
+  if (a.length !== b.length) return false;
+  for (var i = 0; i < a.length; ++i) {
+    if (a[i] !== b[i]) return false;
+  }
+  return true;
+}
+
+async function combination(input_symptoms) {
+  var results = []
+  let number_of_input_symptoms = parseFloat(input_symptoms.length)
+  let unchanged_vector = Array((input_symptoms.length)).fill(0);
+  var parent_symptoms = await db.getParentSymptoms(input_symptoms);
+  // again we push superclasses to the input
+  var parent_symptoms_names = parent_symptoms.map(sym => sym.symptom_name);
+
+  for (superclass of parent_symptoms_names) {input_symptoms.push(superclass)};
+
+  var diseases = await Disease.findAll({include: Symptom})
+
+  // initialize the symptom vector (currently all symptom point values are 1.5 but this would change with input symptom frequency)
+  var symptom_vector = Array((input_symptoms.length)).fill(1.5);
+
+  for (var disease of diseases){
+    var disease_vector = Array((input_symptoms.length)).fill(0);
+    // initialize variables
+    let matches = 0;
+    let frequency_sum = 0;
+    let frequency = 0;
+
+    for (var symptom of disease.Symptoms) {
+      frequency_sum += parseFloat(symptom.Correlation.frequency);
+      // If one of the input symptoms matches this symptom
+      if (input_symptoms.includes(symptom.symptom_name)) {
+        matches += 1;
+        frequency += parseFloat(symptom.Correlation.frequency);
+        let index = input_symptoms.indexOf(symptom.name);
+        if (symptom.frequency == 0.895) {disease_vector.splice(index,1,3);}
+        if (symptom.frequency == 0.545) {disease_vector.splice(index,1,2);}
+        if (symptom.frequency == 0.17) {disease_vector.splice(index,1,1);}
+      }
+    }
+
+    if (!arraysEqual(disease_vector,unchanged_vector)) {
+      var distance = (distance_function(disease_vector, symptom_vector));
+      var likelihood = (frequency / frequency_sum) * (matches/number_of_input_symptoms);
+      var score = distance * (1-likelihood);
+      results.push({
+        disease: disease,
+        score: score
+      });
+    }
+  }
+  return results;
+}
+
+module.exports = {
+  combination
+}

Algorithms/Bayes_Distance_combo/combination_test.js

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+var combination = require('./combination');
+
+let input_symptoms = ['Hypertension', 'Portal hypertension', 'Umbilical hernia', 'Ascites', 'Splenomegaly', 'Iron deficiency anemia', 'Polycythemia', 'Anemia', 'Acidosis', 'Pulmonary embolism', 'Hematemesis', 'Colitis', 'Intestinal bleeding', 'Duodenal ulcer', 'Venous thrombosis', 'Portal vein thrombosis', 'Hernia'];
+combination.combination(input_symptoms).then(ranking => {
+  var ordered_ranking = ranking.sort(function(a,b){return a.score - b.score})
+  var results = []
+  for (var i=0; i < 10; i++){
+    results.push({name: ordered_ranking[i].disease.disease_name,score: ordered_ranking[i].score})
+  }
+  console.log('Combination Test Results')
+  console.log(results)
+})

