(update machine learning component

openmrs · May 15, 2024 · e3db32d · e3db32d
1 parent 3372df4
commit e3db32d
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Showing 8 changed files with 176 additions and 23 deletions.
diff --git a/...ects/ngx-formentry/src/components/machine-learning-button/machine-learning.component.html b/...ects/ngx-formentry/src/components/machine-learning-button/machine-learning.component.html
@@ -1,4 +1,81 @@
-<div style="margin-top: 0.5rem;" *ngIf="showMachineLearningButton">
+<div style="margin-top: 0.5rem">
+  <div
+    *ngIf="riskScore"
+    style="margin-bottom: 0.5rem"
+    role="status"
+    class="cds--inline-notification cds--inline-notification--low-contrast"
+    [ngClass]="{
+      'cds--inline-notification--error':
+        riskScore === 'Highest Risk Client' || riskScore === 'High Risk Client',
+      'cds--inline-notification--success':
+        riskScore === 'Medium Risk Client' || riskScore === 'Low Risk',
+      'cds--inline-notification--warning': riskScore === 'No Risk Score Available'
+    }"
+  >
+    <div class="cds--inline-notification__details">
+      <svg
+        *ngIf="
+          riskScore === 'Highest Risk Client' ||
+          riskScore === 'High Risk Client'
+        "
+        focusable="false"
+        preserveAspectRatio="xMidYMid meet"
+        fill="currentColor"
+        width="20"
+        height="20"
+        viewBox="0 0 32 32"
+        aria-hidden="true"
+        class="cds--inline-notification__icon"
+        xmlns="http://www.w3.org/2000/svg"
+      >
+        <path
+          fill="none"
+          d="M16,26a1.5,1.5,0,1,1,1.5-1.5A1.5,1.5,0,0,1,16,26Zm-1.125-5h2.25V12h-2.25Z"
+          data-icon-path="inner-path"
+        ></path>
+        <path
+          d="M16.002,6.1714h-.004L4.6487,27.9966,4.6506,28H27.3494l.0019-.0034ZM14.875,12h2.25v9h-2.25ZM16,26a1.5,1.5,0,1,1,1.5-1.5A1.5,1.5,0,0,1,16,26Z"
+        ></path>
+        <path
+          d="M29,30H3a1,1,0,0,1-.8872-1.4614l13-25a1,1,0,0,1,1.7744,0l13,25A1,1,0,0,1,29,30ZM4.6507,28H27.3493l.002-.0033L16.002,6.1714h-.004L4.6487,27.9967Z"
+        ></path>
+        <title>notification</title>
+      </svg>
+
+      <svg
+        *ngIf="riskScore === 'Medium Risk Client' || riskScore === 'Low Risk'"
+        focusable="false"
+        preserveAspectRatio="xMidYMid meet"
+        fill="currentColor"
+        width="20"
+        height="20"
+        viewBox="0 0 20 20"
+        aria-hidden="true"
+        class="cds--inline-notification__icon"
+        xmlns="http://www.w3.org/2000/svg"
+      >
+        <path
+          d="M10,1c-4.9,0-9,4.1-9,9s4.1,9,9,9s9-4,9-9S15,1,10,1z M8.7,13.5l-3.2-3.2l1-1l2.2,2.2l4.8-4.8l1,1L8.7,13.5z"
+        ></path>
+        <path
+          fill="none"
+          d="M8.7,13.5l-3.2-3.2l1-1l2.2,2.2l4.8-4.8l1,1L8.7,13.5z"
+          data-icon-path="inner-path"
+          opacity="0"
+        ></path>
+        <title>notification</title>
+      </svg>
+      <div class="cds--inline-notification__text-wrapper">
+        <div class="cds--inline-notification__title" dir="ltr">
+          {{ riskScore }}
+        </div>
+        <div class="cds--inline-notification__subtitle" dir="ltr">
+          {{ riskScoreMessage }}
+        </div>
+      </div>
+    </div>
+  </div>
+
   <button
     (click)="getRiskScore()"
     class="cds--btn cds--btn--primary cds--layout--size-sm"

diff --git a/projects/ngx-formentry/src/components/machine-learning-button/machine-learning.component.ts b/projects/ngx-formentry/src/components/machine-learning-button/machine-learning.component.ts
@@ -1,5 +1,4 @@
 import { Component, Input, OnInit } from '@angular/core';
-
 import { LeafNode } from '../../form-entry/form-factory/form-node';
 import { ObsAdapterHelper } from '../../form-entry/value-adapters/obs-adapter-helper';
 import { generatePredictionPayload } from './model-helper';
@@ -17,9 +16,11 @@ interface Payload {
 export class MachineLearningComponent implements OnInit {
   @Input() node: LeafNode;
   showMachineLearningButton = false;
-  isLoading: boolean;
-  hasError: boolean;
-  errorMessage: string;
+  isLoading = false;
+  hasError = false;
+  errorMessage = '';
+  riskScoreMessage = '';
+  riskScore = '';
 
   constructor(
     private obsAdapter: ObsAdapterHelper,
@@ -28,7 +29,7 @@ export class MachineLearningComponent implements OnInit {
 
   ngOnInit() {
     this.showMachineLearningButton =
-      this.node.question.extras?.questionOptions['machineLearning'] || false;
+      this.node.question.extras?.questionOptions?.machineLearning || false;
   }
 
   getRiskScore() {
@@ -50,12 +51,20 @@ export class MachineLearningComponent implements OnInit {
 
       this.machineLearningService.fetchPredictionScore(riskPayload).subscribe(
         (res) => {
+          if (!res) {
+            this.isLoading = false;
+            this.errorMessage = 'An error occurred while fetching risk score';
+            this.setRiskScore(this.errorMessage);
+            this.setAutoGenerateRiskScore(0, this.errorMessage);
+            return;
+          }
+
           const {
             message,
             riskScore
           } = this.machineLearningService.predictRisk(res);
           this.setRiskScore(message);
-          this.setAutoGenerateRiskScore(riskScore);
+          this.setAutoGenerateRiskScore(riskScore, message);
           this.isLoading = false;
         },
         (error) => {
@@ -75,7 +84,7 @@ export class MachineLearningComponent implements OnInit {
     return this.obsAdapter
       .getObsNodePayload(this.node.form.rootNode)
       .reduce((acc, item) => {
-        acc[item.concept] = item['value'];
+        acc[item.concept] = item.value;
         return acc;
       }, {});
   }
@@ -87,7 +96,7 @@ export class MachineLearningComponent implements OnInit {
         return { key, value: payload[valueKey] ?? '' };
       })
       .reduce((acc, item) => {
-        acc[item.key] = this.extractNumbersOrUuid(item['value']);
+        acc[item.key] = this.extractNumbersOrUuid(item.value);
         return acc;
       }, {});
   }
@@ -107,7 +116,7 @@ export class MachineLearningComponent implements OnInit {
   }
 
   private generateKeyValue(): Payload {
-    let questionConcepts = {};
+    let questionConcepts: Payload = {};
     this.node.form.schema.pages.forEach((page) => {
       page.sections.forEach((section) => {
         section.questions.forEach((question) => {
@@ -125,7 +134,7 @@ export class MachineLearningComponent implements OnInit {
     riskScore.control.setValue(score);
   }
 
-  extractNumbersOrUuid(inputStr) {
+  private extractNumbersOrUuid(inputStr: string) {
     inputStr = String(inputStr); // Convert to string just in case
     const uuidPattern = /^[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$/i;
     if (uuidPattern.test(inputStr)) {
@@ -136,7 +145,7 @@ export class MachineLearningComponent implements OnInit {
     }
   }
 
-  announceRequiredFields() {
+  private announceRequiredFields() {
     const requiredFields = [
       'populationType',
       'facilityHTStrategy',
@@ -155,7 +164,7 @@ export class MachineLearningComponent implements OnInit {
       });
   }
 
-  hasAllrequiredFields() {
+  private hasAllrequiredFields() {
     const requiredFields = [
       'populationType',
       'facilityHTStrategy',
@@ -174,32 +183,45 @@ export class MachineLearningComponent implements OnInit {
     );
   }
 