Algorithms/BayesianAlgorithm/bayesian.js

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+// var queries = require(__dirname+'/queries');
+// var q = require('q');
+// var database = require(__dirname+'/db_connection')
+// var getdata_controller = require(__dirname+'/getData');
+
+var db = require('../../controllers/DatabaseConnection')
+var Disease = db.Disease;
+var Symptom = db.Symptom;
+
+// Input symptoms by name: ['Pain','Fever']
+// Returns: array of score/disease tuple: [{disease: Disease, score: float}]
+//TODO: Not case sensitive
+async function likelihoodCalculator(input_symptoms) {
+  //The results array we return later
+  var results = []
+
+  var parent_symptoms = await db.getParentSymptoms(input_symptoms);
+  // number of input symptoms does not includes superclasses
+  let number_of_input_symptoms = parseFloat(input_symptoms.length)
+
+  var parent_symptoms = await db.getParentSymptoms(input_symptoms);
+  var parent_symptoms_names = parent_symptoms.map(sym => sym.symptom_name);
+
+  for (superclass of parent_symptoms_names) {input_symptoms.push(superclass)};
+
+  //We put this in a variable for later use.
+
+  //Pull every disease into memory. TODO: Make request a bit finer.
+  var diseases = await Disease.findAll({include: Symptom})
+
+  //We iterate through each disease and calculate their score.
+  for (var disease of diseases){
+    // Initial some computational variables
+    var frequency = 0.0;
+    var matches = 0.0;
+    var importance  = 1.0; //We don't actually use this at the moment.
+    var frequency_sum = 0.0;
+
+    //Example Symptom object (for easy reference)
+    // {
+    //   id: 'HP:0000003',
+    //   symptom_name: 'Multicystic kidney dysplasia',
+    //   definition: 'Multicystic dysplasi...',
+    //   Correlation: { disease_orpha: '564', symptom_id: 'HP:0000003', frequency: 0.895 }
+    // }
+    for (var symptom of disease.Symptoms) {
+      frequency_sum += parseFloat(symptom.Correlation.frequency);
+
+      // If one of the input symptoms matches this symptom
+      if (input_symptoms.includes(symptom.symptom_name)) {
+        matches += 1;
+        frequency += parseFloat(symptom.Correlation.frequency);
+      }
+
+      //There is no match, so pull parent symptoms and see if they might match.
+
+      }
+
+      //else if: deal with subclasses
+
+    // If there was at least oen symptom match
+    if (matches != 0) {
+      let likelihood = (frequency / frequency_sum) * (matches/number_of_input_symptoms);
+      results.push({
+        disease: disease,
+        score: likelihood
+      })
+    }
+  }
+  return results;
+}
+
+module.exports = {
+  likelihoodCalculator
+}

Algorithms/BayesianAlgorithm/bayesiantest.js

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+// problem is we always get diseases with very few symtpms
+// want to emphasize similar symptoms over few symptoms
+
+var bayesian = require('./bayesian');
+
+let input_symptoms = ['Arthritis','Fever','Anorexia','Immunodeficiency','Arthralgia','Erythema','Neutrophilia','Hepatitis','Pharyngitis']
+
+bayesian.likelihoodCalculator(input_symptoms).then(ranking => {
+  var ordered_ranking = ranking.sort(function(a,b){return b.score - a.score})
+  var results = []
+  for (var i=0; i < 10; i++){
+    results.push({name: ordered_ranking[i].disease.disease_name,score: ordered_ranking[i].score})
+  }
+  console.log('Bayesian Test Results')
+  console.log(results)
+})

Algorithms/DistanceAlgorithm/distance.js

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+var db = require('../../controllers/DatabaseConnection')
+var Disease = db.Disease;
+var Symptom = db.Symptom;
+//const {Op} = require('sequelize');
+
+var distance_function = require('euclidean-distance')
+
+function arraysEqual(a, b) {
+  if (a === b) return true;
+  if (a == null || b == null) return false;
+  if (a.length !== b.length) return false;
+  for (var i = 0; i < a.length; ++i) {
+    if (a[i] !== b[i]) return false;
+  }
+  return true;
+}
+
+async function distanceCalculator(input_symptoms) {
+  //The results array we return later
+  var results = []
+  var parent_symptoms = await db.getParentSymptoms(input_symptoms);
+  var parent_symptoms_names = parent_symptoms.map(sym => sym.symptom_name)
+  // here we just add the parent symptoms to the input symptoms so we treat
+  // all superclasses as symptoms
+  for (superclass of parent_symptoms_names) {input_symptoms.push(superclass)};
+
+  var diseases = await Disease.findAll({include: Symptom})
+
+  // initialize the symptom vector (currently all symptom point values are 1.5 but this would change with input symptom frequency)
+  var symptom_vector = Array((input_symptoms.length)).fill(1.5);
+  let unchanged_vector = Array((input_symptoms.length)).fill(0);
+
+  for (var disease of diseases){
+    var disease_vector = Array((input_symptoms.length)).fill(0);
+    // disease symptoms
+    for (var symptom of disease.Symptoms) {
+      if (input_symptoms.includes(symptom.symptom_name)) {
+        index = input_symptoms.indexOf(symptom.symptom_name);
+        if (symptom.Correlation.frequency == 0.895) {disease_vector.splice(index,1,3);}
+        if (symptom.Correlation.frequency == 0.545) {disease_vector.splice(index,1,2);}
+        if (symptom.Correlation.frequency == 0.17) {disease_vector.splice(index,1,1); }
+      }
+    }
+    // example:
+    // s = 1.5 1.5 1.5 1.5 1.5
+    // d = 1   0   3    2   0
+    if (!arraysEqual(disease_vector,unchanged_vector)) {
+      var distance = (distance_function(disease_vector, symptom_vector));
+      results.push({
+        disease: disease,
+        score: distance
+      })
+    }
+  }
+  return results;
+}
+
+module.exports = {
+  distanceCalculator
+}