-  getRiskLevel = (probabilityForPositivity) => {
+  private getRiskLevel = (
+    probabilityForPositivity: number,
+    message: string
+  ) => {
     const highRiskThreshold = 0.1079255;
     const mediumRiskThreshold = 0.02795569;
     const lowRiskThreshold = 0.005011473;
 
+    if (probabilityForPositivity === 0) {
+      this.riskScore = 'No Risk Score Available';
+      return '';
+    }
+
     if (probabilityForPositivity > highRiskThreshold) {
+      this.riskScore = 'Highest Risk Client';
       return '167164AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA';
     } else if (
       probabilityForPositivity <= highRiskThreshold &&
       probabilityForPositivity > mediumRiskThreshold
     ) {
+      this.riskScore = 'High Risk Client';
       return '166674AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA';
     } else if (
       probabilityForPositivity <= mediumRiskThreshold &&
       probabilityForPositivity > lowRiskThreshold
     ) {
+      this.riskScore = 'Medium Risk Client';
       return '1499AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA';
     } else if (probabilityForPositivity <= lowRiskThreshold) {
+      this.riskScore = 'Low Risk Client';
       return '166675AAAAAAAAAAAAAAAAAAAAAAAAAAAAAA';
     }
   };
 
   // Set the autocalculate risk score
-  private setAutoGenerateRiskScore(riskScore: number) {
+  private setAutoGenerateRiskScore(riskScore: number, message: string) {
+    this.riskScoreMessage = message;
     const genRisKQuestion = this.node.form.searchNodeByQuestionId('genRisK');
-    const riskLevel = this.getRiskLevel(riskScore);
+    const riskLevel = this.getRiskLevel(riskScore, message);
     genRisKQuestion?.[0]?.control?.setValue(riskLevel);
   }
 }
diff --git a/projects/ngx-formentry/src/components/machine-learning-button/machine-learning.service.ts b/projects/ngx-formentry/src/components/machine-learning-button/machine-learning.service.ts
@@ -21,7 +21,7 @@ export class MachineLearningService {
     // Check if the prediction is available
     const probabilityForPositivity = res?.result?.predictions['probability(1)'];
     if (!probabilityForPositivity) {
-      return { message: 'No results found', riskScore: null };
+      return { message: 'No results found', riskScore: 0 };
     }
 
     // Define risk thresholds
@@ -40,7 +40,7 @@ export class MachineLearningService {
       low:
         'This client has a low probability of a HIV positive test result. Testing may not be recommended'
     };
-    0.1079255<=0.936823 && 0.1079255>0.02795569
+
     // Determine risk level and corresponding message
     let riskLevel;
     if (probabilityForPositivity > highRiskThreshold) {
@@ -55,7 +55,10 @@ export class MachineLearningService {
       probabilityForPositivity > lowRiskThreshold
     ) {
       riskLevel = 'medium';
-    } else if (probabilityForPositivity <= lowRiskThreshold) {
+    } else if (
+      probabilityForPositivity <= lowRiskThreshold &&
+      probabilityForPositivity !== 0
+    ) {
       riskLevel = 'low';
     }
 

diff --git a/projects/ngx-formentry/src/form-entry/form-factory/question.factory.ts b/projects/ngx-formentry/src/form-entry/form-factory/question.factory.ts
@@ -33,6 +33,8 @@ import { MinLengthValidationModel } from '../question-models/min-length-validati
 import { WorkspaceLauncherQuestion } from '../question-models';
 import { DecimalValidationModel } from '../question-models/decimal-validation.model';
 import { DisallowDecimalsValidationModel } from '../question-models/disallow-decimals-validation.model';
+import { MachineLearningQuestion } from '../question-models/machine-learning.model';
+
 @Injectable()
 export class QuestionFactory {
   dataSources: any = {};
@@ -745,6 +747,28 @@ export class QuestionFactory {
     return question;
   }
 
+  toMachineLearningQuestion(schemaQuestion: any): MachineLearningQuestion {
+    const question = new MachineLearningQuestion({
+      type: '',
+      key: schemaQuestion.id,
+      label: schemaQuestion.label
+    });
+    question.questionIndex = this.quetionIndex;
+    question.prefix = schemaQuestion.prefix;
+    question.renderingType = 'machine-learning';
+    question.validators = this.addValidators(schemaQuestion);
+    question.extras = schemaQuestion;
+    question.dataSource = schemaQuestion.questionOptions.dataSource;
+    const mappings = {
+      label: 'label',
+      required: 'required',
+      id: 'key'
+    };
+    question.componentConfigs = schemaQuestion.componentConfigs || [];
+    this.copyProperties(mappings, schemaQuestion, question);
+    return question;
+  }
+
   toDecimalQuestion(schemaQuestion: any): TextInputQuestion {
     const question = new TextInputQuestion({
       placeholder: '',
@@ -938,6 +962,8 @@ export class QuestionFactory {
         return this.toFileUploadQuestion(schema);
       case 'workspace-launcher':
         return this.toWorkspaceLauncher(schema);
+      case 'machine-learning':
+        return this.toMachineLearningQuestion(schema);
       default:
         console.warn('New Schema Question Type found.........' + renderType);
         return this.toTextQuestion(schema);

diff --git a/projects/ngx-formentry/src/form-entry/form-renderer/form-renderer.component.html b/projects/ngx-formentry/src/form-entry/form-renderer/form-renderer.component.html
@@ -375,9 +375,12 @@
             [id]="node.question.key + 'id'"
             [readOnly]="node.question.extras.readOnly"
           />
-          <machine-learning-risk-score
-            [node]="node"
-          ></machine-learning-risk-score>
+
+          <div *ngSwitchCase="'machine-learning'">
+            <machine-learning-risk-score
+              [node]="node"
+            ></machine-learning-risk-score>
+          </div>
 
           <div *ngSwitchCase="'radio'">
             <ofe-radio-button

diff --git a/projects/ngx-formentry/src/form-entry/question-models/machine-learning.model.ts b/projects/ngx-formentry/src/form-entry/question-models/machine-learning.model.ts
@@ -0,0 +1,12 @@
+import { QuestionBase } from './question-base';
+import { AfeControlType } from '../../abstract-controls-extension/afe-control-type';
+import { BaseOptions } from './models';
+
+export class MachineLearningQuestion extends QuestionBase {
+  constructor(options: BaseOptions) {
+    super(options);
+    this.renderingType = 'machine-learning';
+    this.label = options.label || '';
+    this.controlType = AfeControlType.AfeFormControl;
+  }
+}
diff --git a/projects/ngx-formentry/src/form-entry/value-adapters/encounter.adapter.ts b/projects/ngx-formentry/src/form-entry/value-adapters/encounter.adapter.ts
@@ -106,6 +106,16 @@ export class EncounterAdapter implements ValueAdapter {
             const dateValue = moment(node.control.value).utcOffset(
               rootNode.form.valueProcessingInfo.utcOffset || '+0300'
             );
+            // If the dateValue is same as new Date() then we are not going to send the encounterDatetime
+            // to the server. This is to avoid sending the encounterDatetime to the server when the user
+            // has not changed the encounterDatetime
+            console.log(
+              dateValue.format() + ' === ' + moment(new Date()).format()
+            );
+
+            if (dateValue.isSame(moment(new Date()), 'day')) {
+              break;
+            }
             payload['encounterDatetime'] = dateValue.format();
             break;
           case 'encounterProvider':

diff --git a/projects/ngx-formentry/src/form-entry/value-adapters/obs-adapter-helper.ts b/projects/ngx-formentry/src/form-entry/value-adapters/obs-adapter-helper.ts
@@ -134,7 +134,7 @@ export class ObsAdapterHelper {
 
       const obsUuids = [];
       for (const m of obs) {
-        obsUuids.push(m.value.uuid);
+        m.value?.uuid && obsUuids.push(m.value?.uuid);
       }
 
       this.setNodeFormControlValue(node, obsUuids);