Algorithms/DistanceAlgorithm/distance_test.js

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+var distance = require('./distance');
+
+let input_symptoms = ['Hypertension', 'Portal hypertension', 'Umbilical hernia', 'Ascites', 'Splenomegaly', 'Iron deficiency anemia', 'Polycythemia', 'Anemia', 'Acidosis', 'Pulmonary embolism', 'Hematemesis', 'Colitis', 'Intestinal bleeding', 'Duodenal ulcer', 'Venous thrombosis', 'Portal vein thrombosis', 'Hernia'];
+distance.distanceCalculator(input_symptoms).then(ranking => {
+  var ordered_ranking = ranking.sort(function(a,b){return a.score - b.score})
+  var results = []
+  for (var i=0; i < 10; i++){
+    results.push({name: ordered_ranking[i].disease.disease_name,score: ordered_ranking[i].score})
+  }
+  console.log('Distance Test Results')
+  console.log(results)
+})

Algorithms/test.js

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+var bayesian = require('./BayesianAlgorithm/bayesian');
+var distance = require('./DistanceAlgorithm/distance');
+var combination = require('./Bayes_Distance_combo/combination');
+
+
+let input_symptoms = ['Hypertension', 'Portal hypertension', 'Umbilical hernia', 'Ascites', 'Splenomegaly', 'Iron deficiency anemia', 'Polycythemia', 'Anemia', 'Acidosis', 'Pulmonary embolism', 'Hematemesis', 'Colitis', 'Intestinal bleeding', 'Duodenal ulcer', 'Venous thrombosis', 'Portal vein thrombosis', 'Hernia'];
+
+var bayesian = require('./BayesianAlgorithm/bayesian');
+
+bayesian.likelihoodCalculator(input_symptoms).then(ranking => {
+  var ordered_ranking = ranking.sort(function(a,b){return b.score - a.score})
+  var results = []
+  for (var i=0; i < 10; i++){
+    results.push({name: ordered_ranking[i].disease.disease_name,score: ordered_ranking[i].score})
+  }
+  console.log('Bayesian Test Results')
+  console.log(results)
+})
+
+
+distance.distanceCalculator(input_symptoms).then(ranking => {
+  var ordered_ranking = ranking.sort(function(a,b){return a.score - b.score})
+  var results = []
+  for (var i=0; i < 10; i++){
+    results.push({name: ordered_ranking[i].disease.disease_name,score: ordered_ranking[i].score})
+  }
+  console.log('Distance Test Results')
+  console.log(results)
+})
+
+combination.combination(input_symptoms).then(ranking => {
+  var ordered_ranking = ranking.sort(function(a,b){return a.score - b.score})
+  var results = []
+  for (var i=0; i < 10; i++){
+    results.push({name: ordered_ranking[i].disease.disease_name,score: ordered_ranking[i].score})
+  }
+  console.log('Combination Test Results')
+  console.log(results)
+})